python /home/admin/mtr/script_for_cron.py -j coverage -m 9 -a '' -s coverage -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded root_folder /data_2/data_log/job/2025/March/08032025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_2/data_log/job/2025/March/08032025/coverage/htmlcov new path : /data_2/data_log/job/2025/March/08032025/coverage/ command : coverage3 run /home/admin/workarea/git/Velours/python/tests/python_tests.py --short_python3 `cat ~/.fotonower_pass/bdd.py.pass` cat: /home/admin/.fotonower_pass/bdd.py.pass: Aucun fichier ou dossier de ce type import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data python version used : 3 #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 9707 run mask_detect Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.10662055015563965 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Sat Mar 8 11:20:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 9707 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 /home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp 2025-03-08 11:20:34.986855: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-08 11:20:35.019170: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-08 11:20:35.021339: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fba44000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-08 11:20:35.021374: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-08 11:20:35.025862: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-08 11:20:35.337024: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2b540c30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-08 11:20:35.337070: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-08 11:20:35.338887: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 11:20:35.339292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:20:35.342685: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:20:35.363176: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 11:20:35.364082: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 11:20:35.398847: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 11:20:35.404569: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 11:20:35.462014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:20:35.465418: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 11:20:35.465520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:20:35.467543: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 11:20:35.467575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 11:20:35.467587: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 11:20:35.471564: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8840 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 2025-03-08 11:20:36.442388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 11:20:36.442506: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:20:36.442527: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:20:36.442543: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 11:20:36.442559: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 11:20:36.442574: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 11:20:36.442590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 11:20:36.442605: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:20:36.444003: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 11:20:36.445506: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 11:20:36.445558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:20:36.445575: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:20:36.445590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 11:20:36.445604: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 11:20:36.445618: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 11:20:36.445632: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 11:20:36.445647: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:20:36.446928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 11:20:36.446971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 11:20:36.446982: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 11:20:36.446990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 11:20:36.448440: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8840 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-03-08 11:20:47.990027: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:20:48.231901: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:20:50.565275: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.566642: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.567757: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.568347: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.568945: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.569724: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.570428: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2282521344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.571034: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.571987: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.572017: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.581205: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.581285: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.581889: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.581909: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.588496: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.588543: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.589147: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.589167: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.621522: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.621596: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.622211: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.622232: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.628356: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.628401: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.629002: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.629022: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 11:20:50.668206: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.668868: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.670637: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.671296: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.708581: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.730414: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.733549: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.734570: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.741967: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.742573: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.746883: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.747500: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.758305: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.758947: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.760615: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.761228: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.767550: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.768161: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.769904: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.770613: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.777157: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.777760: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.779591: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.780173: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.812230: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.812992: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.813753: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.814471: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.818575: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.819350: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.837791: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.838540: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.839152: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.839735: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.855146: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.855765: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.856340: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.857158: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.862069: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.862669: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.869931: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.870574: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.885097: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.885716: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.890214: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.890831: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.891892: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.892487: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.917150: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.917944: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.918546: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.919134: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.919714: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.920290: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.920858: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.921426: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.933927: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.934539: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.941818: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.942426: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.992843: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.992934: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-03-08 11:20:50.993985: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:50.995094: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.002694: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.003496: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.004234: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.004862: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.013065: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.013673: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.054603: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.055384: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.056024: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.056603: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.060959: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.061547: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.062278: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.062880: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.065289: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.075440: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.076044: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.086591: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.087397: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.088026: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.088728: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.089332: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 11:20:51.089912: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (480, 640, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 4155330 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1233 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 3619 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl454 Catched exception ! Connect or reconnect ! thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0010495185852050781 nb_pixel_total : 15551 time to create 1 rle with old method : 0.0420069694519043 length of segment : 256 time for calcul the mask position with numpy : 0.0034432411193847656 nb_pixel_total : 145329 time to create 1 rle with old method : 0.3498702049255371 length of segment : 371 time for calcul the mask position with numpy : 0.0003559589385986328 nb_pixel_total : 14255 time to create 1 rle with old method : 0.03336000442504883 length of segment : 151 time for calcul the mask position with numpy : 0.00019860267639160156 nb_pixel_total : 5613 time to create 1 rle with old method : 0.016559362411499023 length of segment : 48 time for calcul the mask position with numpy : 0.0001456737518310547 nb_pixel_total : 1824 time to create 1 rle with old method : 0.0060083866119384766 length of segment : 39 time spent for convertir_results : 2.2570464611053467 time spend for datou_step_exec : 26.39288353919983 time spend to save output : 6.175041198730469e-05 total time spend for step 1 : 26.392945289611816 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 3314 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.01460576057434082 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[(957285035, 492601069, 445, 0, 186, 22, 282, 0.9954921, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (127, 30, 27), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923785, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 310), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), (89, 111, 449), (89, 112, 450), (89, 113, 451), (89, 114, 453), (89, 115, 454), (89, 116, 455), (88, 117, 456), (88, 118, 457), (87, 119, 459), (87, 120, 459), (86, 121, 461), (85, 122, 462), (85, 123, 463), (84, 124, 464), (84, 125, 465), (83, 126, 466), (82, 127, 468), (82, 128, 468), (81, 129, 470), (80, 130, 471), (78, 131, 473), (76, 132, 476), (75, 133, 477), (73, 134, 480), (71, 135, 482), (70, 136, 484), (68, 137, 486), (67, 138, 488), (65, 139, 490), (64, 140, 492), (63, 141, 493), (61, 142, 496), (60, 143, 497), (59, 144, 499), (58, 145, 501), (58, 146, 501), (57, 147, 503), (57, 148, 504), (57, 149, 505), (56, 150, 507), (56, 151, 507), (55, 152, 509), (55, 153, 510), (54, 154, 511), (54, 155, 512), (54, 156, 513), (53, 157, 514), (53, 158, 514), (52, 159, 515), (52, 160, 516), (52, 161, 516), (51, 162, 517), (51, 163, 517), (50, 164, 518), (50, 165, 518), (49, 166, 519), (49, 167, 520), (48, 168, 521), (48, 169, 521), (47, 170, 522), (47, 171, 522), (46, 172, 523), (46, 173, 523), (46, 174, 523), (45, 175, 524), (45, 176, 523), (44, 177, 524), (44, 178, 524), (44, 179, 524), (43, 180, 525), (43, 181, 525), (42, 182, 525), (42, 183, 525), (42, 184, 525), (41, 185, 526), (41, 186, 526), (40, 187, 526), (39, 188, 526), (39, 189, 525), (38, 190, 526), (38, 191, 525), (37, 192, 525), (37, 193, 523), (36, 194, 523), (36, 195, 522), (36, 196, 522), (35, 197, 522), (35, 198, 521), (34, 199, 521), (34, 200, 521), (34, 201, 520), (34, 202, 520), (34, 203, 520), (34, 204, 519), (34, 205, 519), (33, 206, 520), (33, 207, 519), (33, 208, 519), (33, 209, 519), (33, 210, 518), (33, 211, 518), (33, 212, 518), (33, 213, 517), (32, 214, 518), (32, 215, 517), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 512), (32, 222, 511), (32, 223, 510), (32, 224, 508), (32, 225, 507), (32, 226, 505), (32, 227, 504), (32, 228, 503), (32, 229, 502), (32, 230, 502), (32, 231, 501), (32, 232, 500), (32, 233, 499), (32, 234, 498), (32, 235, 497), (31, 236, 496), (31, 237, 495), (31, 238, 494), (31, 239, 493), (31, 240, 491), (31, 241, 490), (31, 242, 488), (31, 243, 487), (31, 244, 486), (31, 245, 485), (31, 246, 483), (31, 247, 482), (31, 248, 480), (31, 249, 479), (31, 250, 477), (31, 251, 475), (31, 252, 473), (31, 253, 472), (31, 254, 470), (31, 255, 468), (31, 256, 467), (31, 257, 465), (31, 258, 464), (31, 259, 463), (31, 260, 462), (31, 261, 461), (31, 262, 459), (31, 263, 458), (31, 264, 456), (31, 265, 455), (31, 266, 453), (31, 267, 451), (31, 268, 449), (31, 269, 448), (31, 270, 447), (31, 271, 445), (31, 272, 444), (31, 273, 443), (32, 274, 441), (32, 275, 440), (32, 276, 438), (32, 277, 437), (32, 278, 435), (32, 279, 434), (32, 280, 432), (33, 281, 429), (33, 282, 427), (33, 283, 426), (33, 284, 424), (33, 285, 423), (34, 286, 421), (34, 287, 420), (34, 288, 419), (35, 289, 416), (35, 290, 415), (35, 291, 414), (36, 292, 411), (36, 293, 410), (37, 294, 407), (37, 295, 406), (38, 296, 403), (38, 297, 401), (39, 298, 399), (39, 299, 397), (41, 300, 394), (42, 301, 392), (43, 302, 389), (44, 303, 387), (45, 304, 385), (46, 305, 382), (47, 306, 380), (47, 307, 378), (48, 308, 376), (49, 309, 373), (50, 310, 370), (51, 311, 368), (51, 312, 367), (52, 313, 365), (54, 314, 362), (55, 315, 360), (56, 316, 359), (58, 317, 356), (61, 318, 352), (64, 319, 349), (67, 320, 345), (70, 321, 341), (73, 322, 338), (75, 323, 335), (78, 324, 332), (80, 325, 329), (82, 326, 327), (84, 327, 324), (86, 328, 322), (88, 329, 320), (90, 330, 317), (93, 331, 314), (96, 332, 311), (99, 333, 307), (102, 334, 304), (105, 335, 300), (108, 336, 297), (111, 337, 294), (113, 338, 291), (115, 339, 289), (117, 340, 286), (119, 341, 283), (121, 342, 281), (123, 343, 278), (125, 344, 275), (127, 345, 272), (129, 346, 269), (132, 347, 266), (135, 348, 262), (137, 349, 259), (141, 350, 255), (143, 351, 252), (145, 352, 250), (147, 353, 247), (149, 354, 245), (151, 355, 242), (152, 356, 241), (154, 357, 239), (156, 358, 237), (159, 359, 233), (161, 360, 231), (163, 361, 229), (165, 362, 227), (167, 363, 224), (169, 364, 222), (170, 365, 221), (172, 366, 219), (173, 367, 218), (174, 368, 216), (175, 369, 215), (177, 370, 213), (178, 371, 212), (180, 372, 209), (183, 373, 206), (185, 374, 204), (188, 375, 200), (191, 376, 197), (194, 377, 193), (196, 378, 191), (199, 379, 188), (201, 380, 185), (203, 381, 183), (205, 382, 180), (207, 383, 178), (208, 384, 176), (210, 385, 174), (212, 386, 171), (213, 387, 169), (215, 388, 166), (218, 389, 162), (221, 390, 158), (225, 391, 153), (228, 392, 149), (232, 393, 144), (235, 394, 140), (238, 395, 136), (241, 396, 133), (245, 397, 128), (248, 398, 124), (252, 399, 119), (257, 400, 113), (263, 401, 105), (272, 402, 94), (283, 403, 82), (297, 404, 65), (306, 405, 53), (313, 406, 38), (321, 407, 23)], ['321,407,296,403,263,401,215,388,178,371,168,363,140,349,110,336,90,330,77,323,56,316,39,299,31,273,31,236,34,199,42,184,58,145,79,131,89,116,89,101,104,88,115,72,159,49,180,43,199,41,237,41,272,38,339,37,382,39,402,43,417,43,481,55,543,116,556,143,566,156,568,167,566,186,554,199,548,216,528,235,496,256,471,275,420,309,407,327,403,339,392,355,389,371,383,385,369,400,358,405']), (957285035, 492601069, 445, 485, 636, 23, 174, 0.97114897, [(540, 24, 21), (626, 24, 3), (531, 25, 49), (594, 25, 40), (527, 26, 107), (523, 27, 111), (520, 28, 114), (517, 29, 118), (516, 30, 119), (515, 31, 120), (513, 32, 122), (512, 33, 123), (510, 34, 125), (509, 35, 126), (507, 36, 128), (506, 37, 129), (504, 38, 131), (503, 39, 132), (501, 40, 134), (500, 41, 135), (499, 42, 136), (498, 43, 137), (497, 44, 138), (496, 45, 139), (496, 46, 139), (495, 47, 140), (495, 48, 140), (494, 49, 141), (493, 50, 142), (492, 51, 143), (491, 52, 144), (491, 53, 144), (490, 54, 145), (490, 55, 145), (490, 56, 145), (490, 57, 146), (490, 58, 146), (490, 59, 146), (491, 60, 145), (491, 61, 145), (491, 62, 145), (492, 63, 144), (493, 64, 143), (494, 65, 142), (495, 66, 141), (496, 67, 140), (497, 68, 138), (498, 69, 138), (499, 70, 137), (500, 71, 136), (501, 72, 135), (503, 73, 133), (503, 74, 133), (505, 75, 131), (506, 76, 130), (507, 77, 129), (508, 78, 128), (509, 79, 127), (510, 80, 126), (511, 81, 125), (512, 82, 124), (513, 83, 123), (514, 84, 122), (515, 85, 121), (516, 86, 120), (517, 87, 119), (518, 88, 118), (519, 89, 117), (521, 90, 115), (521, 91, 115), (522, 92, 114), (523, 93, 113), (524, 94, 112), (525, 95, 111), (526, 96, 110), (527, 97, 109), (529, 98, 107), (530, 99, 106), (532, 100, 104), (533, 101, 103), (534, 102, 102), (535, 103, 101), (536, 104, 100), (538, 105, 98), (540, 106, 96), (541, 107, 95), (543, 108, 93), (546, 109, 90), (548, 110, 88), (549, 111, 87), (551, 112, 84), (552, 113, 83), (553, 114, 82), (555, 115, 80), (556, 116, 79), (556, 117, 79), (557, 118, 78), (558, 119, 77), (559, 120, 76), (560, 121, 75), (560, 122, 75), (561, 123, 74), (561, 124, 74), (561, 125, 74), (562, 126, 73), (562, 127, 73), (563, 128, 72), (563, 129, 72), (564, 130, 70), (564, 131, 70), (565, 132, 69), (565, 133, 68), (565, 134, 68), (565, 135, 67), (566, 136, 65), (566, 137, 64), (566, 138, 64), (566, 139, 62), (566, 140, 61), (566, 141, 59), (566, 142, 57), (566, 143, 56), (566, 144, 55), (566, 145, 54), (567, 146, 53), (567, 147, 52), (567, 148, 51), (568, 149, 50), (568, 150, 49), (568, 151, 48), (568, 152, 47), (569, 153, 45), (569, 154, 44), (570, 155, 42), (570, 156, 42), (570, 157, 41), (571, 158, 39), (571, 159, 39), (572, 160, 37), (572, 161, 37), (573, 162, 35), (573, 163, 34), (573, 164, 34), (574, 165, 32), (575, 166, 30), (577, 167, 28), (578, 168, 26), (581, 169, 22), (584, 170, 19), (587, 171, 15), (591, 172, 8)], ['598,172,591,172,590,171,578,168,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,501,40,514,32,517,29,531,25,539,25,540,24,560,24,561,25,579,25,580,26,593,26,594,25,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161,604,166']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8298347, [(292, 3, 128), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 44), (432, 38, 41), (434, 39, 40), (436, 40, 39), (438, 41, 37), (441, 42, 35), (444, 43, 32), (448, 44, 29), (452, 45, 25), (454, 46, 23), (459, 47, 17), (463, 48, 12), (468, 49, 5)], ['472,49,468,49,467,48,459,47,458,46,454,46,451,44,448,44,447,43,444,43,440,41,438,41,428,36,424,36,423,35,419,35,418,34,409,34,408,33,401,33,400,32,371,32,370,31,337,31,336,30,283,31,281,29,281,6,284,4,291,4,292,3,419,3,420,4,429,4,430,5,432,5,436,7,441,11,445,12,453,16,456,19,457,19,465,27,465,29,472,37,476,44,476,46']), (957285035, 492601069, 445, 456, 547, 6, 45, 0.740514, [(482, 8, 19), (464, 9, 3), (481, 9, 44), (457, 10, 12), (479, 10, 50), (457, 11, 13), (476, 11, 56), (457, 12, 15), (475, 12, 65), (457, 13, 84), (457, 14, 85), (457, 15, 89), (457, 16, 89), (458, 17, 88), (459, 18, 87), (460, 19, 86), (461, 20, 80), (464, 21, 71), (466, 22, 63), (467, 23, 59), (468, 24, 55), (469, 25, 52), (469, 26, 51), (470, 27, 48), (471, 28, 46), (471, 29, 44), (472, 30, 42), (473, 31, 39), (473, 32, 38), (474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1741429230_4154994_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 3619 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.3979148864746094 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Sat Mar 8 11:20:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3619 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-08 11:21:04.705365: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-08 11:21:04.739166: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-08 11:21:04.741511: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fba48000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-08 11:21:04.741558: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-08 11:21:04.747776: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-08 11:21:04.934746: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2c685670 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-08 11:21:04.934822: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-08 11:21:04.935849: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 11:21:04.938463: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:21:04.972226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:21:04.998720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 11:21:05.004680: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 11:21:05.050407: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 11:21:05.056205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 11:21:05.135730: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:21:05.137324: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 11:21:05.137984: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:21:05.139319: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 11:21:05.139387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 11:21:05.139399: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 11:21:05.141253: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 87 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-03-08 11:21:05.517436: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 11:21:05.517680: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:21:05.517722: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:21:05.517764: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 11:21:05.517790: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 11:21:05.517815: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 11:21:05.517839: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 11:21:05.517868: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:21:05.518676: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 11:21:05.520013: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 11:21:05.520092: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:21:05.520125: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:21:05.520150: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 11:21:05.520169: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 11:21:05.520193: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 11:21:05.520219: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 11:21:05.520241: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:21:05.521055: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 11:21:05.521108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 11:21:05.521120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 11:21:05.521129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 11:21:05.522189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 87 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) 2025-03-08 11:21:18.792355: W tensorflow/core/common_runtime/bfc_allocator.cc:434] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.25MiB (rounded to 2359296) Current allocation summary follows. 2025-03-08 11:21:18.804808: I tensorflow/core/common_runtime/bfc_allocator.cc:934] BFCAllocator dump for GPU_0_bfc 2025-03-08 11:21:18.804856: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (256): Total Chunks: 60, Chunks in use: 60. 15.0KiB allocated for chunks. 15.0KiB in use in bin. 8.8KiB client-requested in use in bin. 2025-03-08 11:21:18.804876: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (512): Total Chunks: 40, Chunks in use: 40. 20.0KiB allocated for chunks. 20.0KiB in use in bin. 20.0KiB client-requested in use in bin. 2025-03-08 11:21:18.804894: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (1024): Total Chunks: 186, Chunks in use: 186. 186.2KiB allocated for chunks. 186.2KiB in use in bin. 186.0KiB client-requested in use in bin. 2025-03-08 11:21:18.804932: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (2048): Total Chunks: 25, Chunks in use: 25. 50.0KiB allocated for chunks. 50.0KiB in use in bin. 50.0KiB client-requested in use in bin. 2025-03-08 11:21:18.804950: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (4096): Total Chunks: 85, Chunks in use: 85. 345.5KiB allocated for chunks. 345.5KiB in use in bin. 340.0KiB client-requested in use in bin. 2025-03-08 11:21:18.804966: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (8192): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin. 2025-03-08 11:21:18.804983: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (16384): Total Chunks: 1, Chunks in use: 1. 16.0KiB allocated for chunks. 16.0KiB in use in bin. 16.0KiB client-requested in use in bin. 2025-03-08 11:21:18.805000: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (32768): Total Chunks: 1, Chunks in use: 1. 36.8KiB allocated for chunks. 36.8KiB in use in bin. 36.8KiB client-requested in use in bin. 2025-03-08 11:21:18.805018: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (65536): Total Chunks: 6, Chunks in use: 6. 384.0KiB allocated for chunks. 384.0KiB in use in bin. 384.0KiB client-requested in use in bin. 2025-03-08 11:21:18.805035: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (131072): Total Chunks: 5, Chunks in use: 4. 754.8KiB allocated for chunks. 560.0KiB in use in bin. 560.0KiB client-requested in use in bin. 2025-03-08 11:21:18.805050: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (262144): Total Chunks: 7, Chunks in use: 7. 1.91MiB allocated for chunks. 1.91MiB in use in bin. 1.75MiB client-requested in use in bin. 2025-03-08 11:21:18.805068: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (524288): Total Chunks: 8, Chunks in use: 6. 4.95MiB allocated for chunks. 3.44MiB in use in bin. 3.25MiB client-requested in use in bin. 2025-03-08 11:21:18.805085: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (1048576): Total Chunks: 34, Chunks in use: 32. 38.38MiB allocated for chunks. 36.12MiB in use in bin. 32.00MiB client-requested in use in bin. 2025-03-08 11:21:18.805102: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (2097152): Total Chunks: 18, Chunks in use: 18. 40.88MiB allocated for chunks. 40.88MiB in use in bin. 40.25MiB client-requested in use in bin. 2025-03-08 11:21:18.805117: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (4194304): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin. 2025-03-08 11:21:18.805133: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (8388608): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin. 2025-03-08 11:21:18.805147: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (16777216): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin. 2025-03-08 11:21:18.805162: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (33554432): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin. 2025-03-08 11:21:18.805178: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (67108864): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin. 2025-03-08 11:21:18.805193: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (134217728): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin. 2025-03-08 11:21:18.805207: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (268435456): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin. 2025-03-08 11:21:18.805232: I tensorflow/core/common_runtime/bfc_allocator.cc:957] Bin for 2.25MiB was 2.00MiB, Chunk State: 2025-03-08 11:21:18.805245: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 26083328 2025-03-08 11:21:18.805294: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86e000000 of size 2359296 next 370 2025-03-08 11:21:18.805311: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86e240000 of size 1048576 next 394 2025-03-08 11:21:18.805325: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free at 7fb86e340000 of size 1310720 next 388 2025-03-08 11:21:18.805337: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86e480000 of size 2359296 next 387 2025-03-08 11:21:18.805352: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86e6c0000 of size 1048576 next 412 2025-03-08 11:21:18.805366: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86e7c0000 of size 1310720 next 406 2025-03-08 11:21:18.805380: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86e900000 of size 2359296 next 405 2025-03-08 11:21:18.805394: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86eb40000 of size 1048576 next 430 2025-03-08 11:21:18.805408: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86ec40000 of size 1310720 next 424 2025-03-08 11:21:18.805422: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86ed80000 of size 2359296 next 423 2025-03-08 11:21:18.805436: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86efc0000 of size 1048576 next 447 2025-03-08 11:21:18.805448: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86f0c0000 of size 1310720 next 441 2025-03-08 11:21:18.805464: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86f200000 of size 2359296 next 440 2025-03-08 11:21:18.805476: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86f440000 of size 1048576 next 464 2025-03-08 11:21:18.805491: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86f540000 of size 1310720 next 459 2025-03-08 11:21:18.805506: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb86f680000 of size 2490368 next 18446744073709551615 2025-03-08 11:21:18.805519: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 33554432 2025-03-08 11:21:18.805532: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870000000 of size 2359296 next 245 2025-03-08 11:21:18.805548: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870240000 of size 1048576 next 269 2025-03-08 11:21:18.805560: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870340000 of size 1310720 next 263 2025-03-08 11:21:18.805575: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870480000 of size 2359296 next 262 2025-03-08 11:21:18.805589: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8706c0000 of size 1048576 next 287 2025-03-08 11:21:18.805603: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8707c0000 of size 1310720 next 281 2025-03-08 11:21:18.805615: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870900000 of size 2359296 next 280 2025-03-08 11:21:18.805629: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870b40000 of size 1048576 next 304 2025-03-08 11:21:18.805641: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870c40000 of size 1310720 next 298 2025-03-08 11:21:18.805655: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870d80000 of size 2359296 next 297 2025-03-08 11:21:18.805668: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb870fc0000 of size 1048576 next 322 2025-03-08 11:21:18.805683: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8710c0000 of size 1310720 next 316 2025-03-08 11:21:18.805695: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb871200000 of size 2359296 next 315 2025-03-08 11:21:18.805728: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb871440000 of size 1048576 next 340 2025-03-08 11:21:18.805741: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb871540000 of size 1310720 next 334 2025-03-08 11:21:18.805755: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb871680000 of size 2359296 next 333 2025-03-08 11:21:18.805767: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8718c0000 of size 1048576 next 358 2025-03-08 11:21:18.805779: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8719c0000 of size 1310720 next 352 2025-03-08 11:21:18.805791: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb871b00000 of size 2359296 next 351 2025-03-08 11:21:18.805803: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb871d40000 of size 1048576 next 376 2025-03-08 11:21:18.805816: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb871e40000 of size 1835008 next 18446744073709551615 2025-03-08 11:21:18.805828: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 16777216 2025-03-08 11:21:18.805840: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb872000000 of size 2359296 next 181 2025-03-08 11:21:18.805852: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb872240000 of size 2097152 next 196 2025-03-08 11:21:18.805864: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb872440000 of size 1048576 next 216 2025-03-08 11:21:18.805876: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb872540000 of size 1310720 next 209 2025-03-08 11:21:18.805889: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb872680000 of size 2359296 next 208 2025-03-08 11:21:18.805901: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8728c0000 of size 1048576 next 234 2025-03-08 11:21:18.805913: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8729c0000 of size 1310720 next 228 2025-03-08 11:21:18.805925: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb872b00000 of size 2359296 next 227 2025-03-08 11:21:18.805937: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb872d40000 of size 2883584 next 18446744073709551615 2025-03-08 11:21:18.805949: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 2097152 2025-03-08 11:21:18.805965: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874600000 of size 147456 next 55 2025-03-08 11:21:18.805978: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874624000 of size 65536 next 78 2025-03-08 11:21:18.805991: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874634000 of size 4096 next 191 2025-03-08 11:21:18.806003: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874635000 of size 4096 next 192 2025-03-08 11:21:18.806015: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874636000 of size 4096 next 193 2025-03-08 11:21:18.806027: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874637000 of size 256 next 194 2025-03-08 11:21:18.806039: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874637100 of size 256 next 195 2025-03-08 11:21:18.806051: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874637200 of size 4096 next 197 2025-03-08 11:21:18.806063: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874638200 of size 4096 next 198 2025-03-08 11:21:18.806075: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874639200 of size 4096 next 199 2025-03-08 11:21:18.806087: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463a200 of size 4096 next 200 2025-03-08 11:21:18.806099: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463b200 of size 4096 next 201 2025-03-08 11:21:18.806111: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463c200 of size 1024 next 202 2025-03-08 11:21:18.806133: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463c600 of size 1024 next 204 2025-03-08 11:21:18.806146: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463ca00 of size 1024 next 205 2025-03-08 11:21:18.806158: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463ce00 of size 1024 next 206 2025-03-08 11:21:18.806170: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463d200 of size 1024 next 207 2025-03-08 11:21:18.806182: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463d600 of size 1024 next 210 2025-03-08 11:21:18.806194: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463da00 of size 1024 next 211 2025-03-08 11:21:18.806206: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463de00 of size 1024 next 212 2025-03-08 11:21:18.806218: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463e200 of size 1024 next 213 2025-03-08 11:21:18.806230: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463e600 of size 1024 next 214 2025-03-08 11:21:18.806242: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463ea00 of size 4096 next 215 2025-03-08 11:21:18.806254: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87463fa00 of size 4096 next 217 2025-03-08 11:21:18.806266: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874640a00 of size 4096 next 218 2025-03-08 11:21:18.806278: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874641a00 of size 4096 next 219 2025-03-08 11:21:18.806290: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874642a00 of size 4096 next 220 2025-03-08 11:21:18.806302: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874643a00 of size 1024 next 221 2025-03-08 11:21:18.806314: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874643e00 of size 1024 next 223 2025-03-08 11:21:18.806326: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874644200 of size 1024 next 224 2025-03-08 11:21:18.806338: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874644600 of size 1024 next 225 2025-03-08 11:21:18.806351: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874644a00 of size 1024 next 226 2025-03-08 11:21:18.806363: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874644e00 of size 1024 next 229 2025-03-08 11:21:18.806378: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874645200 of size 1024 next 230 2025-03-08 11:21:18.806393: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874645600 of size 1024 next 231 2025-03-08 11:21:18.806405: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874645a00 of size 1024 next 232 2025-03-08 11:21:18.806420: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874645e00 of size 1024 next 233 2025-03-08 11:21:18.806434: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874646200 of size 7680 next 72 2025-03-08 11:21:18.806448: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874648000 of size 147456 next 71 2025-03-08 11:21:18.806462: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87466c000 of size 131072 next 87 2025-03-08 11:21:18.806476: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87468c000 of size 4096 next 289 2025-03-08 11:21:18.806490: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87468d000 of size 4096 next 290 2025-03-08 11:21:18.806503: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87468e000 of size 4096 next 291 2025-03-08 11:21:18.806517: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87468f000 of size 1024 next 292 2025-03-08 11:21:18.806529: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87468f400 of size 1024 next 293 2025-03-08 11:21:18.806554: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87468f800 of size 1024 next 294 2025-03-08 11:21:18.806568: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87468fc00 of size 1024 next 295 2025-03-08 11:21:18.806580: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874690000 of size 1024 next 296 2025-03-08 11:21:18.806592: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874690400 of size 1024 next 299 2025-03-08 11:21:18.806606: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874690800 of size 1024 next 300 2025-03-08 11:21:18.806622: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874690c00 of size 1024 next 301 2025-03-08 11:21:18.806640: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874691000 of size 1024 next 302 2025-03-08 11:21:18.806658: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874691400 of size 1024 next 303 2025-03-08 11:21:18.806676: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874691800 of size 4096 next 305 2025-03-08 11:21:18.806692: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874692800 of size 4096 next 306 2025-03-08 11:21:18.806706: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874693800 of size 4096 next 307 2025-03-08 11:21:18.806720: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874694800 of size 4096 next 308 2025-03-08 11:21:18.806732: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874695800 of size 4096 next 309 2025-03-08 11:21:18.806745: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874696800 of size 1024 next 310 2025-03-08 11:21:18.806760: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874696c00 of size 1024 next 311 2025-03-08 11:21:18.806773: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874697000 of size 1024 next 312 2025-03-08 11:21:18.806786: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874697400 of size 1024 next 313 2025-03-08 11:21:18.806801: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874697800 of size 1024 next 314 2025-03-08 11:21:18.806813: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874697c00 of size 1024 next 317 2025-03-08 11:21:18.806828: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874698000 of size 1024 next 318 2025-03-08 11:21:18.806842: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874698400 of size 1024 next 319 2025-03-08 11:21:18.806854: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874698800 of size 1024 next 320 2025-03-08 11:21:18.806869: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874698c00 of size 1024 next 321 2025-03-08 11:21:18.806883: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874699000 of size 4096 next 323 2025-03-08 11:21:18.806909: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469a000 of size 4096 next 324 2025-03-08 11:21:18.806925: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469b000 of size 4096 next 325 2025-03-08 11:21:18.806939: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469c000 of size 4096 next 326 2025-03-08 11:21:18.806953: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469d000 of size 4096 next 327 2025-03-08 11:21:18.806966: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469e000 of size 1024 next 328 2025-03-08 11:21:18.806981: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469e400 of size 1024 next 329 2025-03-08 11:21:18.806994: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469e800 of size 1024 next 330 2025-03-08 11:21:18.807008: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469ec00 of size 1024 next 331 2025-03-08 11:21:18.807030: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469f000 of size 1024 next 332 2025-03-08 11:21:18.807044: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469f400 of size 1024 next 335 2025-03-08 11:21:18.807058: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469f800 of size 1024 next 336 2025-03-08 11:21:18.807073: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb87469fc00 of size 1024 next 337 2025-03-08 11:21:18.807086: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a0000 of size 1024 next 338 2025-03-08 11:21:18.807099: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a0400 of size 1024 next 339 2025-03-08 11:21:18.807112: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a0800 of size 4096 next 341 2025-03-08 11:21:18.807124: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a1800 of size 4096 next 342 2025-03-08 11:21:18.807137: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a2800 of size 4096 next 343 2025-03-08 11:21:18.807151: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a3800 of size 4096 next 344 2025-03-08 11:21:18.807165: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a4800 of size 4096 next 345 2025-03-08 11:21:18.807177: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a5800 of size 1024 next 346 2025-03-08 11:21:18.807192: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a5c00 of size 1024 next 347 2025-03-08 11:21:18.807205: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a6000 of size 1024 next 348 2025-03-08 11:21:18.807219: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a6400 of size 1024 next 349 2025-03-08 11:21:18.807233: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a6800 of size 1024 next 350 2025-03-08 11:21:18.807245: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a6c00 of size 1024 next 353 2025-03-08 11:21:18.807260: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a7000 of size 1024 next 354 2025-03-08 11:21:18.807273: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a7400 of size 1024 next 355 2025-03-08 11:21:18.807287: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a7800 of size 1024 next 356 2025-03-08 11:21:18.807301: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a7c00 of size 1024 next 357 2025-03-08 11:21:18.807313: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a8000 of size 4096 next 359 2025-03-08 11:21:18.807328: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746a9000 of size 4096 next 360 2025-03-08 11:21:18.807342: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746aa000 of size 4096 next 361 2025-03-08 11:21:18.807354: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ab000 of size 4096 next 362 2025-03-08 11:21:18.807369: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ac000 of size 4096 next 363 2025-03-08 11:21:18.807381: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ad000 of size 1024 next 364 2025-03-08 11:21:18.807396: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ad400 of size 1024 next 365 2025-03-08 11:21:18.807410: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ad800 of size 1024 next 366 2025-03-08 11:21:18.807422: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746adc00 of size 1024 next 367 2025-03-08 11:21:18.807437: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ae000 of size 1024 next 368 2025-03-08 11:21:18.807451: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ae400 of size 1024 next 371 2025-03-08 11:21:18.807464: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ae800 of size 1024 next 372 2025-03-08 11:21:18.807488: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746aec00 of size 1024 next 373 2025-03-08 11:21:18.807502: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746af000 of size 1024 next 374 2025-03-08 11:21:18.807516: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746af400 of size 1024 next 375 2025-03-08 11:21:18.807528: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746af800 of size 4096 next 377 2025-03-08 11:21:18.807543: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b0800 of size 4096 next 378 2025-03-08 11:21:18.807557: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b1800 of size 4096 next 379 2025-03-08 11:21:18.807569: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b2800 of size 4096 next 380 2025-03-08 11:21:18.807583: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b3800 of size 4096 next 381 2025-03-08 11:21:18.807598: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b4800 of size 1024 next 382 2025-03-08 11:21:18.807610: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b4c00 of size 1024 next 383 2025-03-08 11:21:18.807625: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b5000 of size 1024 next 384 2025-03-08 11:21:18.807637: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b5400 of size 1024 next 385 2025-03-08 11:21:18.807651: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b5800 of size 1024 next 386 2025-03-08 11:21:18.807663: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b5c00 of size 1024 next 389 2025-03-08 11:21:18.807676: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b6000 of size 1024 next 390 2025-03-08 11:21:18.807690: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b6400 of size 1024 next 391 2025-03-08 11:21:18.807705: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b6800 of size 1024 next 392 2025-03-08 11:21:18.807717: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b6c00 of size 1024 next 393 2025-03-08 11:21:18.807730: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b7000 of size 4096 next 395 2025-03-08 11:21:18.807744: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b8000 of size 4096 next 396 2025-03-08 11:21:18.807759: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746b9000 of size 4096 next 397 2025-03-08 11:21:18.807771: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ba000 of size 4096 next 398 2025-03-08 11:21:18.807783: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bb000 of size 4096 next 399 2025-03-08 11:21:18.807795: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bc000 of size 1024 next 400 2025-03-08 11:21:18.807807: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bc400 of size 1024 next 401 2025-03-08 11:21:18.807820: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bc800 of size 1024 next 402 2025-03-08 11:21:18.807832: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bcc00 of size 1024 next 403 2025-03-08 11:21:18.807844: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bd000 of size 1024 next 404 2025-03-08 11:21:18.807856: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bd400 of size 1024 next 407 2025-03-08 11:21:18.807868: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bd800 of size 1024 next 408 2025-03-08 11:21:18.807879: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bdc00 of size 1024 next 409 2025-03-08 11:21:18.807891: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746be000 of size 1024 next 410 2025-03-08 11:21:18.807911: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746be400 of size 1024 next 411 2025-03-08 11:21:18.807924: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746be800 of size 4096 next 413 2025-03-08 11:21:18.807936: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746bf800 of size 4096 next 414 2025-03-08 11:21:18.807948: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c0800 of size 4096 next 415 2025-03-08 11:21:18.807963: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c1800 of size 4096 next 416 2025-03-08 11:21:18.807975: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c2800 of size 4096 next 417 2025-03-08 11:21:18.807987: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c3800 of size 1024 next 418 2025-03-08 11:21:18.807999: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c3c00 of size 1024 next 419 2025-03-08 11:21:18.808011: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c4000 of size 1024 next 420 2025-03-08 11:21:18.808023: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c4400 of size 1024 next 421 2025-03-08 11:21:18.808035: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c4800 of size 1024 next 422 2025-03-08 11:21:18.808046: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c4c00 of size 1024 next 425 2025-03-08 11:21:18.808058: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c5000 of size 1024 next 426 2025-03-08 11:21:18.808071: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c5400 of size 1024 next 427 2025-03-08 11:21:18.808083: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c5800 of size 1024 next 428 2025-03-08 11:21:18.808094: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c5c00 of size 1024 next 429 2025-03-08 11:21:18.808107: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c6000 of size 4096 next 431 2025-03-08 11:21:18.808120: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c7000 of size 4096 next 432 2025-03-08 11:21:18.808134: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c8000 of size 4096 next 433 2025-03-08 11:21:18.808146: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746c9000 of size 4096 next 434 2025-03-08 11:21:18.808158: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746ca000 of size 4096 next 435 2025-03-08 11:21:18.808170: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746cb000 of size 1024 next 436 2025-03-08 11:21:18.808183: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746cb400 of size 1024 next 437 2025-03-08 11:21:18.808195: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746cb800 of size 1024 next 438 2025-03-08 11:21:18.808207: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746cbc00 of size 1024 next 103 2025-03-08 11:21:18.808220: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8746cc000 of size 262144 next 102 2025-03-08 11:21:18.808232: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free at 7fb87470c000 of size 999424 next 18446744073709551615 2025-03-08 11:21:18.808244: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 4194304 2025-03-08 11:21:18.808257: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874800000 of size 589824 next 96 2025-03-08 11:21:18.808269: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874890000 of size 262144 next 117 2025-03-08 11:21:18.808281: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d0000 of size 1024 next 439 2025-03-08 11:21:18.808293: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d0400 of size 1024 next 442 2025-03-08 11:21:18.808313: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d0800 of size 1024 next 443 2025-03-08 11:21:18.808325: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d0c00 of size 1024 next 444 2025-03-08 11:21:18.808337: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d1000 of size 1024 next 445 2025-03-08 11:21:18.808349: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d1400 of size 1024 next 446 2025-03-08 11:21:18.808361: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d1800 of size 4096 next 448 2025-03-08 11:21:18.808373: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d2800 of size 4096 next 449 2025-03-08 11:21:18.808385: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d3800 of size 4096 next 450 2025-03-08 11:21:18.808396: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d4800 of size 4096 next 451 2025-03-08 11:21:18.808408: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d5800 of size 4096 next 452 2025-03-08 11:21:18.808420: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d6800 of size 1024 next 453 2025-03-08 11:21:18.808434: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d6c00 of size 1024 next 454 2025-03-08 11:21:18.808446: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d7000 of size 1024 next 455 2025-03-08 11:21:18.808458: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d7400 of size 1024 next 456 2025-03-08 11:21:18.808470: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d7800 of size 1024 next 457 2025-03-08 11:21:18.808481: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d7c00 of size 1024 next 458 2025-03-08 11:21:18.808493: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d8000 of size 1024 next 460 2025-03-08 11:21:18.808505: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d8400 of size 1024 next 461 2025-03-08 11:21:18.808517: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d8800 of size 1024 next 462 2025-03-08 11:21:18.808529: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d8c00 of size 1024 next 463 2025-03-08 11:21:18.808541: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748d9000 of size 4096 next 465 2025-03-08 11:21:18.808553: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748da000 of size 4096 next 466 2025-03-08 11:21:18.808565: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748db000 of size 4096 next 467 2025-03-08 11:21:18.808577: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748dc000 of size 4096 next 468 2025-03-08 11:21:18.808589: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748dd000 of size 4096 next 469 2025-03-08 11:21:18.808604: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748de000 of size 1024 next 470 2025-03-08 11:21:18.808616: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748de400 of size 1024 next 471 2025-03-08 11:21:18.808631: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748de800 of size 1024 next 472 2025-03-08 11:21:18.808645: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748dec00 of size 1024 next 473 2025-03-08 11:21:18.808657: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748df000 of size 1024 next 474 2025-03-08 11:21:18.808694: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8748df400 of size 256 next 475 2025-03-08 11:21:18.808707: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free at 7fb8748df500 of size 199424 next 112 2025-03-08 11:21:18.808719: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874910000 of size 524288 next 111 2025-03-08 11:21:18.808762: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874990000 of size 262144 next 135 2025-03-08 11:21:18.808777: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8749d0000 of size 327680 next 124 2025-03-08 11:21:18.808791: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874a20000 of size 589824 next 123 2025-03-08 11:21:18.808805: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874ab0000 of size 262144 next 152 2025-03-08 11:21:18.808817: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874af0000 of size 327680 next 142 2025-03-08 11:21:18.808833: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874b40000 of size 786432 next 18446744073709551615 2025-03-08 11:21:18.808845: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 8388608 2025-03-08 11:21:18.808857: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free at 7fb874c00000 of size 589824 next 160 2025-03-08 11:21:18.808873: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874c90000 of size 589824 next 158 2025-03-08 11:21:18.808885: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874d20000 of size 524288 next 171 2025-03-08 11:21:18.808897: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free at 7fb874da0000 of size 1048576 next 222 2025-03-08 11:21:18.808909: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874ea0000 of size 1048576 next 190 2025-03-08 11:21:18.808921: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb874fa0000 of size 1048576 next 189 2025-03-08 11:21:18.808933: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8750a0000 of size 1048576 next 203 2025-03-08 11:21:18.808945: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8751a0000 of size 1048576 next 251 2025-03-08 11:21:18.808957: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb8752a0000 of size 1441792 next 18446744073709551615 2025-03-08 11:21:18.808969: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 1048576 2025-03-08 11:21:18.808981: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00000 of size 1280 next 1 2025-03-08 11:21:18.808993: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00500 of size 256 next 5 2025-03-08 11:21:18.809005: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00600 of size 256 next 8 2025-03-08 11:21:18.809017: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00700 of size 256 next 9 2025-03-08 11:21:18.809029: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00800 of size 256 next 10 2025-03-08 11:21:18.809041: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00900 of size 256 next 11 2025-03-08 11:21:18.809055: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00a00 of size 256 next 12 2025-03-08 11:21:18.809073: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00b00 of size 256 next 13 2025-03-08 11:21:18.809093: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00c00 of size 256 next 17 2025-03-08 11:21:18.809112: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00d00 of size 256 next 19 2025-03-08 11:21:18.809130: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00e00 of size 256 next 20 2025-03-08 11:21:18.809149: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae00f00 of size 256 next 21 2025-03-08 11:21:18.809168: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01000 of size 256 next 22 2025-03-08 11:21:18.809188: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01100 of size 256 next 24 2025-03-08 11:21:18.809201: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01200 of size 256 next 25 2025-03-08 11:21:18.809213: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01300 of size 256 next 23 2025-03-08 11:21:18.809234: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01400 of size 256 next 28 2025-03-08 11:21:18.809250: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01500 of size 256 next 29 2025-03-08 11:21:18.809262: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01600 of size 256 next 30 2025-03-08 11:21:18.809274: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01700 of size 256 next 31 2025-03-08 11:21:18.809285: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01800 of size 256 next 33 2025-03-08 11:21:18.809297: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01900 of size 256 next 34 2025-03-08 11:21:18.809311: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01a00 of size 1024 next 32 2025-03-08 11:21:18.809324: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae01e00 of size 1024 next 37 2025-03-08 11:21:18.809336: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae02200 of size 1024 next 38 2025-03-08 11:21:18.809348: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae02600 of size 1024 next 39 2025-03-08 11:21:18.809360: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae02a00 of size 1024 next 40 2025-03-08 11:21:18.809372: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae02e00 of size 1024 next 41 2025-03-08 11:21:18.809384: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae03200 of size 1024 next 43 2025-03-08 11:21:18.809396: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae03600 of size 1024 next 44 2025-03-08 11:21:18.809412: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae03a00 of size 1024 next 45 2025-03-08 11:21:18.809432: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae03e00 of size 1024 next 46 2025-03-08 11:21:18.809454: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04200 of size 256 next 48 2025-03-08 11:21:18.809474: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04300 of size 256 next 49 2025-03-08 11:21:18.809495: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04400 of size 256 next 50 2025-03-08 11:21:18.809516: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04500 of size 256 next 51 2025-03-08 11:21:18.809528: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04600 of size 256 next 52 2025-03-08 11:21:18.809540: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04700 of size 256 next 53 2025-03-08 11:21:18.809552: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04800 of size 256 next 56 2025-03-08 11:21:18.809564: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04900 of size 256 next 57 2025-03-08 11:21:18.809579: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04a00 of size 256 next 58 2025-03-08 11:21:18.809591: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04b00 of size 256 next 14 2025-03-08 11:21:18.809603: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04c00 of size 256 next 15 2025-03-08 11:21:18.809615: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04d00 of size 256 next 16 2025-03-08 11:21:18.809626: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae04e00 of size 1024 next 60 2025-03-08 11:21:18.809638: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae05200 of size 1024 next 61 2025-03-08 11:21:18.809651: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae05600 of size 1024 next 62 2025-03-08 11:21:18.809663: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae05a00 of size 1024 next 63 2025-03-08 11:21:18.809684: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae05e00 of size 1024 next 64 2025-03-08 11:21:18.809696: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06200 of size 256 next 66 2025-03-08 11:21:18.809708: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06300 of size 256 next 67 2025-03-08 11:21:18.809720: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06400 of size 256 next 68 2025-03-08 11:21:18.809732: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06500 of size 256 next 69 2025-03-08 11:21:18.809744: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06600 of size 256 next 70 2025-03-08 11:21:18.809756: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06700 of size 256 next 73 2025-03-08 11:21:18.809768: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06800 of size 256 next 74 2025-03-08 11:21:18.809780: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06900 of size 256 next 75 2025-03-08 11:21:18.809792: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06a00 of size 256 next 76 2025-03-08 11:21:18.809808: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06b00 of size 256 next 77 2025-03-08 11:21:18.809820: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae06c00 of size 1024 next 79 2025-03-08 11:21:18.809833: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae07000 of size 1024 next 80 2025-03-08 11:21:18.809847: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae07400 of size 1024 next 81 2025-03-08 11:21:18.809859: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae07800 of size 1024 next 82 2025-03-08 11:21:18.809871: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae07c00 of size 1024 next 83 2025-03-08 11:21:18.809883: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08000 of size 256 next 85 2025-03-08 11:21:18.809897: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08100 of size 256 next 86 2025-03-08 11:21:18.809910: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08200 of size 512 next 84 2025-03-08 11:21:18.809922: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08400 of size 512 next 88 2025-03-08 11:21:18.809934: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08600 of size 512 next 89 2025-03-08 11:21:18.809946: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08800 of size 512 next 90 2025-03-08 11:21:18.809958: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08a00 of size 512 next 91 2025-03-08 11:21:18.809972: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08c00 of size 256 next 93 2025-03-08 11:21:18.809984: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08d00 of size 256 next 94 2025-03-08 11:21:18.809996: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae08e00 of size 512 next 92 2025-03-08 11:21:18.810008: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae09000 of size 512 next 97 2025-03-08 11:21:18.810020: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae09200 of size 512 next 98 2025-03-08 11:21:18.810034: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae09400 of size 512 next 99 2025-03-08 11:21:18.810052: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae09600 of size 512 next 2 2025-03-08 11:21:18.810069: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae09800 of size 256 next 3 2025-03-08 11:21:18.810081: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae09900 of size 256 next 4 2025-03-08 11:21:18.810094: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae09a00 of size 16384 next 18 2025-03-08 11:21:18.810116: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae0da00 of size 256 next 100 2025-03-08 11:21:18.810128: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae0db00 of size 256 next 101 2025-03-08 11:21:18.810144: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae0dc00 of size 2048 next 104 2025-03-08 11:21:18.810156: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae0e400 of size 2048 next 105 2025-03-08 11:21:18.810168: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae0ec00 of size 2048 next 106 2025-03-08 11:21:18.810180: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae0f400 of size 2048 next 107 2025-03-08 11:21:18.810195: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae0fc00 of size 2048 next 108 2025-03-08 11:21:18.810207: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae10400 of size 256 next 109 2025-03-08 11:21:18.810219: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae10500 of size 256 next 110 2025-03-08 11:21:18.810234: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae10600 of size 2048 next 113 2025-03-08 11:21:18.810246: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae10e00 of size 2048 next 114 2025-03-08 11:21:18.810258: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae11600 of size 2048 next 115 2025-03-08 11:21:18.810269: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae11e00 of size 2048 next 116 2025-03-08 11:21:18.810281: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae12600 of size 2048 next 6 2025-03-08 11:21:18.810293: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae12e00 of size 37632 next 7 2025-03-08 11:21:18.810308: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1c100 of size 512 next 118 2025-03-08 11:21:18.810320: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1c300 of size 512 next 119 2025-03-08 11:21:18.810332: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1c500 of size 512 next 120 2025-03-08 11:21:18.810347: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1c700 of size 512 next 121 2025-03-08 11:21:18.810366: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1c900 of size 512 next 122 2025-03-08 11:21:18.810385: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1cb00 of size 512 next 125 2025-03-08 11:21:18.810407: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1cd00 of size 512 next 126 2025-03-08 11:21:18.810427: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1cf00 of size 512 next 127 2025-03-08 11:21:18.810447: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1d100 of size 512 next 128 2025-03-08 11:21:18.810468: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1d300 of size 512 next 129 2025-03-08 11:21:18.810488: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1d500 of size 2048 next 130 2025-03-08 11:21:18.810510: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1dd00 of size 2048 next 131 2025-03-08 11:21:18.810530: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1e500 of size 2048 next 132 2025-03-08 11:21:18.810549: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1ed00 of size 2048 next 133 2025-03-08 11:21:18.810565: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1f500 of size 2048 next 134 2025-03-08 11:21:18.810586: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1fd00 of size 512 next 136 2025-03-08 11:21:18.810607: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae1ff00 of size 512 next 137 2025-03-08 11:21:18.810646: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae20100 of size 512 next 138 2025-03-08 11:21:18.810667: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae20300 of size 512 next 139 2025-03-08 11:21:18.810688: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae20500 of size 512 next 140 2025-03-08 11:21:18.810708: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae20700 of size 512 next 141 2025-03-08 11:21:18.810726: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae20900 of size 512 next 143 2025-03-08 11:21:18.810747: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae20b00 of size 512 next 144 2025-03-08 11:21:18.810767: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae20d00 of size 512 next 145 2025-03-08 11:21:18.810787: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae20f00 of size 512 next 146 2025-03-08 11:21:18.810808: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae21100 of size 2048 next 147 2025-03-08 11:21:18.810826: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae21900 of size 2048 next 148 2025-03-08 11:21:18.810848: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae22100 of size 2048 next 149 2025-03-08 11:21:18.810868: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae22900 of size 2048 next 150 2025-03-08 11:21:18.810889: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae23100 of size 2048 next 151 2025-03-08 11:21:18.810917: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae23900 of size 512 next 153 2025-03-08 11:21:18.810935: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae23b00 of size 512 next 154 2025-03-08 11:21:18.810955: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae23d00 of size 512 next 155 2025-03-08 11:21:18.810977: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae23f00 of size 512 next 156 2025-03-08 11:21:18.810997: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae24100 of size 512 next 157 2025-03-08 11:21:18.811017: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae24300 of size 512 next 161 2025-03-08 11:21:18.811036: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae24500 of size 512 next 162 2025-03-08 11:21:18.811057: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae24700 of size 512 next 163 2025-03-08 11:21:18.811077: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae24900 of size 512 next 164 2025-03-08 11:21:18.811097: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae24b00 of size 512 next 165 2025-03-08 11:21:18.811117: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae24d00 of size 2048 next 166 2025-03-08 11:21:18.811137: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae25500 of size 2048 next 167 2025-03-08 11:21:18.811159: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae25d00 of size 2048 next 168 2025-03-08 11:21:18.811179: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae26500 of size 2048 next 169 2025-03-08 11:21:18.811195: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae26d00 of size 2048 next 170 2025-03-08 11:21:18.811207: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae27500 of size 1024 next 172 2025-03-08 11:21:18.811219: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae27900 of size 1024 next 173 2025-03-08 11:21:18.811230: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae27d00 of size 1024 next 174 2025-03-08 11:21:18.811242: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae28100 of size 1024 next 175 2025-03-08 11:21:18.811254: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae28500 of size 1024 next 176 2025-03-08 11:21:18.811275: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae28900 of size 256 next 178 2025-03-08 11:21:18.811288: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae28a00 of size 256 next 179 2025-03-08 11:21:18.811300: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae28b00 of size 1024 next 177 2025-03-08 11:21:18.811311: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae28f00 of size 1024 next 182 2025-03-08 11:21:18.811323: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae29300 of size 1024 next 183 2025-03-08 11:21:18.811335: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae29700 of size 1024 next 184 2025-03-08 11:21:18.811347: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae29b00 of size 1024 next 185 2025-03-08 11:21:18.811359: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae29f00 of size 256 next 187 2025-03-08 11:21:18.811371: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae2a000 of size 256 next 188 2025-03-08 11:21:18.811383: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae2a100 of size 4096 next 186 2025-03-08 11:21:18.811395: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae2b100 of size 4096 next 42 2025-03-08 11:21:18.811407: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae2c100 of size 65536 next 36 2025-03-08 11:21:18.811419: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae3c100 of size 65536 next 35 2025-03-08 11:21:18.811431: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae4c100 of size 4096 next 235 2025-03-08 11:21:18.811443: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae4d100 of size 4096 next 236 2025-03-08 11:21:18.811455: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae4e100 of size 4096 next 237 2025-03-08 11:21:18.811467: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae4f100 of size 4096 next 238 2025-03-08 11:21:18.811479: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae50100 of size 1024 next 239 2025-03-08 11:21:18.811491: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae50500 of size 1024 next 240 2025-03-08 11:21:18.811502: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae50900 of size 1024 next 241 2025-03-08 11:21:18.811514: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae50d00 of size 1024 next 242 2025-03-08 11:21:18.811526: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae51100 of size 1024 next 243 2025-03-08 11:21:18.811538: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae51500 of size 1024 next 246 2025-03-08 11:21:18.811550: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae51900 of size 1024 next 247 2025-03-08 11:21:18.811562: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae51d00 of size 1024 next 248 2025-03-08 11:21:18.811574: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae52100 of size 1024 next 249 2025-03-08 11:21:18.811586: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae52500 of size 1024 next 250 2025-03-08 11:21:18.811598: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae52900 of size 4096 next 252 2025-03-08 11:21:18.811609: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae53900 of size 4096 next 253 2025-03-08 11:21:18.811621: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae54900 of size 4096 next 254 2025-03-08 11:21:18.811633: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae55900 of size 4096 next 255 2025-03-08 11:21:18.811645: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae56900 of size 4096 next 256 2025-03-08 11:21:18.811665: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae57900 of size 1024 next 257 2025-03-08 11:21:18.811677: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae57d00 of size 1024 next 258 2025-03-08 11:21:18.811689: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae58100 of size 1024 next 259 2025-03-08 11:21:18.811701: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae58500 of size 1024 next 260 2025-03-08 11:21:18.811713: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae58900 of size 1024 next 261 2025-03-08 11:21:18.811725: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae58d00 of size 1024 next 264 2025-03-08 11:21:18.811737: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae59100 of size 1024 next 265 2025-03-08 11:21:18.811749: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae59500 of size 1024 next 266 2025-03-08 11:21:18.811761: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae59900 of size 1024 next 267 2025-03-08 11:21:18.811773: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae59d00 of size 1024 next 268 2025-03-08 11:21:18.811785: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5a100 of size 4096 next 270 2025-03-08 11:21:18.811796: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5b100 of size 4096 next 271 2025-03-08 11:21:18.811808: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5c100 of size 4096 next 272 2025-03-08 11:21:18.811820: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5d100 of size 4096 next 273 2025-03-08 11:21:18.811832: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5e100 of size 4096 next 274 2025-03-08 11:21:18.811844: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5f100 of size 1024 next 275 2025-03-08 11:21:18.811856: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5f500 of size 1024 next 276 2025-03-08 11:21:18.811868: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5f900 of size 1024 next 277 2025-03-08 11:21:18.811880: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae5fd00 of size 1024 next 278 2025-03-08 11:21:18.811892: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae60100 of size 1024 next 279 2025-03-08 11:21:18.811904: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae60500 of size 1024 next 282 2025-03-08 11:21:18.811916: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae60900 of size 1024 next 283 2025-03-08 11:21:18.811927: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae60d00 of size 1024 next 284 2025-03-08 11:21:18.811939: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae61100 of size 1024 next 285 2025-03-08 11:21:18.811951: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae61500 of size 1024 next 286 2025-03-08 11:21:18.811963: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae61900 of size 4096 next 288 2025-03-08 11:21:18.811976: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae62900 of size 6144 next 27 2025-03-08 11:21:18.811988: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae64100 of size 147456 next 26 2025-03-08 11:21:18.812000: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae88100 of size 65536 next 47 2025-03-08 11:21:18.812012: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aae98100 of size 65536 next 59 2025-03-08 11:21:18.812024: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aaea8100 of size 65536 next 65 2025-03-08 11:21:18.812036: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7fb9aaeb8100 of size 294656 next 18446744073709551615 2025-03-08 11:21:18.812056: I tensorflow/core/common_runtime/bfc_allocator.cc:995] Summary of in-use Chunks by size: 2025-03-08 11:21:18.812073: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 60 Chunks of size 256 totalling 15.0KiB 2025-03-08 11:21:18.812087: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 40 Chunks of size 512 totalling 20.0KiB 2025-03-08 11:21:18.812100: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 185 Chunks of size 1024 totalling 185.0KiB 2025-03-08 11:21:18.812113: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 1280 totalling 1.2KiB 2025-03-08 11:21:18.812126: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 25 Chunks of size 2048 totalling 50.0KiB 2025-03-08 11:21:18.812140: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 83 Chunks of size 4096 totalling 332.0KiB 2025-03-08 11:21:18.812153: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 6144 totalling 6.0KiB 2025-03-08 11:21:18.812166: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 7680 totalling 7.5KiB 2025-03-08 11:21:18.812180: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 16384 totalling 16.0KiB 2025-03-08 11:21:18.812193: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 37632 totalling 36.8KiB 2025-03-08 11:21:18.812206: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 6 Chunks of size 65536 totalling 384.0KiB 2025-03-08 11:21:18.812219: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 131072 totalling 128.0KiB 2025-03-08 11:21:18.812233: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 3 Chunks of size 147456 totalling 432.0KiB 2025-03-08 11:21:18.812246: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 4 Chunks of size 262144 totalling 1.00MiB 2025-03-08 11:21:18.812259: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 294656 totalling 287.8KiB 2025-03-08 11:21:18.812272: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 2 Chunks of size 327680 totalling 640.0KiB 2025-03-08 11:21:18.812285: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 2 Chunks of size 524288 totalling 1.00MiB 2025-03-08 11:21:18.812298: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 3 Chunks of size 589824 totalling 1.69MiB 2025-03-08 11:21:18.812311: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 786432 totalling 768.0KiB 2025-03-08 11:21:18.812324: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 18 Chunks of size 1048576 totalling 18.00MiB 2025-03-08 11:21:18.812337: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 12 Chunks of size 1310720 totalling 15.00MiB 2025-03-08 11:21:18.812350: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 1441792 totalling 1.38MiB 2025-03-08 11:21:18.812363: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 1835008 totalling 1.75MiB 2025-03-08 11:21:18.812376: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 2097152 totalling 2.00MiB 2025-03-08 11:21:18.812389: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 15 Chunks of size 2359296 totalling 33.75MiB 2025-03-08 11:21:18.812402: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 2490368 totalling 2.38MiB 2025-03-08 11:21:18.812415: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 2883584 totalling 2.75MiB 2025-03-08 11:21:18.812428: I tensorflow/core/common_runtime/bfc_allocator.cc:1002] Sum Total of in-use chunks: 83.92MiB 2025-03-08 11:21:18.812440: I tensorflow/core/common_runtime/bfc_allocator.cc:1004] total_region_allocated_bytes_: 92143616 memory_limit_: 92143616 available bytes: 0 curr_region_allocation_bytes_: 134217728 2025-03-08 11:21:18.812457: I tensorflow/core/common_runtime/bfc_allocator.cc:1010] Stats: Limit: 92143616 InUse: 87995648 MaxInUse: 87995648 NumAllocs: 1581 MaxAllocSize: 4325376 2025-03-08 11:21:18.812534: W tensorflow/core/common_runtime/bfc_allocator.cc:439] ****_*********************************************************************************************** 2025-03-08 11:21:18.812585: W tensorflow/core/framework/op_kernel.cc:1753] OP_REQUIRES failed at cwise_ops_common.h:134 : Resource exhausted: OOM when allocating tensor with shape[3,3,256,256] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 Exception in mask_detect : OOM when allocating tensor with shape[3,3,256,256] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Mul] we want to redo the detection Using TensorFlow backend. max_time_sub_proc : 3600 erreur pendant la detection Useless call to update_current_state in case -12 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! ERROR : mask output needs to be a dictionnary now ! No output to save, continue without doing anything ! save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : -12 ERROR : 'int' object is not subscriptable reconnect to base ! warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data #&_# TEST FAILED #&_# : tests/mask_test #&_# Error : invalid literal for int() with base 10: "'int' object is not subscriptable" #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : sam list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1728801727294922 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:sam Sat Mar 8 11:21:26 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0030755996704101562 nb_pixel_total : 5629 time to create 1 rle with old method : 0.017900705337524414 time for calcul the mask position with numpy : 0.0022249221801757812 nb_pixel_total : 6640 time to create 1 rle with old method : 0.024025678634643555 time for calcul the mask position with numpy : 0.002286672592163086 nb_pixel_total : 4213 time to create 1 rle with old method : 0.015893936157226562 time for calcul the mask position with numpy : 0.0019202232360839844 nb_pixel_total : 16421 time to create 1 rle with old method : 0.04332876205444336 time for calcul the mask position with numpy : 0.0016407966613769531 nb_pixel_total : 7628 time to create 1 rle with old method : 0.01975846290588379 time for calcul the mask position with numpy : 0.0031006336212158203 nb_pixel_total : 83873 time to create 1 rle with old method : 0.24271845817565918 time for calcul the mask position with numpy : 0.002354860305786133 nb_pixel_total : 29476 time to create 1 rle with old method : 0.09330010414123535 time for calcul the mask position with numpy : 0.0021753311157226562 nb_pixel_total : 5430 time to create 1 rle with old method : 0.02039051055908203 time for calcul the mask position with numpy : 0.0018579959869384766 nb_pixel_total : 10833 time to create 1 rle with old method : 0.031349897384643555 time for calcul the mask position with numpy : 0.002099752426147461 nb_pixel_total : 14648 time to create 1 rle with old method : 0.04660296440124512 time for calcul the mask position with numpy : 0.002552032470703125 nb_pixel_total : 38914 time to create 1 rle with old method : 0.11746954917907715 time for calcul the mask position with numpy : 0.0017573833465576172 nb_pixel_total : 3782 time to create 1 rle with old method : 0.012586593627929688 time for calcul the mask position with numpy : 0.001775503158569336 nb_pixel_total : 6027 time to create 1 rle with old method : 0.020898103713989258 time for calcul the mask position with numpy : 0.001867055892944336 nb_pixel_total : 13939 time to create 1 rle with old method : 0.042867183685302734 time for calcul the mask position with numpy : 0.0017099380493164062 nb_pixel_total : 2948 time to create 1 rle with old method : 0.007489919662475586 time for calcul the mask position with numpy : 0.0015337467193603516 nb_pixel_total : 2452 time to create 1 rle with old method : 0.006170034408569336 time for calcul the mask position with numpy : 0.0015528202056884766 nb_pixel_total : 1222 time to create 1 rle with old method : 0.003368854522705078 time for calcul the mask position with numpy : 0.0018563270568847656 nb_pixel_total : 9890 time to create 1 rle with old method : 0.02522444725036621 time for calcul the mask position with numpy : 0.0017621517181396484 nb_pixel_total : 3951 time to create 1 rle with old method : 0.010045528411865234 time for calcul the mask position with numpy : 0.0016033649444580078 nb_pixel_total : 2079 time to create 1 rle with old method : 0.005270719528198242 time for calcul the mask position with numpy : 0.0017843246459960938 nb_pixel_total : 16341 time to create 1 rle with old method : 0.04368424415588379 time for calcul the mask position with numpy : 0.001683950424194336 nb_pixel_total : 3502 time to create 1 rle with old method : 0.010488510131835938 time for calcul the mask position with numpy : 0.0019161701202392578 nb_pixel_total : 11885 time to create 1 rle with old method : 0.04392290115356445 time for calcul the mask position with numpy : 0.001837015151977539 nb_pixel_total : 2761 time to create 1 rle with old method : 0.007609128952026367 time for calcul the mask position with numpy : 0.0017333030700683594 nb_pixel_total : 2825 time to create 1 rle with old method : 0.007279634475708008 time for calcul the mask position with numpy : 0.0018889904022216797 nb_pixel_total : 4275 time to create 1 rle with old method : 0.010531187057495117 time for calcul the mask position with numpy : 0.0016353130340576172 nb_pixel_total : 5499 time to create 1 rle with old method : 0.01300501823425293 time for calcul the mask position with numpy : 0.0017430782318115234 nb_pixel_total : 1648 time to create 1 rle with old method : 0.004142284393310547 time for calcul the mask position with numpy : 0.0016438961029052734 nb_pixel_total : 8640 time to create 1 rle with old method : 0.020853757858276367 time for calcul the mask position with numpy : 0.0016748905181884766 nb_pixel_total : 13096 time to create 1 rle with old method : 0.03382086753845215 time for calcul the mask position with numpy : 0.0019295215606689453 nb_pixel_total : 2491 time to create 1 rle with old method : 0.006677865982055664 time for calcul the mask position with numpy : 0.0021986961364746094 nb_pixel_total : 2727 time to create 1 rle with old method : 0.007456779479980469 time for calcul the mask position with numpy : 0.0021123886108398438 nb_pixel_total : 2324 time to create 1 rle with old method : 0.011863470077514648 time for calcul the mask position with numpy : 0.0018603801727294922 nb_pixel_total : 1505 time to create 1 rle with old method : 0.004687070846557617 time for calcul the mask position with numpy : 0.002218008041381836 nb_pixel_total : 3330 time to create 1 rle with old method : 0.009351253509521484 time for calcul the mask position with numpy : 0.0020503997802734375 nb_pixel_total : 1026 time to create 1 rle with old method : 0.003221273422241211 time for calcul the mask position with numpy : 0.0018990039825439453 nb_pixel_total : 2408 time to create 1 rle with old method : 0.006888628005981445 time for calcul the mask position with numpy : 0.0023844242095947266 nb_pixel_total : 27811 time to create 1 rle with old method : 0.08748626708984375 time for calcul the mask position with numpy : 0.002148151397705078 nb_pixel_total : 10557 time to create 1 rle with old method : 0.04040837287902832 time for calcul the mask position with numpy : 0.0022368431091308594 nb_pixel_total : 342 time to create 1 rle with old method : 0.001367807388305664 time for calcul the mask position with numpy : 0.003713369369506836 nb_pixel_total : 4113 time to create 1 rle with old method : 0.0206754207611084 time for calcul the mask position with numpy : 0.002811908721923828 nb_pixel_total : 3851 time to create 1 rle with old method : 0.019591331481933594 time for calcul the mask position with numpy : 0.002553224563598633 nb_pixel_total : 1255 time to create 1 rle with old method : 0.004624128341674805 time for calcul the mask position with numpy : 0.002616405487060547 nb_pixel_total : 4171 time to create 1 rle with old method : 0.020806550979614258 time for calcul the mask position with numpy : 0.002057790756225586 nb_pixel_total : 864 time to create 1 rle with old method : 0.00422978401184082 time for calcul the mask position with numpy : 0.0034041404724121094 nb_pixel_total : 877 time to create 1 rle with old method : 0.0047893524169921875 time for calcul the mask position with numpy : 0.003400087356567383 nb_pixel_total : 2388 time to create 1 rle with old method : 0.010368585586547852 time for calcul the mask position with numpy : 0.0026912689208984375 nb_pixel_total : 595 time to create 1 rle with old method : 0.03503918647766113 time for calcul the mask position with numpy : 0.003449678421020508 nb_pixel_total : 896 time to create 1 rle with old method : 0.005255699157714844 time for calcul the mask position with numpy : 0.0027527809143066406 nb_pixel_total : 1212 time to create 1 rle with old method : 0.005129337310791016 time for calcul the mask position with numpy : 0.0019104480743408203 nb_pixel_total : 2046 time to create 1 rle with old method : 0.0089874267578125 time for calcul the mask position with numpy : 0.003010988235473633 nb_pixel_total : 1603 time to create 1 rle with old method : 0.005445003509521484 time for calcul the mask position with numpy : 0.0036764144897460938 nb_pixel_total : 1707 time to create 1 rle with old method : 0.009020090103149414 time for calcul the mask position with numpy : 0.0030579566955566406 nb_pixel_total : 340 time to create 1 rle with old method : 0.0012905597686767578 time for calcul the mask position with numpy : 0.002557039260864258 nb_pixel_total : 577 time to create 1 rle with old method : 0.002373933792114258 time for calcul the mask position with numpy : 0.002584695816040039 nb_pixel_total : 695 time to create 1 rle with old method : 0.00483393669128418 time for calcul the mask position with numpy : 0.0030372142791748047 nb_pixel_total : 18437 time to create 1 rle with old method : 0.062204837799072266 time for calcul the mask position with numpy : 0.0021758079528808594 nb_pixel_total : 16672 time to create 1 rle with old method : 0.04320693016052246 time for calcul the mask position with numpy : 0.0016102790832519531 nb_pixel_total : 1055 time to create 1 rle with old method : 0.0028238296508789062 time for calcul the mask position with numpy : 0.0014979839324951172 nb_pixel_total : 583 time to create 1 rle with old method : 0.001550436019897461 time for calcul the mask position with numpy : 0.0017287731170654297 nb_pixel_total : 27530 time to create 1 rle with old method : 0.0679628849029541 time for calcul the mask position with numpy : 0.0016028881072998047 nb_pixel_total : 8703 time to create 1 rle with old method : 0.0220487117767334 time for calcul the mask position with numpy : 0.0015239715576171875 nb_pixel_total : 1095 time to create 1 rle with old method : 0.003015279769897461 time for calcul the mask position with numpy : 0.0015647411346435547 nb_pixel_total : 1200 time to create 1 rle with old method : 0.0030422210693359375 time for calcul the mask position with numpy : 0.0016326904296875 nb_pixel_total : 3111 time to create 1 rle with old method : 0.00958395004272461 time for calcul the mask position with numpy : 0.0016033649444580078 nb_pixel_total : 1827 time to create 1 rle with old method : 0.005211830139160156 time for calcul the mask position with numpy : 0.001967906951904297 nb_pixel_total : 3179 time to create 1 rle with old method : 0.011159658432006836 time for calcul the mask position with numpy : 0.001994609832763672 nb_pixel_total : 1745 time to create 1 rle with old method : 0.005742549896240234 time for calcul the mask position with numpy : 0.002775430679321289 nb_pixel_total : 39081 time to create 1 rle with old method : 0.1305713653564453 time for calcul the mask position with numpy : 0.0017359256744384766 nb_pixel_total : 1520 time to create 1 rle with old method : 0.004435539245605469 time for calcul the mask position with numpy : 0.0020074844360351562 nb_pixel_total : 9671 time to create 1 rle with old method : 0.02339911460876465 time for calcul the mask position with numpy : 0.0016200542449951172 nb_pixel_total : 9077 time to create 1 rle with old method : 0.022920846939086914 time for calcul the mask position with numpy : 0.0015802383422851562 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006911754608154297 time for calcul the mask position with numpy : 0.0015482902526855469 nb_pixel_total : 1332 time to create 1 rle with old method : 0.003328561782836914 time for calcul the mask position with numpy : 0.0015904903411865234 nb_pixel_total : 969 time to create 1 rle with old method : 0.002468109130859375 time for calcul the mask position with numpy : 0.0015175342559814453 nb_pixel_total : 717 time to create 1 rle with old method : 0.0018606185913085938 time for calcul the mask position with numpy : 0.0015113353729248047 nb_pixel_total : 615 time to create 1 rle with old method : 0.0015223026275634766 time for calcul the mask position with numpy : 0.0015225410461425781 nb_pixel_total : 248 time to create 1 rle with old method : 0.0007178783416748047 time for calcul the mask position with numpy : 0.0015163421630859375 nb_pixel_total : 8460 time to create 1 rle with old method : 0.019692182540893555 time for calcul the mask position with numpy : 0.0015158653259277344 nb_pixel_total : 222 time to create 1 rle with old method : 0.0006124973297119141 time for calcul the mask position with numpy : 0.0014472007751464844 nb_pixel_total : 971 time to create 1 rle with old method : 0.002410411834716797 time for calcul the mask position with numpy : 0.0014986991882324219 nb_pixel_total : 735 time to create 1 rle with old method : 0.001984119415283203 time for calcul the mask position with numpy : 0.001575469970703125 nb_pixel_total : 5088 time to create 1 rle with old method : 0.01248931884765625 time for calcul the mask position with numpy : 0.0016074180603027344 nb_pixel_total : 7512 time to create 1 rle with old method : 0.01932668685913086 time for calcul the mask position with numpy : 0.0021255016326904297 nb_pixel_total : 1644 time to create 1 rle with old method : 0.006367683410644531 time for calcul the mask position with numpy : 0.002284526824951172 nb_pixel_total : 941 time to create 1 rle with old method : 0.004277944564819336 time for calcul the mask position with numpy : 0.002349376678466797 nb_pixel_total : 1443 time to create 1 rle with old method : 0.00617671012878418 time for calcul the mask position with numpy : 0.002374887466430664 nb_pixel_total : 1438 time to create 1 rle with old method : 0.006574392318725586 time for calcul the mask position with numpy : 0.0029196739196777344 nb_pixel_total : 595 time to create 1 rle with old method : 0.0032105445861816406 time for calcul the mask position with numpy : 0.0018768310546875 nb_pixel_total : 2209 time to create 1 rle with old method : 0.00772857666015625 time for calcul the mask position with numpy : 0.0018420219421386719 nb_pixel_total : 1133 time to create 1 rle with old method : 0.003011941909790039 time for calcul the mask position with numpy : 0.0014879703521728516 nb_pixel_total : 889 time to create 1 rle with old method : 0.002229928970336914 time for calcul the mask position with numpy : 0.0016415119171142578 nb_pixel_total : 300 time to create 1 rle with old method : 0.0011034011840820312 time for calcul the mask position with numpy : 0.0017325878143310547 nb_pixel_total : 1381 time to create 1 rle with old method : 0.004944324493408203 time for calcul the mask position with numpy : 0.0017504692077636719 nb_pixel_total : 956 time to create 1 rle with old method : 0.0033867359161376953 time for calcul the mask position with numpy : 0.0017452239990234375 nb_pixel_total : 885 time to create 1 rle with old method : 0.0030791759490966797 time for calcul the mask position with numpy : 0.0018379688262939453 nb_pixel_total : 930 time to create 1 rle with old method : 0.005403995513916016 time for calcul the mask position with numpy : 0.0024225711822509766 nb_pixel_total : 1682 time to create 1 rle with old method : 0.006674051284790039 time for calcul the mask position with numpy : 0.0025014877319335938 nb_pixel_total : 1082 time to create 1 rle with old method : 0.00508427619934082 time for calcul the mask position with numpy : 0.0023632049560546875 nb_pixel_total : 16916 time to create 1 rle with old method : 0.05701875686645508 time for calcul the mask position with numpy : 0.0017240047454833984 nb_pixel_total : 830 time to create 1 rle with old method : 0.0021877288818359375 batch 1 Loaded 101 chid ids of type : 4677 Number RLEs to save : 9743 TO DO : save crop sub photo not yet done ! Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] Looping around the photos to save general results len do output : 1 /1189321094Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4573', None, None, None, None, None, None, None, None) ('4573', None, '1189321094', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.017645835876464844 save_final save missing photos in datou_result : time spend for datou_step_exec : 18.998854637145996 time spend to save output : 0.01821112632751465 total time spend for step 1 : 19.01706576347351 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1741429286_4154994_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 101 ############################### TEST frcnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : frcnn list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.10412859916687012 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:frcnn Sat Mar 8 11:21:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1741429305_4154994_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg image_size (600, 800, 3) [[[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] ... [[ 14 16 16] [ 13 15 15] [ 11 13 13] ... [198 206 205] [198 206 205] [198 206 205]] [[ 16 18 18] [ 14 16 16] [ 11 13 13] ... [206 214 213] [206 214 213] [206 214 213]] [[ 13 15 15] [ 12 14 14] [ 9 11 11] ... [210 218 217] [210 218 217] [210 218 217]]] Detection took 0.132s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 3.952500104904175 time spend to save output : 0.00012230873107910156 total time spend for step 1 : 3.952622413635254 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False Inside saveFrcnn : final : True verbose : False threshold to save the result : 0.1 Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.015760421752929688 [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.014953374862670898 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.06384058, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052224033, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271848, None)], 'temp/1741429305_4154994_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.078369140625 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Sat Mar 8 11:21:49 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 355 time to import caffe and check if the image exist : 0.013506889343261719 time to convert the images to numpy array : 0.006256103515625 total time to convert the images to numpy array : 0.020289182662963867 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__port_506135,astra_1027_gao__port_506215,2_1027_gao__port_506151,doblo_1027_gao__port_506251,807_1027_gao__port_506152,206_1027_gao__port_506126,a7_1027_gao__port_506373,renegade_1027_gao__port_506346', 'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__port_506135,astra_1027_gao__port_506215,2_1027_gao__port_506151,doblo_1027_gao__port_506251,807_1027_gao__port_506152,206_1027_gao__port_506126,a7_1027_gao__port_506373,renegade_1027_gao__port_506346', 'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 4638 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : car_360_1027 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 4638 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.016873836517333984 time used to do the prediction : 0.08614778518676758 save descriptor for thcl : 355 time to traite the descriptors : 0.07610774040222168 storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.6486907005310059 Inside saveOutput : final : False verbose : False time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 1.1205673217773438e-05 save missing photos in datou_result : time spend for datou_step_exec : 7.042692184448242 time spend to save output : 1.5690879821777344 total time spend for step 1 : 8.611780166625977 step2:argmax Sat Mar 8 11:21:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 355 Inside saveOutput : final : True verbose : False photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.017712181, 332, '355'), 'temp/1741429309_4154994_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.046045780181884766 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.01579141616821289 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.028154850006103516 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 7.62939453125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00045990943908691406 time spend to save output : 0.09052658081054688 total time spend for step 2 : 0.09098649024963379 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.017712181, 332, '355'), 'temp/1741429309_4154994_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12835 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12836 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.19644594192504883 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Sat Mar 8 11:21:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory 2025-03-08 11:22:03.909764: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-08 11:22:03.911011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 11:22:03.911171: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:22:03.911240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:22:03.923457: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 11:22:03.923642: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 11:22:03.946474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 11:22:03.949956: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 11:22:03.995256: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:22:03.996756: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 11:22:03.997581: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-08 11:22:04.031408: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-08 11:22:04.033134: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb7b4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-08 11:22:04.033208: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-08 11:22:04.039704: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xa2933a80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-08 11:22:04.039746: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-08 11:22:04.041276: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 11:22:04.041447: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:22:04.041473: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 11:22:04.041595: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 11:22:04.041626: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 11:22:04.041662: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 11:22:04.041700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 11:22:04.041738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 11:22:04.042934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 11:22:04.043000: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 11:22:04.043071: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 11:22:04.043083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 11:22:04.043093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 11:22:04.044321: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) l 3637 free memory gpu now : 4638 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] Traceback (most recent call last): File "/home/admin/workarea/git/Velours/python/tests/python_tests.py", line 245, in v, r = module_name.test(verbose=verbose, context=context) File "/home/admin/workarea/git/Velours/python/tests/mask_test.py", line 264, in test title2, result2, res_bool2 = test_detect_objet(context, verbose) File "/home/admin/workarea/git/Velours/python/tests/mask_test.py", line 90, in test_detect_objet test_nb = len(ret_da["917855882"][0]) == nb_rectangle_nonreg TypeError: 'int' object is not subscriptable File "/usr/local/bin/coverage3", line 8, in sys.exit(main()) File "/usr/local/lib/python3.8/dist-packages/coverage/cmdline.py", line 865, in main status = CoverageScript().command_line(argv) File "/usr/local/lib/python3.8/dist-packages/coverage/cmdline.py", line 582, in command_line return self.do_run(options, args) File "/usr/local/lib/python3.8/dist-packages/coverage/cmdline.py", line 740, in do_run runner.run() File "/usr/local/lib/python3.8/dist-packages/coverage/execfile.py", line 247, in run exec(code, main_mod.__dict__) File "/home/admin/workarea/git/Velours/python/tests/python_tests.py", line 251, in traceback.print_stack() /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 15.629414081573486 time used to load_weights : 0.1666584014892578 0it [00:00, ?it/s] 3it [00:00, 27.21it/s] 3it [00:00, 27.13it/s]2025-03-08 11:22:23.773329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1741429318_4154994_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1741429318_4154994_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1741429318_4154994_1171252764_29d5179a892cc50aadc9d67245534b59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 5.254276990890503 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 time to traite the descriptors : 0.04470252990722656 storage_type for insertDescriptorsMulti : 3 To insert : 1171252784 To insert : 1171252487 To insert : 1171252764 time to insert the descriptors : 0.7694239616394043 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252784, 1171252487, 1171252764] Looping around the photos to save general results len do output : 3 /1171252784Didn't retrieve data . /1171252487Didn't retrieve data . /1171252764Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.012483358383178711 save_final save missing photos in datou_result : time spend for datou_step_exec : 32.12293100357056 time spend to save output : 0.03292226791381836 total time spend for step 1 : 32.155853271484375 step2:argmax Sat Mar 8 11:22:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 Inside saveOutput : final : True verbose : False photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.96776694, 4674, '3609'), 'temp/1741429318_4154994_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.92624104, 4674, '3609'), 'temp/1741429318_4154994_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.9853658, 4674, '3609'), 'temp/1741429318_4154994_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.009921789169311523 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.013677597045898438 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.011937618255615234 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 5.0067901611328125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.000286102294921875 time spend to save output : 0.04062056541442871 total time spend for step 2 : 0.040906667709350586 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252784': [(1171252784, 'jrm', 0.96776694, 4674, '3609'), 'temp/1741429318_4154994_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252487': [(1171252487, 'jrm', 0.92624104, 4674, '3609'), 'temp/1741429318_4154994_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853658, 4674, '3609'), 'temp/1741429318_4154994_1171252764_29d5179a892cc50aadc9d67245534b59.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.5705459117889404 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Sat Mar 8 11:22:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1086 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1086 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 12 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1086 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1528 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1528 wait 20 seconds l 3637 free memory gpu now : 1528 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_1[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_2[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 24.899150848388672 time used to load_weights : 0.4089217185974121 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 2.3859946727752686 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 time to traite the descriptors : 0.048509836196899414 storage_type for insertDescriptorsMulti : 3 To insert : 1171275372 To insert : 1171291875 To insert : 1171275314 time to insert the descriptors : 1.1217708587646484 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171275372, 1171291875, 1171275314] Looping around the photos to save general results len do output : 3 /1171275372Didn't retrieve data . /1171291875Didn't retrieve data . /1171275314Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.013457775115966797 save_final save missing photos in datou_result : time spend for datou_step_exec : 161.29367351531982 time spend to save output : 0.053594350814819336 total time spend for step 1 : 161.34726786613464 step2:argmax Sat Mar 8 11:25:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 Inside saveOutput : final : True verbose : False photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.9674468, 4723, '3655'), 'temp/1741429350_4154994_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.97076905, 4723, '3655'), 'temp/1741429350_4154994_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96518344, 4723, '3655'), 'temp/1741429350_4154994_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.00927424430847168 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 2.688591480255127 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.014938116073608398 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 5.4836273193359375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0011403560638427734 time spend to save output : 2.719871997833252 total time spend for step 2 : 2.7210123538970947 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171275372': [(1171275372, 'tapis_vide', 0.9674468, 4723, '3655'), 'temp/1741429350_4154994_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171291875': [(1171291875, 'tapis_vide', 0.97076905, 4723, '3655'), 'temp/1741429350_4154994_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96518344, 4723, '3655'), 'temp/1741429350_4154994_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 51 thcl : 358 photo_hashtag_type : 337 ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14424490928649902 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rotate Sat Mar 8 11:25:15 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! Needs to change image size ! Needs to change image size ! About to upload 3 photos upload in portfolio : 551782 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741429516_4154994 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.0534777641296387 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 1.2988204956054688 time spend to save output : 4.458427429199219e-05 total time spend for step 1 : 1.2988650798797607 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 3 /1341829208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('230', None, None, None, None, None, None, None, None) ('230', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.017349958419799805 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1341829208: ['917849322', 'temp/1741429515_4154994_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1341829209: ['917849322', 'temp/1741429515_4154994_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1341829210: ['917849322', 'temp/1741429515_4154994_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.13687515258789062 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:thcl Sat Mar 8 11:25:17 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 500 time to import caffe and check if the image exist : 0.000377655029296875 time to convert the images to numpy array : 0.9813179969787598 total time to convert the images to numpy array : 0.9823236465454102 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 886 wait 20 seconds l 3637 free memory gpu now : 886 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : orientation_carte_grise_all_2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2942 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 2.9972400665283203 time used to do the prediction : 0.1816420555114746 save descriptor for thcl : 500 time to traite the descriptors : 0.06705403327941895 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 1.0738799571990967 time spend for datou_step_exec : 30.929198741912842 time spend to save output : 8.106231689453125e-05 total time spend for step 1 : 30.929279804229736 step2:argmax Sat Mar 8 11:25:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step Argmax ! calculate argmax for thcl : 500 time spend for datou_step_exec : 0.0003936290740966797 time spend to save output : 3.647804260253906e-05 total time spend for step 2 : 0.00043010711669921875 step3:rotate Sat Mar 8 11:25:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_rotate ! We are in a datou with depends ! angle_condi : {'carteGrisesVerticales__port_549774': 0, 'cartegrise_90deg__port_550987': 270, 'portfolio_270deg__port_550988': 90, 'cartesGrisesEnvers__port_549765': 180} rotate photos for hashtag carteGrisesVerticales__port_549774 of 0 degres 1 photos founded : [917849322] batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! About to upload 1 photos upload in portfolio : 551782 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741429549_4154994 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.6735305786132812 Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres 0 photos founded : [] rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres 0 photos founded : [] rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres 0 photos founded : [] time spend for datou_step_exec : 0.7965350151062012 time spend to save output : 4.792213439941406e-05 total time spend for step 3 : 0.7965829372406006 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 1 /1341829252Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('233', None, None, None, None, None, None, None, None) ('233', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.01317739486694336 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1341829252: ['917849322', 'temp/1741429517_4154994_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : crop, tile, rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.4106001853942871 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:crop Sat Mar 8 11:25:50 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Crop ! param_json : {'hashtag_id_ellipse': 2087736828, 'photo_hashtag_type_from_ellipse': 520, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'crop_detect_varroa', 'photo_hashtag_type': 407, 'feed_id_new_photos_not_used': 549103, 'host': 'www.fotonower.com', 'margin': 8, 'upload_type': 'python'} margin_type : margin margin_value : [8, 8, 8, 8] Loading chi in step crop with photo_hashtag_type : 407 Loading chi in step crop for list_pids : 1 ! batch 1 Loaded 4 chid ids of type : 407 +WARNING : Unexpected points, we should remove this data for chi_id : 8165075, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165076, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165077, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165078, for now we just ignore these empty polygon points WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 4 Here we crop with rles About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=crop_detect_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 21177616 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741429631_4154994 we have uploaded 4 photos in the portfolio 21177616 time of upload the photos Elapsed time : 82.05067539215088 Now we prepare data that will be used for ellipse search ! About to compute ellipse and record with type : 520 score : 5120 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2311 | arg_min : -30.0 min_score : 1968 | arg_min : 17.8125 min_score : 1614 | arg_min : 31.875 min_score : 1105 | arg_min : 28.5 min_score : 1105 arg_min : 1.9500000000000002 min_score : 1105 arg_min : 25.0 min_score : 1088 arg_min : 24.9375 min_score : 979 arg_min : 31.875 min_score : 979 arg_min : 28.5 min_score : 979 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! score : 5362 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2281 | arg_min : -10.0 min_score : 2127 | arg_min : 25.0 min_score : 2127 | arg_min : 30.9375 min_score : 714 | arg_min : 25.0 min_score : 714 arg_min : 1.9500000000000002 min_score : 714 arg_min : -5.0 min_score : 668 arg_min : 23.4375 min_score : 655 arg_min : 29.53125 min_score : 631 arg_min : 25.0 min_score : 631 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! score : 4603 strategy_opt : 5| | arg_min : 1.85 min_score : 2981 | arg_min : -50.0 min_score : 1356 | arg_min : 30.28125 min_score : 1079 | arg_min : 23.625 min_score : 995 | arg_min : 27.0 min_score : 995 arg_min : 1.6500000000000001 min_score : 961 arg_min : -70.0 min_score : 852 arg_min : 28.6875 min_score : 847 arg_min : 23.625 min_score : 847 arg_min : 27.0 min_score : 847 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! score : 7970 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 1576 | arg_min : 40.0 min_score : 632 | arg_min : 20.15625 min_score : 561 | arg_min : 26.0 min_score : 561 | arg_min : 26.0 min_score : 561 arg_min : 1.8 min_score : 520 arg_min : 40.0 min_score : 520 arg_min : 18.8125 min_score : 494 arg_min : 26.0 min_score : 494 arg_min : 26.0 min_score : 494 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 21177617 Result OK ! uploaded one batch 0 Elapsed time : 22.205784559249878 time spend for datou_step_exec : 109.37913179397583 time spend to save output : 2.0742416381835938e-05 total time spend for step 1 : 109.37915253639221 step2:tile Sat Mar 8 11:27:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'photo_tile_type': 17, 'whiten': True, 'remove_crop_border': True, 'minimal_size_crop_border': 900, 'stride': 240, 'crop_hashtag_type_tiled': 521, 'ETA': 86400, 'new_width': 480, 'new_height': 480, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_taggage_varroa', 'crop_hashtag_type': 520, 'host': 'www.fotonower.com', 'arg_aux_upload': {'type_upload': 'python'}} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {520: 521} TO DEPRECATE VR 14-6-18 map_filenames : {937852786: 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 Loaded 4 chid ids of type : 520 ++++https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 21177618 with name tile_taggage_varroa feed_id_new_photos : 21177618 filename : temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg photo_id : 937852786 height_image_input : 480 width_image_input : 480 new_width : 480 new_height : 480 stride : 240 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 4 list_bib_to_crops : 1 [(0, 480, 0, 480, 0)] new_crops_tiles : 1 crop_transformed : 4 batch 1 Loaded 1 chid ids of type : 17 treat the image : temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.012985944747924805 on upload les photos avec python init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741429671_4154994 we have uploaded 1 photos in the portfolio 21177618 Importing ! upload mediasElapsed time : 0.7249011993408203 , 0Saving 4 CHIs. batch 1 Loaded 4 chid ids of type : 521 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 1.8799264430999756 time spend for datou_step_exec : 13.08562159538269 time spend to save output : 3.0040740966796875e-05 total time spend for step 2 : 13.085651636123657 step3:rotate Sat Mar 8 11:27:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a datou with depends ! rotate photos of 0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345 degres batch 1 Loaded 4 chid ids of type : 521 ++++++++ map_chi of length : 1 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 21177619 Needs to change image size ! time for calcul the mask position with numpy : 0.0024907588958740234 nb_pixel_total : 1389 time to create 1 rle with old method : 0.005603313446044922 .time for calcul the mask position with numpy : 0.0004267692565917969 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0033130645751953125 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00039505958557128906 nb_pixel_total : 694 time to create 1 rle with old method : 0.0018310546875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.000362396240234375 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0027828216552734375 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004901885986328125 nb_pixel_total : 221 time to create 1 rle with old method : 0.0009016990661621094 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004379749298095703 nb_pixel_total : 1155 time to create 1 rle with old method : 0.004146099090576172 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004684925079345703 nb_pixel_total : 143 time to create 1 rle with old method : 0.000614166259765625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004391670227050781 nb_pixel_total : 1161 time to create 1 rle with old method : 0.004317283630371094 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003848075866699219 nb_pixel_total : 414 time to create 1 rle with old method : 0.00107574462890625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00043201446533203125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0035343170166015625 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00041866302490234375 nb_pixel_total : 1204 time to create 1 rle with old method : 0.003622293472290039 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003561973571777344 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027942657470703125 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003600120544433594 nb_pixel_total : 264 time to create 1 rle with old method : 0.0009033679962158203 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00040030479431152344 nb_pixel_total : 1389 time to create 1 rle with old method : 0.004045963287353516 .time for calcul the mask position with numpy : 0.00036334991455078125 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0028498172760009766 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00038170814514160156 nb_pixel_total : 694 time to create 1 rle with old method : 0.002253293991088867 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003609657287597656 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0027658939361572266 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00037980079650878906 nb_pixel_total : 221 time to create 1 rle with old method : 0.0007178783416748047 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039005279541015625 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0035207271575927734 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003724098205566406 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004382133483886719 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003628730773925781 nb_pixel_total : 1160 time to create 1 rle with old method : 0.002744913101196289 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00041985511779785156 nb_pixel_total : 414 time to create 1 rle with old method : 0.001354217529296875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003612041473388672 nb_pixel_total : 1159 time to create 1 rle with old method : 0.003465414047241211 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003409385681152344 nb_pixel_total : 1 time to create 1 rle with old method : 2.1696090698242188e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.0003821849822998047 nb_pixel_total : 1204 time to create 1 rle with old method : 0.003690958023071289 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003604888916015625 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0037975311279296875 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003502368927001953 nb_pixel_total : 264 time to create 1 rle with old method : 0.0008699893951416016 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00044608116149902344 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003975868225097656 .time for calcul the mask position with numpy : 0.0003635883331298828 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0034563541412353516 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004799365997314453 nb_pixel_total : 727 time to create 1 rle with old method : 0.002565145492553711 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004687309265136719 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0029799938201904297 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004010200500488281 nb_pixel_total : 250 time to create 1 rle with old method : 0.0007228851318359375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035262107849121094 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002824544906616211 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003523826599121094 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005428791046142578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003578662872314453 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0028061866760253906 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.000392913818359375 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011794567108154297 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003628730773925781 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002947568893432617 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003447532653808594 nb_pixel_total : 1 time to create 1 rle with old method : 2.288818359375e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.0004229545593261719 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0029442310333251953 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035572052001953125 nb_pixel_total : 1158 time to create 1 rle with old method : 0.003080129623413086 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0004012584686279297 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006763935089111328 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004107952117919922 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0032362937927246094 .time for calcul the mask position with numpy : 0.00035572052001953125 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002777099609375 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003993511199951172 nb_pixel_total : 727 time to create 1 rle with old method : 0.001786947250366211 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003561973571777344 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002773761749267578 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003745555877685547 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006988048553466797 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037097930908203125 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002764463424682617 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004169940948486328 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005033016204833984 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00040793418884277344 nb_pixel_total : 1161 time to create 1 rle with old method : 0.002763032913208008 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00033545494079589844 nb_pixel_total : 450 time to create 1 rle with old method : 0.001203775405883789 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003418922424316406 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0029680728912353516 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004296302795410156 nb_pixel_total : 1237 time to create 1 rle with old method : 0.003712892532348633 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003800392150878906 nb_pixel_total : 1157 time to create 1 rle with old method : 0.36359596252441406 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00041747093200683594 nb_pixel_total : 234 time to create 1 rle with old method : 0.0008270740509033203 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 21177619 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741429677_4154994 we have uploaded 24 photos in the portfolio 21177619 time of upload the photos Elapsed time : 8.592879056930542 Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 batch 1 Loaded 28 chid ids of type : 529 Number RLEs to save : 1197 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! Catched exception ! Connect or reconnect ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 25.256555795669556 time spend to save output : 0.00010991096496582031 total time spend for step 3 : 25.25666570663452 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1341829529'] Looping around the photos to save general results len do output : 24 /1341829543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341829574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '1341829529', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 time used for this insertion : 0.021489381790161133 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1341829543: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1341829545: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1341829546: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1341829547: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1341829549: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1341829550: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1341829551: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1341829553: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1341829554: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1341829555: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1341829557: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1341829558: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1341829559: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1341829561: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1341829562: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1341829563: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1341829565: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1341829566: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1341829567: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1341829569: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1341829570: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1341829572: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1341829573: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1341829574: ['937852786', 'temp/1741429550_4154994_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} list chi : [[, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], []] ############################### TEST flip ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : flip list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.46311259269714355 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:flip Sat Mar 8 11:32:22 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_flip ! We are in a linear step without datou_depend ! batch 1 Loaded 6 chid ids of type : 741 +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points + map_chi_objs of length : 1 photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 Horizontal flip of photo 911785586 About to upload 2 photos upload in portfolio : 1090565 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741429944_4154994 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 3.9224140644073486 Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 batch 1 Loaded 12 chid ids of type : 741 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 5.073652029037476 time spend to save output : 5.078315734863281e-05 total time spend for step 1 : 5.073702812194824 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] Looping around the photos to save general results len do output : 2 /1341830814 /1341830817 before output type Managing all output in save final without adding information in the mtr_datou_result ('571', None, None, None, None, None, None, None, None) ('571', None, '911785586', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.014203310012817383 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1341830814': ['911785586', 'temp/1741429942_4154994_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1341830817': ['911785586', 'temp/1741429942_4154994_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', [, , , , , ]]} ############################### TEST crop_rles ################################ # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : TEST CROP RLES Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : crop list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.4284794330596924 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:crop Sat Mar 8 11:32:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Crop ! param_json : {'photo_hashtag_type': 755, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 0, 'host': 'www.fotonower.com', 'crop_type': 'rle', 'margin_relative': 0.1, 'min_score': 0.3, 'upload,type': 'python'} margin_type : margin_relative margin_value : [0.1, 0.1, 0.1, 0.1] Loading chi in step crop with photo_hashtag_type : 755 Loading chi in step crop for list_pids : 1 ! batch 1 Loaded 8 chid ids of type : 755 ++++++++WARNING : margin is only used for type bib ! we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles map_result returned by crop_photo_return_map_crop : length : 8 Here we crop with rles About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 21177787 Result OK ! uploaded one batch 0 Elapsed time : 25.02870774269104 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 27.484098434448242 time spend to save output : 2.4557113647460938e-05 total time spend for step 1 : 27.48412299156189 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] Looping around the photos to save general results len do output : 8 /1341830943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341830960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341830993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341831017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341831044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341831067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341831105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341831127Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('686', None, None, None, None, None, None, None, None) ('686', None, '950103132', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 time used for this insertion : 0.1558215618133545 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1341830943': ['950103132', 'temp/1741429948_4154994_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1341830960': ['950103132', 'temp/1741429948_4154994_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1341830993': ['950103132', 'temp/1741429948_4154994_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1341831017': ['950103132', 'temp/1741429948_4154994_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1341831044': ['950103132', 'temp/1741429948_4154994_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1341831067': ['950103132', 'temp/1741429948_4154994_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1341831105': ['950103132', 'temp/1741429948_4154994_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1341831127': ['950103132', 'temp/1741429948_4154994_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : angular_coeff list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.6237318515777588 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:angular_coeff Sat Mar 8 11:32:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter param_json : {'input_type': 846, 'output_type': -1, 'orientation_type': 872, 'ref_crop_type': 846, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'crops_coeffs': {'CAR_EXTERIEUR_angle_avant_droit.*': {'aile-avant': [[15, 0.0], [240, 0.0], [285, 1.0], [345, 1.0]], 'capot': [[45, 1.0], [60, 0.5], [270, 0.0], [315, 1.0], [360, 1.0]]}}} angular_coefficients_to_crops batch 1 Loaded 19 chid ids of type : 846 treating photo 932296368 time spend for datou_step_exec : 3.332279920578003 time spend to save output : 6.651878356933594e-05 total time spend for step 1 : 3.3323464393615723 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {932296368: ([(932296368, 2106233860, 846, 1066, 1277, 93, 340, 0.31964028378983567, 0, []), (932296368, 2106233860, 846, 434, 690, 218, 498, 0.7170410105787726, 0, []), (932296368, 503548896, 846, 902, 1111, 466, 576, 0.31724966, 769189715, []), (932296368, 599722655, 846, 523, 1100, 152, 337, 0.98039776, 0, []), (932296368, 492601069, 846, 143, 1190, 90, 695, 0.9696157, 769189717, []), (932296368, 492601069, 846, 0, 408, 246, 719, 0.9431181, 769189718, []), (932296368, 2096875722, 846, 567, 964, 162, 215, 0.55490255, 769189721, []), (932296368, 2096875709, 846, 437, 939, 24, 198, 0.9983077, 769189723, []), (932296368, 2096875709, 846, 1004, 1263, 28, 144, 0.9485744, 769189724, []), (932296368, 624624117, 846, 595, 1122, 331, 640, 0.99100167, 769189725, []), (932296368, 492624020, 846, 585, 874, 308, 393, 0.78697366, 769189727, []), (932296368, 2096875719, 846, 943, 1100, 428, 547, 0.96733797, 769189729, []), (932296368, 492654799, 846, 253, 467, 35, 441, 0.99621326, 769189730, []), (932296368, 492689227, 846, 1118, 1264, 270, 438, 0.9901647, 769189732, []), (932296368, 492689227, 846, 486, 671, 378, 690, 0.98789483, 769189733, []), (932296368, 492689227, 846, 161, 255, 229, 409, 0.70801014, 769189734, []), (932296368, 492925064, 846, 261, 421, 27, 193, 0.92215157, 769189737, []), (932296368, 492925064, 846, 873, 1045, 46, 156, 0.7535122, 769189738, []), (932296368, 492925064, 846, 1090, 1279, 20, 107, 0.45259848, 769189739, [])],)} test angular coeff is a success ! ############################### TEST detection_filter_by_crop ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_crop list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.23794293403625488 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_crop Sat Mar 8 11:33:00 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter param_json : {'input_type': 631, 'output_type': -1, 'condition_type': 445, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'min_surface_ratio': 0.7} conditional_crop_copy batch 1 Loaded 3 chid ids of type : 445 +++batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++batch 1 Loaded 3 chid ids of type : 445 +++ treating photo 946711423 time spend for datou_step_exec : 3.8966968059539795 time spend to save output : 8.58306884765625e-05 total time spend for step 1 : 3.896782636642456 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, ['491,350,489,350,488,349,487,350,483,350,480,348,480,341,482,339,482,337,485,334,487,334,491,330,494,330,495,328,498,326,501,326,503,324,507,325,509,323,514,321,516,319,518,321,520,321,521,319,522,319,524,321,527,321,530,317,530,315,531,314,535,313,540,309,543,310,544,311,542,313,542,314,544,316,541,318,541,322,536,322,535,323,533,323,532,322,528,322,527,321,524,321,522,323,518,322,516,324,517,327,516,328,512,327,510,329,512,332,513,332,515,330,516,331,516,333,514,332,511,333,511,336,514,337,516,336,516,339,515,339,513,338,511,340,512,341,512,342,510,343,507,343,502,347,500,347,497,349,492,349', '514,325,515,324,513,322,512,322,511,325,512,326', '522,327,521,327,521,326,522,325']), (946711423, 599722655, 631, 176, 535, 138, 264, 0.9818268, 1947740373, ['453,253,413,253,412,252,387,252,386,250,386,248,383,246,379,245,376,243,361,243,361,240,362,239,359,238,358,237,356,237,355,236,352,236,351,235,333,235,332,234,329,234,329,233,331,231,331,229,329,228,328,224,330,222,330,221,324,218,308,219,307,218,302,218,298,216,288,217,287,218,285,218,283,220,283,221,287,224,295,225,295,225,294,226,289,226,288,227,283,227,282,228,273,228,272,229,271,228,259,228,258,227,254,227,253,226,247,225,247,225,251,221,248,218,243,216,247,213,248,213,249,212,248,211,246,211,245,210,241,210,240,209,237,209,236,208,231,207,230,206,228,202,224,201,223,200,221,200,220,199,214,198,213,195,211,193,208,193,203,189,203,184,201,181,201,176,198,171,199,170,199,158,203,154,205,153,205,151,206,149,209,149,210,148,225,148,226,147,283,147,284,148,287,148,288,147,305,147,306,148,312,148,313,147,354,147,355,146,428,146,429,147,433,147,434,148,437,148,438,149,451,149,457,156,459,162,462,165,464,166,471,166,472,165,477,165,480,167,480,171,486,175,488,175,489,176,502,176,503,178,503,180,509,185,509,189,512,193,512,199,513,200,513,203,514,204,514,210,513,211,514,217,512,221,513,222,513,225,510,229,510,235,507,237,504,238,502,243,490,243,489,244,485,244,484,245,480,245,479,246,463,246,462,247,460,247,458,249,457,252,454,252', '528,212,528,207,526,206,524,203,526,203,527,202,528,202', '299,215,302,212,299,211,298,210,291,210,290,211,281,212,286,215,290,215,291,216', '375,242,376,240,375,238,363,239,368,242,371,242,372,243']), (946711423, 492844413, 631, 89, 163, 93, 144, 0.9772748, 1947740375, ['159,142,153,141,151,139,148,138,145,135,141,133,139,133,138,132,131,132,130,131,125,131,124,130,121,130,120,129,116,129,115,128,112,128,108,126,106,126,100,123,98,121,94,113,94,104,97,101,103,98,105,98,106,97,110,97,111,96,116,96,117,95,132,95,133,96,139,97,141,99,144,100,149,105,150,107,154,108,155,113,157,115,158,115,160,118,160,120,161,121,161,133,160,134,160,140']), (946711423, 2096875709, 631, 185, 431, 39, 136, 0.97171515, 1947740377, ['331,134,287,134,286,133,284,133,283,134,272,134,271,133,264,133,263,134,258,134,257,133,254,133,253,132,236,132,235,131,225,131,224,132,223,131,213,131,212,130,208,130,207,129,204,129,203,128,199,127,193,121,192,117,189,113,189,110,188,109,187,93,186,92,187,91,187,89,186,88,186,65,185,64,186,63,186,61,185,60,185,48,186,47,186,42,187,40,232,40,233,41,248,41,249,42,281,43,282,44,290,44,291,45,300,45,301,46,308,46,309,47,314,47,315,48,322,49,328,53,334,54,336,56,339,57,344,62,349,64,351,66,353,67,356,67,358,69,359,72,363,76,367,78,369,80,379,91,380,93,383,94,390,100,393,101,395,103,396,106,399,109,402,110,406,115,408,115,410,117,410,120,412,123,411,127,409,129,399,129,398,130,395,130,394,131,378,131,377,132,368,132,367,131,346,131,345,132,342,132,341,133,332,133']), (946711423, 2096875722, 631, 198, 395, 118, 142, 0.9699756, 1947740378, ['328,137,251,137,250,136,249,137,241,137,240,136,219,136,218,135,213,135,212,134,206,133,205,132,201,131,200,130,200,122,201,121,205,121,206,122,222,122,226,124,239,124,240,125,369,125,370,124,371,125,389,125,391,127,391,133,390,134,386,134,385,135,380,135,379,134,375,134,374,135,341,135,340,136,329,136']), (946711423, 499500794, 631, 93, 107, 127, 146, 0.9574813, 1947740379, ['101,143,98,143,95,139,95,131,97,129,100,129,101,133,102,134,102,136,103,137,103,140']), (946711423, 492925064, 631, 71, 125, 36, 95, 0.95296955, 1947740380, ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80,82,75,77,75,75,74,74,74,66,75,65,75,62,77,60,77,58,80,55,80,54,83,51,83,50,88,45,94,44,95,43,99,43,100,42,113,42,117,45,117,47,116,48,116,51,115,52,114,59,113,60,112,65,111,66,111,69,110,70,110,75,109,76,109,83,108,84,108,86,109,87,108,89']), (946711423, 492925064, 631, 101, 167, 38, 127, 0.9508439, 1947740381, ['154,117,152,115,152,112,150,110,148,106,148,104,145,101,143,100,138,100,137,99,135,99,133,95,131,95,126,93,126,91,128,88,128,83,129,82,129,70,127,68,127,66,128,65,125,61,127,59,127,56,129,52,129,49,130,47,135,42,144,42,148,45,151,49,151,60,152,61,152,75,153,76,153,80,155,83,155,87,156,88,156,105,155,106,156,107,156,110,154,112,156,116', '109,100,108,100,107,99,109,97']), (946711423, 492624020, 631, 249, 400, 219, 316, 0.8792459, 1947740382, ['395,313,390,313,386,311,384,312,381,312,376,309,358,308,357,307,354,307,353,306,350,306,349,307,345,305,343,303,341,304,337,304,334,302,325,302,324,301,315,300,313,298,313,297,310,295,304,295,300,293,295,293,291,288,289,287,283,287,281,285,281,283,278,280,274,280,272,279,272,276,270,273,270,270,269,268,266,265,265,265,264,264,264,262,261,260,260,258,260,256,261,255,259,252,259,248,258,246,255,244,256,241,252,239,251,238,251,226,265,226,266,227,268,227,272,232,276,232,277,233,279,233,281,234,283,237,285,238,290,239,293,241,296,241,304,246,312,247,316,251,318,252,320,252,326,255,328,255,337,260,342,260,343,261,345,261,349,264,351,264,355,266,357,268,364,271,366,273,370,275,374,275,376,276,377,279,379,281,383,282,384,283,386,283,387,286,390,289,390,290,394,294,396,294,398,296,398,308,397,309,397,311']), (946711423, 503548896, 631, 302, 540, 339, 403, 0.7406652, 1947740386, ['442,401,372,401,372,397,370,395,369,392,366,390,367,389,366,386,357,386,354,384,350,384,349,383,320,383,319,382,320,378,318,376,318,374,314,370,309,370,308,369,306,369,305,363,305,357,306,356,306,353,307,353,308,354,313,354,314,355,315,354,320,354,321,353,331,353,332,354,335,354,336,355,339,355,340,356,379,356,380,357,406,357,407,356,409,356,410,357,474,357,475,356,482,356,484,357,485,356,488,356,493,353,501,354,502,353,506,353,507,352,517,352,518,351,522,351,525,347,527,346,530,347,530,349,533,351,530,355,528,355,527,356,515,356,509,359,508,361,505,362,503,365,497,368,494,372,490,373,489,374,492,376,495,376,493,377,488,378,490,380,495,380,497,381,497,381,487,382,485,385,476,387,469,392,466,392,465,393,460,393,456,396,453,397,451,399,443,400', '519,353,518,352,517,353,518,354']), (946711423, 2106233860, 631, 53, 85, 75, 182, 0.73015845, 1947740387, ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,56,124,56,121,54,119,54,110,56,108,56,103,59,100,60,101,61,100,61,96,63,93,63,89,66,84,65,83,65,80,66,78,68,78,68,79,70,80,70,83,74,87,74,90,75,91,75,100,77,102,77,105,75,106,75,125,76,126,77,125,77,125,77,128,76,129,77,131,77,136,75,139,75,143,78,145,76,146,71,146', '61,107,60,106,59,107,60,108', '77,134,76,131,75,134,76,135']), (946711423, 2096875717, 631, 477, 510, 220, 243, 0.69028217, 1947740388, ['501,241,493,241,489,239,488,237,487,237,480,232,479,230,479,226,484,222,487,222,488,223,492,224,496,228,497,228,497,229,502,234,502,235,504,236,504,240,502,240']), (946711423, 2096875712, 631, 309, 326, 382, 404, 0.6633776, 1947740390, ['309,383,309,382,311,382', '325,385,324,383,319,383,318,382,325,382', '320,400,311,400,309,398,309,385,310,386,311,385,315,385,316,384,318,384,319,385,322,385,325,387,325,398,323,398']), (946711423, 2096875719, 631, 427, 553, 258, 315, 0.6446218, 1947740391, ['531,284,526,284,525,283,525,281,523,279,522,280,519,281,521,282,521,283,519,284,518,283,519,281,515,279,516,277,515,276,513,276,512,277,513,279,511,280,507,279,505,276,504,279,497,279,496,278,495,279,485,279,484,280,480,280,479,281,481,283,482,283,482,283,469,283,468,284,438,284,436,283,440,279,446,279,447,278,456,278,458,276,457,275,457,275,467,275,468,274,490,274,491,273,494,273,496,271,496,269,499,268,501,266,503,265,506,265,510,263,514,263,516,261,520,259,544,259,544,259,543,260,545,262,548,262,550,263,550,276,549,277,547,277,540,282,538,282,537,283,532,283']), (946711423, 2106233861, 631, 144, 267, 181, 307, 0.63958377, 1947740392, ['212,251,209,251,208,250,203,251,201,250,201,249,195,243,189,242,188,241,185,241,184,240,182,236,180,236,179,235,173,235,172,234,170,235,164,235,163,234,163,232,162,231,163,217,166,217,168,218,170,215,171,210,172,209,173,209,176,212,178,210,178,208,181,203,186,203,188,201,193,201,194,202,195,201,201,201,202,202,204,202,205,201,209,201,210,202,212,202,215,200,217,200,220,202,221,201,227,201,231,205,231,206,234,209,235,209,235,210,238,213,238,224,234,228,235,232,234,233,228,234,225,237,224,241,222,242,216,242,209,246,211,248,212,248,213,250', '221,228,220,227,219,228,220,229', '224,238,224,237,221,235,217,237,219,239']), (946711423, 2096875712, 631, 285, 433, 343, 377, 0.61493844, 1947740393, ['431,376,286,376,285,375,285,368,286,367,286,362,287,361,287,359,291,359,297,362,306,363,307,364,312,364,313,365,322,366,323,367,331,366,332,368,334,368,335,369,338,368,337,366,336,366,337,365,336,364,327,364,325,361,319,361,317,357,317,356,318,355,325,355,326,353,331,353,333,351,332,350,330,350,328,348,326,348,325,347,319,347,315,345,306,345,305,344,297,344,299,344,300,343,431,343,432,344,432,353,431,354,431,358,430,359,430,365,427,365,425,363,424,363,422,364,421,366,418,366,413,369,404,370,409,371,409,371,399,371,398,372,395,373,419,374,420,373,426,372,428,370,428,367,429,367,430,368,429,369,430,370,430,373,431,374', '381,373,378,372,377,371,356,371,356,371,359,370,347,369,345,367,343,367,342,368,343,369,341,370,354,371,354,371,352,372,353,373,359,373,360,374']), (946711423, 2106233860, 631, 146, 287, 140, 311, 0.54784286, 1947740394, ['234,254,227,254,221,251,219,248,215,253,212,253,210,252,206,247,203,247,198,243,197,243,194,239,189,238,186,236,182,236,181,235,167,235,164,233,164,228,159,227,158,226,158,219,159,218,159,213,162,207,162,205,169,192,169,186,170,185,172,185,177,179,175,175,173,173,177,171,181,171,182,170,184,170,187,167,187,164,188,163,188,161,199,161,202,164,205,165,207,167,209,167,212,165,215,165,216,168,218,170,219,170,221,168,221,164,220,163,220,161,222,161,223,160,230,160,231,159,242,159,244,158,247,161,248,161,247,162,246,168,248,172,248,174,253,176,254,180,253,182,249,182,247,185,249,188,253,188,254,189,254,194,249,194,247,196,247,198,249,200,252,200,253,199,255,202,255,205,254,206,254,208,250,207,249,206,246,209,246,210,249,214,252,212,254,212,254,214,255,215,255,217,254,218,254,221,252,221,249,219,247,221,247,225,249,228,250,228,252,226,253,224,253,224,253,229,252,229,251,228,249,228,247,230,247,233,246,234,246,237,245,238,245,240,243,244,243,247,239,251,237,251', '230,167,229,166,227,167,228,168']), (946711423, 495920967, 631, 202, 524, 112, 333, 0.45109355, 1947740396, ['483,289,483,286,482,285,482,283,480,279,480,274,476,270,472,268,465,268,464,269,459,269,458,268,454,268,453,267,437,267,436,268,428,268,427,269,418,269,417,270,414,270,410,266,410,265,416,262,418,262,421,260,423,260,425,259,426,257,424,255,422,255,419,253,417,253,416,252,412,251,410,250,410,249,412,249,413,248,415,248,416,247,422,246,428,243,429,242,428,241,424,240,423,239,420,239,419,238,390,238,389,237,386,237,385,236,369,236,368,235,363,234,363,233,364,232,364,230,366,226,365,225,357,220,344,220,341,218,339,218,339,218,342,212,342,210,336,207,327,207,326,206,319,206,318,205,314,205,313,204,297,204,291,207,288,210,288,212,291,217,290,220,288,222,284,224,282,224,278,227,273,228,271,230,270,235,265,239,262,236,261,232,263,228,266,226,261,224,256,219,256,210,249,206,242,205,237,202,234,195,226,186,227,184,227,180,228,179,225,175,225,174,222,171,225,165,227,163,229,158,230,157,232,156,235,156,236,155,239,155,240,154,245,154,246,155,254,155,255,156,258,156,259,157,268,157,269,156,272,156,273,155,280,155,281,156,298,156,300,155,301,156,307,156,308,157,311,157,318,152,322,151,323,150,333,150,338,146,339,146,342,143,343,143,346,140,357,140,362,136,366,134,368,134,369,133,373,133,374,132,377,132,378,131,388,131,389,130,410,130,411,131,417,131,428,140,432,142,434,142,435,143,443,145,446,147,448,147,451,154,453,156,457,158,462,159,463,160,466,160,467,161,472,162,474,163,474,164,481,171,489,175,491,175,492,176,494,176,495,177,499,178,500,179,502,184,507,189,517,194,518,195,514,201,514,203,518,207,519,209,518,214,515,218,517,227,515,229,515,231,514,232,515,236,518,239,518,252,519,253,519,263,518,264,518,267,517,269,514,272,512,273,512,274,506,280,500,277,498,273,496,272,493,272,491,274,491,278,490,279,490,281', '312,179,311,178,308,179,309,180', '268,269,264,269,259,266,259,262,261,258,261,250,265,245,269,250,270,257,274,260,278,265,275,267,269,268', '414,281,401,281,414,281']), (946711423, 2096875722, 631, 433, 558, 248, 286, 0.44133398, 1947740397, ['492,272,474,272,473,271,468,271,465,269,460,269,460,268,465,266,467,266,468,265,470,265,471,264,475,264,476,263,479,263,480,262,486,262,487,261,491,261,492,260,495,260,496,259,502,259,506,257,510,257,514,255,517,255,518,254,530,253,531,252,535,252,536,251,538,251,539,252,543,252,544,253,547,253,549,251,553,251,555,253,555,267,552,270,550,270,550,269,548,267,547,267,547,267,548,266,547,265,545,266,540,266,539,264,530,264,529,263,524,263,519,266,513,266,510,268,507,268,506,269,499,270,498,271,493,271', '438,279,435,279,435,273,436,272,448,271,449,272,448,274,443,274,440,277,440,278']), (946711423, 492654799, 631, 399, 569, 68, 251, 0.41876298, 1947740399, []), (946711423, 492624020, 631, 420, 552, 244, 293, 0.35962066, 1947740400, ['474,289,453,289,452,288,439,288,437,286,431,286,427,284,423,284,422,283,422,275,427,275,428,273,430,272,435,272,436,271,438,271,442,269,447,269,450,267,454,267,460,264,464,264,467,262,483,261,484,260,488,260,489,259,494,259,495,258,502,258,503,257,505,257,509,255,512,255,516,252,520,252,521,251,526,250,530,248,534,248,535,247,546,247,547,248,549,248,549,250,550,251,550,266,551,267,551,275,550,276,550,278,549,279,549,281,537,282,535,284,528,284,527,285,504,285,503,286,495,286,492,288,488,287,487,288,475,288']), (946711423, 503548896, 631, 301, 540, 339, 403, 0.740756, 3140491551, ['442,401,371,401,371,397,366,390,365,386,356,386,353,384,348,383,319,383,319,378,314,370,310,370,305,368,304,357,305,353,330,353,339,356,378,356,379,357,474,357,475,356,488,356,493,353,501,354,507,352,517,352,522,351,527,346,530,347,533,351,530,355,527,356,515,356,505,362,503,365,497,368,494,372,489,374,492,376,488,378,490,380,495,380,487,382,485,385,476,387,469,392,461,393,456,395,451,399,447,399', '519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] origin we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.004128217697143555 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Sat Mar 8 11:33:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter with classification results param_json : {'input_type': 631, 'output_type': 816, 'condition_type': 872, 'crops_ok': {'CAR_DOCUMENT.*': {}, 'CAR_INTERIEUR.*': {}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'Retroviseur': 2, 'Roue': 2, 'Capot': 1, 'Pare-brise': 1, 'vitre': 10, 'phare': 2, 'Feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'Plaque-immatriculation': 1, 'Essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1}}, 'separation': {'CAR_EXTERIEUR_avant.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-gauche', 'a-droite-de', 'phare-droit']}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-droite', 'a-gauche-de', 'phare-gauche'], 'porte': ['porte-avant', 'a-droite-de', 'porte-arriere']}}} conditional_crop_by_classif_copy batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ treating photo 946711423 batch 1 Loaded 0 chid ids of type : 0 batch 1 Loaded 23 chid ids of type : 816 Number RLEs to save : 1600 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 4.769570827484131 time spend to save output : 0.00016117095947265625 total time spend for step 1 : 4.7697319984436035 caffe_path_current : About to save ! 0 After save, about to update current ! test detection filter by classif is a success ! ############################### TEST blur_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : blur_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.9096417427062988 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:blur_detection Sat Mar 8 11:33:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blur_detection methode: ratio et variance treat image : temp/1741429993_4154994_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.5985627174377441 time spend to save output : 5.173683166503906e-05 total time spend for step 1 : 0.5986144542694092 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {930729675: [(930729675, 12.961859636534896, 492688767)]} {930729675: [(930729675, 12.961859636534896, 492688767)]} ############################### TEST detect_point_224x224 ################################ test_detect_point_224x224 Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 4589 thcl is not linked in the step_by_step architecture ! WARNING : step 4590 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFBFFBFFBFBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 2.0826170444488525 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Sat Mar 8 11:33:16 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 1528 time to import caffe and check if the image exist : 0.0002655982971191406 time to convert the images to numpy array : 0.030565500259399414 time to import caffe and check if the image exist : 0.021039962768554688 time to convert the images to numpy array : 0.11333942413330078 time to import caffe and check if the image exist : 0.020160913467407227 time to convert the images to numpy array : 0.11091208457946777 time to import caffe and check if the image exist : 0.04613447189331055 time to convert the images to numpy array : 0.08273553848266602 time to import caffe and check if the image exist : 0.0170590877532959 time to convert the images to numpy array : 0.12554669380187988 time to import caffe and check if the image exist : 0.05280423164367676 time to convert the images to numpy array : 0.08849620819091797 time to import caffe and check if the image exist : 0.046262264251708984 time to convert the images to numpy array : 0.10291409492492676 time to import caffe and check if the image exist : 0.08109235763549805 time to convert the images to numpy array : 0.06243562698364258 time to import caffe and check if the image exist : 0.037329673767089844 time to convert the images to numpy array : 0.11136698722839355 time to import caffe and check if the image exist : 0.04297947883605957 time to convert the images to numpy array : 0.20854854583740234 total time to convert the images to numpy array : 0.253218412399292 list photo_ids error: [] list photo_ids correct : [987515187, 987515246, 987515247, 987515248, 987515249, 987515250, 987515188, 987515189, 987515236, 987515237, 987515238, 987515207, 987515208, 987515209, 987515211, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515222, 987515223, 987515175, 987515176, 987515177, 987515178, 987515179, 987515201, 987515202, 987515204, 987515205, 987515224, 987515226, 987515227, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245] number of photos to traite : 64 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 61 wait 20 seconds l 3637 free memory gpu now : 61 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2500 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'res5b']) time used to do the prepocess of the images : 0.08738470077514648 time used to do the prediction : 0.5254087448120117 save descriptor for thcl : 1528 time to traite the descriptors : 5.464843988418579 storage_type for insertDescriptorsMulti : 1 To insert : 987515187 To insert : 987515246 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515188 To insert : 987515189 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515222 To insert : 987515223 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515239 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 time to insert the descriptors : 18.87695837020874 time spend for datou_step_exec : 50.137749671936035 time spend to save output : 5.1021575927734375e-05 total time spend for step 1 : 50.13780069351196 step2:argmax Sat Mar 8 11:34:06 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 time spend for datou_step_exec : 0.0021224021911621094 time spend to save output : 8.20159912109375e-05 total time spend for step 2 : 0.002204418182373047 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515187': [('987515187', 'Carton', 0.9810144, 1927, '1528'), 'temp/1741429994_4154994_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515246': [('987515246', 'Carton', 0.9992322, 1927, '1528'), 'temp/1741429994_4154994_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966824, 1927, '1528'), 'temp/1741429994_4154994_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.98130864, 1927, '1528'), 'temp/1741429994_4154994_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.98129797, 1927, '1528'), 'temp/1741429994_4154994_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.98079234, 1927, '1528'), 'temp/1741429994_4154994_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515188': [('987515188', 'Carton', 0.9956514, 1927, '1528'), 'temp/1741429994_4154994_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.99778986, 1927, '1528'), 'temp/1741429994_4154994_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5376788, 1927, '1528'), 'temp/1741429994_4154994_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7700721, 1927, '1528'), 'temp/1741429994_4154994_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.999574, 1927, '1528'), 'temp/1741429994_4154994_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8738858, 1927, '1528'), 'temp/1741429994_4154994_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.9917241, 1927, '1528'), 'temp/1741429994_4154994_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9678697, 1927, '1528'), 'temp/1741429994_4154994_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.97345495, 1927, '1528'), 'temp/1741429994_4154994_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515180': [('987515180', 'Carton', 0.9900096, 1927, '1528'), 'temp/1741429994_4154994_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977811, 1927, '1528'), 'temp/1741429994_4154994_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.992417, 1927, '1528'), 'temp/1741429994_4154994_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1741429994_4154994_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997321, 1927, '1528'), 'temp/1741429994_4154994_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7979514, 1927, '1528'), 'temp/1741429994_4154994_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847039, 1927, '1528'), 'temp/1741429994_4154994_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5220325, 1927, '1528'), 'temp/1741429994_4154994_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.9994055, 1927, '1528'), 'temp/1741429994_4154994_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.99942136, 1927, '1528'), 'temp/1741429994_4154994_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924517, 1927, '1528'), 'temp/1741429994_4154994_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.98345745, 1927, '1528'), 'temp/1741429994_4154994_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.9446919, 1927, '1528'), 'temp/1741429994_4154994_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89197475, 1927, '1528'), 'temp/1741429994_4154994_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515212': [('987515212', 'Carton', 0.9869213, 1927, '1528'), 'temp/1741429994_4154994_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.9869142, 1927, '1528'), 'temp/1741429994_4154994_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939143, 1927, '1528'), 'temp/1741429994_4154994_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774524, 1927, '1528'), 'temp/1741429994_4154994_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5290114, 1927, '1528'), 'temp/1741429994_4154994_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936944, 1927, '1528'), 'temp/1741429994_4154994_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963804, 1927, '1528'), 'temp/1741429994_4154994_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515190': [('987515190', 'Carton', 0.97624606, 1927, '1528'), 'temp/1741429994_4154994_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999114, 1927, '1528'), 'temp/1741429994_4154994_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993967, 1927, '1528'), 'temp/1741429994_4154994_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846635, 1927, '1528'), 'temp/1741429994_4154994_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.9846847, 1927, '1528'), 'temp/1741429994_4154994_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.96621656, 1927, '1528'), 'temp/1741429994_4154994_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.9860103, 1927, '1528'), 'temp/1741429994_4154994_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515222': [('987515222', 'Carton', 0.9974759, 1927, '1528'), 'temp/1741429994_4154994_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920844, 1927, '1528'), 'temp/1741429994_4154994_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.999814, 1927, '1528'), 'temp/1741429994_4154994_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.999813, 1927, '1528'), 'temp/1741429994_4154994_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97714555, 1927, '1528'), 'temp/1741429994_4154994_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8577944, 1927, '1528'), 'temp/1741429994_4154994_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.92698634, 1927, '1528'), 'temp/1741429994_4154994_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515201': [('987515201', 'Carton', 0.9954614, 1927, '1528'), 'temp/1741429994_4154994_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.99111533, 1927, '1528'), 'temp/1741429994_4154994_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950787, 1927, '1528'), 'temp/1741429994_4154994_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99085474, 1927, '1528'), 'temp/1741429994_4154994_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.90836835, 1927, '1528'), 'temp/1741429994_4154994_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.98698366, 1927, '1528'), 'temp/1741429994_4154994_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9002397, 1927, '1528'), 'temp/1741429994_4154994_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1741429994_4154994_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.99952054, 1927, '1528'), 'temp/1741429994_4154994_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.98211783, 1927, '1528'), 'temp/1741429994_4154994_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.935906, 1927, '1528'), 'temp/1741429994_4154994_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8741464, 1927, '1528'), 'temp/1741429994_4154994_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.8175622, 1927, '1528'), 'temp/1741429994_4154994_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8658935, 1927, '1528'), 'temp/1741429994_4154994_987515245_757d9d208d5bd4375c5f21f68b699148.jpg']} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detect_points list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.10356330871582031 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detect_points Sat Mar 8 11:34:06 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : 1 To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 1 Inside predict_points step exec : nb paths : 1 treate image : temp/1741430046_4154994_987515173_91fa471b1a04f95b356afdbaf021f623.jpg size of numpy array img : 2408584 scale method : caffe/skimage size of numpy array img_scale : 2408584 (448, 448, 3) nb_h 8 nb_w 8 size of sub images : (224, 224, 3) size of caffe_input : 38535320 (64, 3, 224, 224) time to do the preprocess : 0.033957481384277344 time to do a prediction : 0.3761603832244873 dict_keys(['prob']) shape of output (64, 10, 1, 1) shape of the out_put heatmap (10, 8, 8) number of sub_photos vertical and horizon 8 8 size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) time spend for datou_step_exec : 2.8624911308288574 time spend to save output : 3.147125244140625e-05 total time spend for step 1 : 2.862522602081299 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.311452662582928e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4364623166239774e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0619353396634779e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.4494569806374784e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.924215212056879e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.7734731449745595e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012279990187380463), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9445349355228245e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.359315054434319e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.208848037810185e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3824967481923522e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4760281601411407e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1270904906268697e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015789356257300824), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004442368517629802), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.537795707117766e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3298572412168141e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.6210768762903172e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5215376808773726e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6154681361513212e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.266816399147501e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.610777149442583e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.00032675257534720004), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003052267129532993), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8551056200521998e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.939120223454665e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7122114261146635e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.7988360923482105e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.3443026293534786e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.6996822523651645e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.532731509243604e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.797489499556832e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.100985501689138e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.645676206862845e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.9639737729448825e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4370144754138892e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.856068805267569e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2846929166698828e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.29545331018744e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.168477476516273e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.828363787761191e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.54905307883746e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.958117192974896e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.75691672868561e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.326668658904964e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.158617674780544e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.1521858141350094e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.941112299798988e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2083270121365786e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.5635998352081515e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.3487085602246225e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.00015428384358529001), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.00026036310009658337), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.00018893455853685737), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.00021537230350077152), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.00016463168140035123), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.5522788241214585e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.733652788971085e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.926212568534538e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.0001228630426339805), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.00019642498227767646), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.0001887495454866439), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.00012353502097539604), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.00027201740886084735), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.588048093026373e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.0380559767072555e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 6.9899433583486825e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.000872062926646322), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.002649021800607443), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.00338024296797812), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.03137916699051857), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.055752094835042953), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.00012349055032245815), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020854879403486848), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.0003685440169647336), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.006842430680990219), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.01588341034948826), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.009402014315128326), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.00978046003729105), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.022121703252196312), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.02184414304792881), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.1932147890329361), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.09641356021165848), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.1237376406788826), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.5328954458236694), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.46018165349960327), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.771140992641449), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8663477897644043), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8498831987380981), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.98431396484375), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9847954511642456), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919524788856506), (987515173, 1982, 'Carton', 240, -1, 208, -1, 0.9993791580200195), (987515173, 1982, 'Carton', 272, -1, 208, -1, 0.9994125366210938), (987515173, 1982, 'Carton', 304, -1, 208, -1, 0.9995891451835632), (987515173, 1982, 'Carton', 336, -1, 208, -1, 0.9992263317108154), (987515173, 1982, 'Carton', 112, -1, 240, -1, 0.9274692535400391), (987515173, 1982, 'Carton', 144, -1, 240, -1, 0.9810022115707397), (987515173, 1982, 'Carton', 176, -1, 240, -1, 0.9660314917564392), (987515173, 1982, 'Carton', 208, -1, 240, -1, 0.967751681804657), (987515173, 1982, 'Carton', 240, -1, 240, -1, 0.996393620967865), (987515173, 1982, 'Carton', 272, -1, 240, -1, 0.9994219541549683), (987515173, 1982, 'Carton', 304, -1, 240, -1, 0.9997864365577698), (987515173, 1982, 'Carton', 336, -1, 240, -1, 0.999669075012207), (987515173, 1982, 'Carton', 112, -1, 272, -1, 0.9895098209381104), (987515173, 1982, 'Carton', 144, -1, 272, -1, 0.9954743981361389), (987515173, 1982, 'Carton', 176, -1, 272, -1, 0.9854915738105774), (987515173, 1982, 'Carton', 208, -1, 272, -1, 0.9734681844711304), (987515173, 1982, 'Carton', 240, -1, 272, -1, 0.9974761605262756), (987515173, 1982, 'Carton', 272, -1, 272, -1, 0.9992037415504456), (987515173, 1982, 'Carton', 304, -1, 272, -1, 0.999516487121582), (987515173, 1982, 'Carton', 336, -1, 272, -1, 0.9991257786750793), (987515173, 1982, 'Carton', 112, -1, 304, -1, 0.997771680355072), (987515173, 1982, 'Carton', 144, -1, 304, -1, 0.9977580904960632), (987515173, 1982, 'Carton', 176, -1, 304, -1, 0.9955573678016663), (987515173, 1982, 'Carton', 208, -1, 304, -1, 0.9927297234535217), (987515173, 1982, 'Carton', 240, -1, 304, -1, 0.9920361638069153), (987515173, 1982, 'Carton', 272, -1, 304, -1, 0.9836013913154602), (987515173, 1982, 'Carton', 304, -1, 304, -1, 0.9819754958152771), (987515173, 1982, 'Carton', 336, -1, 304, -1, 0.9808628559112549), (987515173, 1982, 'Carton', 112, -1, 336, -1, 0.9924349188804626), (987515173, 1982, 'Carton', 144, -1, 336, -1, 0.9762681126594543), (987515173, 1982, 'Carton', 176, -1, 336, -1, 0.9911606907844543), (987515173, 1982, 'Carton', 208, -1, 336, -1, 0.9869688153266907), (987515173, 1982, 'Carton', 240, -1, 336, -1, 0.9086806178092957), (987515173, 1982, 'Carton', 272, -1, 336, -1, 0.9451309442520142), (987515173, 1982, 'Carton', 304, -1, 336, -1, 0.9365527033805847), (987515173, 1982, 'Carton', 336, -1, 336, -1, 0.9808965921401978), (987515173, 1982, 'Kraft', 112, -1, 112, -1, 1.963537554772188e-09), (987515173, 1982, 'Kraft', 144, -1, 112, -1, 1.6948972003660856e-08), (987515173, 1982, 'Kraft', 176, -1, 112, -1, 9.646705620980356e-07), (987515173, 1982, 'Kraft', 208, -1, 112, -1, 3.1427978683495894e-05), (987515173, 1982, 'Kraft', 240, -1, 112, -1, 4.4469208660302684e-05), (987515173, 1982, 'Kraft', 272, -1, 112, -1, 0.00020619004499167204), (987515173, 1982, 'Kraft', 304, -1, 112, -1, 0.0010821860050782561), (987515173, 1982, 'Kraft', 336, -1, 112, -1, 0.0008280138717964292), (987515173, 1982, 'Kraft', 112, -1, 144, -1, 2.614026925584767e-05), (987515173, 1982, 'Kraft', 144, -1, 144, -1, 6.9788807195436675e-06), (987515173, 1982, 'Kraft', 176, -1, 144, -1, 3.62822288479947e-06), (987515173, 1982, 'Kraft', 208, -1, 144, -1, 3.5642198781715706e-05), (987515173, 1982, 'Kraft', 240, -1, 144, -1, 6.710173329338431e-05), (987515173, 1982, 'Kraft', 272, -1, 144, -1, 8.674572745803744e-05), (987515173, 1982, 'Kraft', 304, -1, 144, -1, 0.00012222684745211154), (987515173, 1982, 'Kraft', 336, -1, 144, -1, 0.000112353045551572), (987515173, 1982, 'Kraft', 112, -1, 176, -1, 0.0004972500028088689), (987515173, 1982, 'Kraft', 144, -1, 176, -1, 0.0001231156347785145), (987515173, 1982, 'Kraft', 176, -1, 176, -1, 9.072991088032722e-05), (987515173, 1982, 'Kraft', 208, -1, 176, -1, 5.177967977942899e-05), (987515173, 1982, 'Kraft', 240, -1, 176, -1, 0.00011565832392079756), (987515173, 1982, 'Kraft', 272, -1, 176, -1, 0.000435527297668159), (987515173, 1982, 'Kraft', 304, -1, 176, -1, 0.0009218435734510422), (987515173, 1982, 'Kraft', 336, -1, 176, -1, 0.0014276128495112062), (987515173, 1982, 'Kraft', 112, -1, 208, -1, 6.881912850076333e-05), (987515173, 1982, 'Kraft', 144, -1, 208, -1, 1.847152816480957e-05), (987515173, 1982, 'Kraft', 176, -1, 208, -1, 2.5960442144423723e-05), (987515173, 1982, 'Kraft', 208, -1, 208, -1, 3.544113496900536e-05), (987515173, 1982, 'Kraft', 240, -1, 208, -1, 3.741686305147596e-05), (987515173, 1982, 'Kraft', 272, -1, 208, -1, 8.651857933728024e-05), (987515173, 1982, 'Kraft', 304, -1, 208, -1, 0.00012332217011135072), (987515173, 1982, 'Kraft', 336, -1, 208, -1, 0.00039113452658057213), (987515173, 1982, 'Kraft', 112, -1, 240, -1, 0.0003078926238231361), (987515173, 1982, 'Kraft', 144, -1, 240, -1, 4.1650182538433e-05), (987515173, 1982, 'Kraft', 176, -1, 240, -1, 1.2273818356334232e-05), (987515173, 1982, 'Kraft', 208, -1, 240, -1, 7.3566261562518775e-06), (987515173, 1982, 'Kraft', 240, -1, 240, -1, 2.2960999558563344e-05), (987515173, 1982, 'Kraft', 272, -1, 240, -1, 5.813991083414294e-05), (987515173, 1982, 'Kraft', 304, -1, 240, -1, 6.550296529894695e-05), (987515173, 1982, 'Kraft', 336, -1, 240, -1, 0.0001869113475549966), (987515173, 1982, 'Kraft', 112, -1, 272, -1, 0.0014607906341552734), (987515173, 1982, 'Kraft', 144, -1, 272, -1, 0.000690087559632957), (987515173, 1982, 'Kraft', 176, -1, 272, -1, 0.00027351410244591534), (987515173, 1982, 'Kraft', 208, -1, 272, -1, 4.3601005017990246e-05), (987515173, 1982, 'Kraft', 240, -1, 272, -1, 3.368101897649467e-05), (987515173, 1982, 'Kraft', 272, -1, 272, -1, 8.341232751263306e-05), (987515173, 1982, 'Kraft', 304, -1, 272, -1, 0.00011204380280105397), (987515173, 1982, 'Kraft', 336, -1, 272, -1, 0.00042515984387136996), (987515173, 1982, 'Kraft', 112, -1, 304, -1, 0.0009936627466231585), (987515173, 1982, 'Kraft', 144, -1, 304, -1, 0.0008983864681795239), (987515173, 1982, 'Kraft', 176, -1, 304, -1, 0.0006185025558806956), (987515173, 1982, 'Kraft', 208, -1, 304, -1, 0.0010818518931046128), (987515173, 1982, 'Kraft', 240, -1, 304, -1, 0.0017911012982949615), (987515173, 1982, 'Kraft', 272, -1, 304, -1, 0.0046957810409367085), (987515173, 1982, 'Kraft', 304, -1, 304, -1, 0.004705911502242088), (987515173, 1982, 'Kraft', 336, -1, 304, -1, 0.01252014096826315), (987515173, 1982, 'Kraft', 112, -1, 336, -1, 0.0021777355577796698), (987515173, 1982, 'Kraft', 144, -1, 336, -1, 0.005690750200301409), (987515173, 1982, 'Kraft', 176, -1, 336, -1, 0.0008304791408590972), (987515173, 1982, 'Kraft', 208, -1, 336, -1, 0.0012617255561053753), (987515173, 1982, 'Kraft', 240, -1, 336, -1, 0.0077849640510976315), (987515173, 1982, 'Kraft', 272, -1, 336, -1, 0.012587265111505985), (987515173, 1982, 'Kraft', 304, -1, 336, -1, 0.017925787717103958), (987515173, 1982, 'Kraft', 336, -1, 336, -1, 0.007731788791716099), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 112, -1, 1.5026267730089415e-10), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 112, -1, 8.20859735739532e-09), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 112, -1, 5.499930466612568e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 112, -1, 5.519157639355399e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 112, -1, 8.247114237747155e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 112, -1, 3.5594537621364e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 112, -1, 8.156188414432108e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 112, -1, 4.216713205096312e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 144, -1, 3.622791950874671e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 144, -1, 1.3077349194645649e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 144, -1, 2.474487928338931e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 144, -1, 3.4621409668034175e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 144, -1, 5.713036898669088e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 144, -1, 2.0593857698258944e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 144, -1, 3.9010574255371466e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 144, -1, 1.3678216419066302e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 176, -1, 3.891645690146106e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 176, -1, 2.5532469862810103e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 176, -1, 1.201919440063648e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 176, -1, 7.816622201062273e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 176, -1, 6.363046395563288e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 176, -1, 2.641357059474103e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 176, -1, 3.9177397411549464e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 176, -1, 1.847095336415805e-05), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 208, -1, 1.7978043160837842e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 208, -1, 5.702423777620425e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 208, -1, 2.1972359718347434e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 208, -1, 1.7347733773931395e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 208, -1, 3.994065309598227e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 208, -1, 2.0305473924508988e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 208, -1, 9.83420420652692e-08), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 208, -1, 8.676183682609917e-08), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 240, -1, 1.8237388985653524e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 240, -1, 6.326725383587473e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 240, -1, 7.836115401005372e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 240, -1, 6.407729529200878e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 240, -1, 7.702116704422224e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 240, -1, 5.17482931172708e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 240, -1, 1.7717866285238415e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 240, -1, 1.4434307615829312e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 272, -1, 4.1850708498714084e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 272, -1, 3.165851580888557e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 272, -1, 5.09092330958083e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 272, -1, 7.332783980018576e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 272, -1, 4.0274971979670227e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 272, -1, 3.2146911621566687e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 272, -1, 2.1020245810632332e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 272, -1, 3.4937869486384443e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 304, -1, 3.300958724139491e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 304, -1, 3.2596270216345147e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 304, -1, 8.366387760361249e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 304, -1, 3.298137698948267e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 304, -1, 4.427502062753774e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 304, -1, 5.037025402998552e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 304, -1, 8.235601853812113e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 304, -1, 5.56572740606498e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 336, -1, 4.1912545611921814e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 336, -1, 8.198740601983445e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 176, -1, 336, -1, 7.15200826562068e-07), (987515173, 1982, 'Lointain_Papier_Magazine', 208, -1, 336, -1, 1.9586534563131863e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 240, -1, 336, -1, 3.908966846211115e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 272, -1, 336, -1, 2.461061058056657e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 304, -1, 336, -1, 3.4359718483756296e-06), (987515173, 1982, 'Lointain_Papier_Magazine', 336, -1, 336, -1, 4.8458041419507936e-06), (987515173, 1982, 'Metal', 112, -1, 112, -1, 7.452999328805276e-11), (987515173, 1982, 'Metal', 144, -1, 112, -1, 1.9203787449129095e-09), (987515173, 1982, 'Metal', 176, -1, 112, -1, 7.29597900317458e-07), (987515173, 1982, 'Metal', 208, -1, 112, -1, 9.473810678173322e-06), (987515173, 1982, 'Metal', 240, -1, 112, -1, 2.660163227119483e-05), (987515173, 1982, 'Metal', 272, -1, 112, -1, 0.0001694332022452727), (987515173, 1982, 'Metal', 304, -1, 112, -1, 0.0007548956200480461), (987515173, 1982, 'Metal', 336, -1, 112, -1, 0.00030263018561527133), (987515173, 1982, 'Metal', 112, -1, 144, -1, 5.325392749000457e-07), (987515173, 1982, 'Metal', 144, -1, 144, -1, 4.3718330289266305e-07), (987515173, 1982, 'Metal', 176, -1, 144, -1, 3.1898898669169284e-06), (987515173, 1982, 'Metal', 208, -1, 144, -1, 9.601927558833268e-06), (987515173, 1982, 'Metal', 240, -1, 144, -1, 1.6591238818364218e-05), (987515173, 1982, 'Metal', 272, -1, 144, -1, 7.65492586651817e-05), (987515173, 1982, 'Metal', 304, -1, 144, -1, 0.00013113152817822993), (987515173, 1982, 'Metal', 336, -1, 144, -1, 4.668191832024604e-05), (987515173, 1982, 'Metal', 112, -1, 176, -1, 5.881561719434103e-06), (987515173, 1982, 'Metal', 144, -1, 176, -1, 4.022629127575783e-06), (987515173, 1982, 'Metal', 176, -1, 176, -1, 1.0135973752767313e-05), (987515173, 1982, 'Metal', 208, -1, 176, -1, 4.157212970312685e-06), (987515173, 1982, 'Metal', 240, -1, 176, -1, 6.6637744566833135e-06), (987515173, 1982, 'Metal', 272, -1, 176, -1, 5.453106859931722e-05), (987515173, 1982, 'Metal', 304, -1, 176, -1, 0.00012889668869320303), (987515173, 1982, 'Metal', 336, -1, 176, -1, 6.56148768030107e-05), (987515173, 1982, 'Metal', 112, -1, 208, -1, 8.133810297294986e-06), (987515173, 1982, 'Metal', 144, -1, 208, -1, 2.2585120404983172e-06), (987515173, 1982, 'Metal', 176, -1, 208, -1, 6.635597401327686e-06), (987515173, 1982, 'Metal', 208, -1, 208, -1, 4.597159204422496e-06), (987515173, 1982, 'Metal', 240, -1, 208, -1, 1.7141743455795222e-06), (987515173, 1982, 'Metal', 272, -1, 208, -1, 1.3710451867154916e-06), (987515173, 1982, 'Metal', 304, -1, 208, -1, 5.768836217612261e-07), (987515173, 1982, 'Metal', 336, -1, 208, -1, 8.339735018125793e-07), (987515173, 1982, 'Metal', 112, -1, 240, -1, 2.928820777015062e-06), (987515173, 1982, 'Metal', 144, -1, 240, -1, 7.383653155557113e-07), (987515173, 1982, 'Metal', 176, -1, 240, -1, 6.092026865189837e-07), (987515173, 1982, 'Metal', 208, -1, 240, -1, 6.244954988687823e-07), (987515173, 1982, 'Metal', 240, -1, 240, -1, 1.1967158570769243e-06), (987515173, 1982, 'Metal', 272, -1, 240, -1, 6.95241965331661e-07), (987515173, 1982, 'Metal', 304, -1, 240, -1, 3.1426085911334667e-07), (987515173, 1982, 'Metal', 336, -1, 240, -1, 4.11685618928459e-07), (987515173, 1982, 'Metal', 112, -1, 272, -1, 2.6211232579953503e-06), (987515173, 1982, 'Metal', 144, -1, 272, -1, 8.047377377806697e-07), (987515173, 1982, 'Metal', 176, -1, 272, -1, 8.241214004556241e-07), (987515173, 1982, 'Metal', 208, -1, 272, -1, 4.009949350347597e-07), (987515173, 1982, 'Metal', 240, -1, 272, -1, 2.7507451250130543e-07), (987515173, 1982, 'Metal', 272, -1, 272, -1, 3.3054948289645836e-07), (987515173, 1982, 'Metal', 304, -1, 272, -1, 3.4394142289784213e-07), (987515173, 1982, 'Metal', 336, -1, 272, -1, 1.4815389022260206e-06), (987515173, 1982, 'Metal', 112, -1, 304, -1, 2.31465264732833e-06), (987515173, 1982, 'Metal', 144, -1, 304, -1, 1.961622956514475e-06), (987515173, 1982, 'Metal', 176, -1, 304, -1, 4.52781523563317e-06), (987515173, 1982, 'Metal', 208, -1, 304, -1, 1.334011903963983e-05), (987515173, 1982, 'Metal', 240, -1, 304, -1, 5.66216976949363e-06), (987515173, 1982, 'Metal', 272, -1, 304, -1, 5.689735189662315e-06), (987515173, 1982, 'Metal', 304, -1, 304, -1, 6.394623596861493e-06), (987515173, 1982, 'Metal', 336, -1, 304, -1, 7.3631872510304675e-06), (987515173, 1982, 'Metal', 112, -1, 336, -1, 7.144433311623288e-06), (987515173, 1982, 'Metal', 144, -1, 336, -1, 3.30361544911284e-05), (987515173, 1982, 'Metal', 176, -1, 336, -1, 4.0233695472124964e-05), (987515173, 1982, 'Metal', 208, -1, 336, -1, 7.630951586179435e-05), (987515173, 1982, 'Metal', 240, -1, 336, -1, 0.00012130672257626429), (987515173, 1982, 'Metal', 272, -1, 336, -1, 3.7260826502460986e-05), (987515173, 1982, 'Metal', 304, -1, 336, -1, 2.9097533115418628e-05), (987515173, 1982, 'Metal', 336, -1, 336, -1, 2.8053667847416364e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 112, -1, 0.999995231628418), (987515173, 1982, 'Papier_Magazine', 144, -1, 112, -1, 0.9999926090240479), (987515173, 1982, 'Papier_Magazine', 176, -1, 112, -1, 0.9999088048934937), (987515173, 1982, 'Papier_Magazine', 208, -1, 112, -1, 0.9949555993080139), (987515173, 1982, 'Papier_Magazine', 240, -1, 112, -1, 0.993760883808136), (987515173, 1982, 'Papier_Magazine', 272, -1, 112, -1, 0.9866660237312317), (987515173, 1982, 'Papier_Magazine', 304, -1, 112, -1, 0.8921563625335693), (987515173, 1982, 'Papier_Magazine', 336, -1, 112, -1, 0.8723288178443909), (987515173, 1982, 'Papier_Magazine', 112, -1, 144, -1, 0.9998149275779724), (987515173, 1982, 'Papier_Magazine', 144, -1, 144, -1, 0.9997430443763733), (987515173, 1982, 'Papier_Magazine', 176, -1, 144, -1, 0.9994128942489624), (987515173, 1982, 'Papier_Magazine', 208, -1, 144, -1, 0.9911892414093018), (987515173, 1982, 'Papier_Magazine', 240, -1, 144, -1, 0.9781175255775452), (987515173, 1982, 'Papier_Magazine', 272, -1, 144, -1, 0.9000726938247681), (987515173, 1982, 'Papier_Magazine', 304, -1, 144, -1, 0.535672128200531), (987515173, 1982, 'Papier_Magazine', 336, -1, 144, -1, 0.8109166622161865), (987515173, 1982, 'Papier_Magazine', 112, -1, 176, -1, 0.9776164293289185), (987515173, 1982, 'Papier_Magazine', 144, -1, 176, -1, 0.8064233064651489), (987515173, 1982, 'Papier_Magazine', 176, -1, 176, -1, 0.9028670191764832), (987515173, 1982, 'Papier_Magazine', 208, -1, 176, -1, 0.8749717473983765), (987515173, 1982, 'Papier_Magazine', 240, -1, 176, -1, 0.4656515121459961), (987515173, 1982, 'Papier_Magazine', 272, -1, 176, -1, 0.52583247423172), (987515173, 1982, 'Papier_Magazine', 304, -1, 176, -1, 0.16662706434726715), (987515173, 1982, 'Papier_Magazine', 336, -1, 176, -1, 0.1104620099067688), (987515173, 1982, 'Papier_Magazine', 112, -1, 208, -1, 0.14983606338500977), (987515173, 1982, 'Papier_Magazine', 144, -1, 208, -1, 0.015427442267537117), (987515173, 1982, 'Papier_Magazine', 176, -1, 208, -1, 0.014734864234924316), (987515173, 1982, 'Papier_Magazine', 208, -1, 208, -1, 0.007586717139929533), (987515173, 1982, 'Papier_Magazine', 240, -1, 208, -1, 0.0003460709995124489), (987515173, 1982, 'Papier_Magazine', 272, -1, 208, -1, 0.0001708049967419356), (987515173, 1982, 'Papier_Magazine', 304, -1, 208, -1, 5.3639174439013004e-05), (987515173, 1982, 'Papier_Magazine', 336, -1, 208, -1, 6.261250382522121e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 240, -1, 0.07178651541471481), (987515173, 1982, 'Papier_Magazine', 144, -1, 240, -1, 0.018803689628839493), (987515173, 1982, 'Papier_Magazine', 176, -1, 240, -1, 0.03386254608631134), (987515173, 1982, 'Papier_Magazine', 208, -1, 240, -1, 0.03220289200544357), (987515173, 1982, 'Papier_Magazine', 240, -1, 240, -1, 0.0034896256402134895), (987515173, 1982, 'Papier_Magazine', 272, -1, 240, -1, 0.00034147658152505755), (987515173, 1982, 'Papier_Magazine', 304, -1, 240, -1, 2.967331420222763e-05), (987515173, 1982, 'Papier_Magazine', 336, -1, 240, -1, 1.637022796785459e-05), (987515173, 1982, 'Papier_Magazine', 112, -1, 272, -1, 0.008516430854797363), (987515173, 1982, 'Papier_Magazine', 144, -1, 272, -1, 0.003641001880168915), (987515173, 1982, 'Papier_Magazine', 176, -1, 272, -1, 0.014048256911337376), (987515173, 1982, 'Papier_Magazine', 208, -1, 272, -1, 0.02641293592751026), (987515173, 1982, 'Papier_Magazine', 240, -1, 272, -1, 0.002447801409289241), (987515173, 1982, 'Papier_Magazine', 272, -1, 272, -1, 0.0006448603235185146), (987515173, 1982, 'Papier_Magazine', 304, -1, 272, -1, 0.0002749093691818416), (987515173, 1982, 'Papier_Magazine', 336, -1, 272, -1, 0.00015943736070767045), (987515173, 1982, 'Papier_Magazine', 112, -1, 304, -1, 0.0010050483979284763), (987515173, 1982, 'Papier_Magazine', 144, -1, 304, -1, 0.000993254012428224), (987515173, 1982, 'Papier_Magazine', 176, -1, 304, -1, 0.0031945344526320696), (987515173, 1982, 'Papier_Magazine', 208, -1, 304, -1, 0.005036425311118364), (987515173, 1982, 'Papier_Magazine', 240, -1, 304, -1, 0.0034855769481509924), (987515173, 1982, 'Papier_Magazine', 272, -1, 304, -1, 0.0040479484014213085), (987515173, 1982, 'Papier_Magazine', 304, -1, 304, -1, 0.007203770335763693), (987515173, 1982, 'Papier_Magazine', 336, -1, 304, -1, 0.0019458681344985962), (987515173, 1982, 'Papier_Magazine', 112, -1, 336, -1, 0.0048708380199968815), (987515173, 1982, 'Papier_Magazine', 144, -1, 336, -1, 0.015464402735233307), (987515173, 1982, 'Papier_Magazine', 176, -1, 336, -1, 0.0067983330227434635), (987515173, 1982, 'Papier_Magazine', 208, -1, 336, -1, 0.007960936985909939), (987515173, 1982, 'Papier_Magazine', 240, -1, 336, -1, 0.019048338755965233), (987515173, 1982, 'Papier_Magazine', 272, -1, 336, -1, 0.003654355648905039), (987515173, 1982, 'Papier_Magazine', 304, -1, 336, -1, 0.00631098123267293), (987515173, 1982, 'Papier_Magazine', 336, -1, 336, -1, 0.00443676533177495), (987515173, 1982, 'Plastique', 112, -1, 112, -1, 5.6460365271959745e-08), (987515173, 1982, 'Plastique', 144, -1, 112, -1, 8.149434052029392e-07), (987515173, 1982, 'Plastique', 176, -1, 112, -1, 6.230080907698721e-05), (987515173, 1982, 'Plastique', 208, -1, 112, -1, 0.003547704080119729), (987515173, 1982, 'Plastique', 240, -1, 112, -1, 0.0031660886015743017), (987515173, 1982, 'Plastique', 272, -1, 112, -1, 0.007487444672733545), (987515173, 1982, 'Plastique', 304, -1, 112, -1, 0.05484921857714653), (987515173, 1982, 'Plastique', 336, -1, 112, -1, 0.05954817309975624), (987515173, 1982, 'Plastique', 112, -1, 144, -1, 2.778638190648053e-06), (987515173, 1982, 'Plastique', 144, -1, 144, -1, 1.9085104213445447e-05), (987515173, 1982, 'Plastique', 176, -1, 144, -1, 0.00017654323892202228), (987515173, 1982, 'Plastique', 208, -1, 144, -1, 0.0015028296038508415), (987515173, 1982, 'Plastique', 240, -1, 144, -1, 0.005067582707852125), (987515173, 1982, 'Plastique', 272, -1, 144, -1, 0.08458472788333893), (987515173, 1982, 'Plastique', 304, -1, 144, -1, 0.40984347462654114), (987515173, 1982, 'Plastique', 336, -1, 144, -1, 0.07098563760519028), (987515173, 1982, 'Plastique', 112, -1, 176, -1, 3.926957106159534e-06), (987515173, 1982, 'Plastique', 144, -1, 176, -1, 9.542243060423061e-05), (987515173, 1982, 'Plastique', 176, -1, 176, -1, 0.00033985081245191395), (987515173, 1982, 'Plastique', 208, -1, 176, -1, 0.00038918774225749075), (987515173, 1982, 'Plastique', 240, -1, 176, -1, 0.000624339678324759), (987515173, 1982, 'Plastique', 272, -1, 176, -1, 0.008959591388702393), (987515173, 1982, 'Plastique', 304, -1, 176, -1, 0.03725248575210571), (987515173, 1982, 'Plastique', 336, -1, 176, -1, 0.0020749408286064863), (987515173, 1982, 'Plastique', 112, -1, 208, -1, 7.62264899094589e-05), (987515173, 1982, 'Plastique', 144, -1, 208, -1, 4.133026595809497e-05), (987515173, 1982, 'Plastique', 176, -1, 208, -1, 8.201674791052938e-05), (987515173, 1982, 'Plastique', 208, -1, 208, -1, 3.834217204712331e-05), (987515173, 1982, 'Plastique', 240, -1, 208, -1, 8.358430022781249e-06), (987515173, 1982, 'Plastique', 272, -1, 208, -1, 1.2020434951409698e-05), (987515173, 1982, 'Plastique', 304, -1, 208, -1, 1.2073540347046219e-05), (987515173, 1982, 'Plastique', 336, -1, 208, -1, 4.108172561245738e-06), (987515173, 1982, 'Plastique', 112, -1, 240, -1, 0.0001447224640287459), (987515173, 1982, 'Plastique', 144, -1, 240, -1, 3.534879215294495e-05), (987515173, 1982, 'Plastique', 176, -1, 240, -1, 2.1191388441366144e-05), (987515173, 1982, 'Plastique', 208, -1, 240, -1, 7.36214224161813e-06), (987515173, 1982, 'Plastique', 240, -1, 240, -1, 4.863757567363791e-06), (987515173, 1982, 'Plastique', 272, -1, 240, -1, 2.761969199127634e-06), (987515173, 1982, 'Plastique', 304, -1, 240, -1, 7.968383215484209e-07), (987515173, 1982, 'Plastique', 336, -1, 240, -1, 3.533784251885663e-07), (987515173, 1982, 'Plastique', 112, -1, 272, -1, 3.370254489709623e-05), (987515173, 1982, 'Plastique', 144, -1, 272, -1, 1.1651050044747535e-05), (987515173, 1982, 'Plastique', 176, -1, 272, -1, 1.3421683433989529e-05), (987515173, 1982, 'Plastique', 208, -1, 272, -1, 5.4174392971617635e-06), (987515173, 1982, 'Plastique', 240, -1, 272, -1, 9.571350574333337e-07), (987515173, 1982, 'Plastique', 272, -1, 272, -1, 6.892423130011593e-07), (987515173, 1982, 'Plastique', 304, -1, 272, -1, 5.696467724192189e-07), (987515173, 1982, 'Plastique', 336, -1, 272, -1, 7.459733524228795e-07), (987515173, 1982, 'Plastique', 112, -1, 304, -1, 3.35986987920478e-06), (987515173, 1982, 'Plastique', 144, -1, 304, -1, 3.3671278742986033e-06), (987515173, 1982, 'Plastique', 176, -1, 304, -1, 6.482889148173854e-06), (987515173, 1982, 'Plastique', 208, -1, 304, -1, 8.73669887369033e-06), (987515173, 1982, 'Plastique', 240, -1, 304, -1, 3.386883918210515e-06), (987515173, 1982, 'Plastique', 272, -1, 304, -1, 4.758877821586793e-06), (987515173, 1982, 'Plastique', 304, -1, 304, -1, 4.511898168857442e-06), (987515173, 1982, 'Plastique', 336, -1, 304, -1, 2.4077030502667185e-06), (987515173, 1982, 'Plastique', 112, -1, 336, -1, 1.54988174472237e-05), (987515173, 1982, 'Plastique', 144, -1, 336, -1, 5.3312338422983885e-05), (987515173, 1982, 'Plastique', 176, -1, 336, -1, 3.428035415709019e-05), (987515173, 1982, 'Plastique', 208, -1, 336, -1, 2.3748476451146416e-05), (987515173, 1982, 'Plastique', 240, -1, 336, -1, 3.8532980397576466e-05), (987515173, 1982, 'Plastique', 272, -1, 336, -1, 2.5802119125728495e-05), (987515173, 1982, 'Plastique', 304, -1, 336, -1, 3.325781290186569e-05), (987515173, 1982, 'Plastique', 336, -1, 336, -1, 4.184032150078565e-05), (987515173, 1982, 'Sol_Environement', 112, -1, 112, -1, 2.9366769432881412e-12), (987515173, 1982, 'Sol_Environement', 144, -1, 112, -1, 5.48636358566057e-10), (987515173, 1982, 'Sol_Environement', 176, -1, 112, -1, 4.922945322505257e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 112, -1, 1.0142226528842002e-05), (987515173, 1982, 'Sol_Environement', 240, -1, 112, -1, 9.192769539367873e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 112, -1, 5.620903903036378e-05), (987515173, 1982, 'Sol_Environement', 304, -1, 112, -1, 0.0003787271270994097), (987515173, 1982, 'Sol_Environement', 336, -1, 112, -1, 0.0001649119076319039), (987515173, 1982, 'Sol_Environement', 112, -1, 144, -1, 1.0210935386112396e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 144, -1, 5.798833626613487e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 144, -1, 1.4366597724801977e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 144, -1, 5.8999644352297764e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 144, -1, 1.8247819753014483e-05), (987515173, 1982, 'Sol_Environement', 272, -1, 144, -1, 0.00012619215704035014), (987515173, 1982, 'Sol_Environement', 304, -1, 144, -1, 0.00032413742155767977), (987515173, 1982, 'Sol_Environement', 336, -1, 144, -1, 9.636588947614655e-05), (987515173, 1982, 'Sol_Environement', 112, -1, 176, -1, 4.1780776882660575e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 176, -1, 1.0190673265242367e-06), (987515173, 1982, 'Sol_Environement', 176, -1, 176, -1, 6.58136605125037e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 176, -1, 1.7202869457833003e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 176, -1, 3.7331865314627066e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 176, -1, 2.8020253012073226e-05), (987515173, 1982, 'Sol_Environement', 304, -1, 176, -1, 0.00015342213737312704), (987515173, 1982, 'Sol_Environement', 336, -1, 176, -1, 0.00024263840168714523), (987515173, 1982, 'Sol_Environement', 112, -1, 208, -1, 3.999169621238252e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 208, -1, 7.509718784604047e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 208, -1, 1.3328549357538577e-06), (987515173, 1982, 'Sol_Environement', 208, -1, 208, -1, 9.057594070327468e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 208, -1, 6.959451752663881e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 208, -1, 1.0827143341884948e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 208, -1, 1.4378452988239587e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 208, -1, 2.319878376511042e-06), (987515173, 1982, 'Sol_Environement', 112, -1, 240, -1, 4.60828778159339e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 240, -1, 3.7673186170650297e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 240, -1, 2.4918125518524903e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 240, -1, 2.1697762520034303e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 240, -1, 5.576732178269594e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 240, -1, 8.619728646408475e-07), (987515173, 1982, 'Sol_Environement', 304, -1, 240, -1, 6.635400495724753e-07), (987515173, 1982, 'Sol_Environement', 336, -1, 240, -1, 7.825842089914659e-07), (987515173, 1982, 'Sol_Environement', 112, -1, 272, -1, 2.9951870601507835e-06), (987515173, 1982, 'Sol_Environement', 144, -1, 272, -1, 6.87938893406681e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 272, -1, 5.04081924646016e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 272, -1, 2.0085754215415363e-07), (987515173, 1982, 'Sol_Environement', 240, -1, 272, -1, 1.320834286389072e-07), (987515173, 1982, 'Sol_Environement', 272, -1, 272, -1, 2.697400418583129e-07), (987515173, 1982, 'Sol_Environement', 304, -1, 272, -1, 1.6117596146614233e-07), (987515173, 1982, 'Sol_Environement', 336, -1, 272, -1, 4.873600119026378e-07), (987515173, 1982, 'Sol_Environement', 112, -1, 304, -1, 6.995445005486545e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 304, -1, 4.0596822259431065e-07), (987515173, 1982, 'Sol_Environement', 176, -1, 304, -1, 8.917913874029182e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 304, -1, 2.3298541691474384e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 304, -1, 3.079601810895838e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 304, -1, 2.6826421617442975e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 304, -1, 1.5091852674231632e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 304, -1, 1.609982859918091e-06), (987515173, 1982, 'Sol_Environement', 112, -1, 336, -1, 4.6346880822056846e-07), (987515173, 1982, 'Sol_Environement', 144, -1, 336, -1, 1.1245992936892435e-06), (987515173, 1982, 'Sol_Environement', 176, -1, 336, -1, 6.800858045608038e-07), (987515173, 1982, 'Sol_Environement', 208, -1, 336, -1, 1.4417461215998628e-06), (987515173, 1982, 'Sol_Environement', 240, -1, 336, -1, 3.3329188227071427e-06), (987515173, 1982, 'Sol_Environement', 272, -1, 336, -1, 2.130468146788189e-06), (987515173, 1982, 'Sol_Environement', 304, -1, 336, -1, 2.2967662971495884e-06), (987515173, 1982, 'Sol_Environement', 336, -1, 336, -1, 3.383322791705723e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 112, -1, 4.59037937616813e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 112, -1, 2.4638964077894343e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 112, -1, 1.7661359379417263e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 112, -1, 0.00047500498476438224), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 112, -1, 0.00011574888048926368), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 112, -1, 0.0002096462994813919), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 112, -1, 0.0014418879291042686), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 112, -1, 0.002020640531554818), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 144, -1, 3.072047547902912e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 144, -1, 1.720365253277123e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 144, -1, 2.0789311747648753e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 144, -1, 0.0002744520897977054), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 144, -1, 0.0004651468188967556), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 144, -1, 0.0005288884858600795), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 144, -1, 0.00018992599507328123), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 144, -1, 0.0002050874609267339), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 176, -1, 1.5018348676676396e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 176, -1, 6.758959443686763e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 176, -1, 2.1058001948404126e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 176, -1, 2.412801404716447e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 176, -1, 3.541276964824647e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 176, -1, 7.938583439681679e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 176, -1, 0.00010072066652355716), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 176, -1, 0.0003779694379772991), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 208, -1, 1.5053492461447604e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 208, -1, 1.4652391655545216e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 208, -1, 4.7431522034457885e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 208, -1, 3.8244279494392686e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 208, -1, 3.647086941782618e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 208, -1, 1.1673816516122315e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 208, -1, 1.3314019270183053e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 208, -1, 5.961527131148614e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 240, -1, 4.2338047933299094e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 240, -1, 5.340836196410237e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 240, -1, 3.91564844903769e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 240, -1, 2.413066113149398e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 240, -1, 5.773184511781437e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 240, -1, 2.1077232304378413e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 240, -1, 1.7755282897269353e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 240, -1, 2.270090590172913e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 272, -1, 0.00020111247431486845), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 272, -1, 6.689425936201587e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 272, -1, 4.340330633567646e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 272, -1, 1.4518254829454236e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 272, -1, 7.103289135557134e-06), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 272, -1, 1.5493236787733622e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 272, -1, 1.540792618470732e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 272, -1, 0.00010492877481738105), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 304, -1, 9.522055188426748e-05), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 304, -1, 0.00011016927601303905), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 304, -1, 0.00015792834165040404), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 304, -1, 0.0005422052927315235), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 304, -1, 0.002344260225072503), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 304, -1, 0.007415777072310448), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 304, -1, 0.005866921041160822), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 304, -1, 0.00447053974494338), (987515173, 1982, 'Teint_Dans_La_Masse', 112, -1, 336, -1, 0.0002411019813735038), (987515173, 1982, 'Teint_Dans_La_Masse', 144, -1, 336, -1, 0.0020019246730953455), (987515173, 1982, 'Teint_Dans_La_Masse', 176, -1, 336, -1, 0.0007498019258491695), (987515173, 1982, 'Teint_Dans_La_Masse', 208, -1, 336, -1, 0.0033434682991355658), (987515173, 1982, 'Teint_Dans_La_Masse', 240, -1, 336, -1, 0.06401578336954117), (987515173, 1982, 'Teint_Dans_La_Masse', 272, -1, 336, -1, 0.03827493637800217), (987515173, 1982, 'Teint_Dans_La_Masse', 304, -1, 336, -1, 0.03888778015971184), (987515173, 1982, 'Teint_Dans_La_Masse', 336, -1, 336, -1, 0.005852381698787212), (987515173, 1982, 'autre_refus', 112, -1, 112, -1, 5.844096873097726e-10), (987515173, 1982, 'autre_refus', 144, -1, 112, -1, 2.0580751325383062e-08), (987515173, 1982, 'autre_refus', 176, -1, 112, -1, 1.4463229263128596e-06), (987515173, 1982, 'autre_refus', 208, -1, 112, -1, 9.254770702682436e-05), (987515173, 1982, 'autre_refus', 240, -1, 112, -1, 0.00021777678921353072), (987515173, 1982, 'autre_refus', 272, -1, 112, -1, 0.0017514508217573166), (987515173, 1982, 'autre_refus', 304, -1, 112, -1, 0.017753202468156815), (987515173, 1982, 'autre_refus', 336, -1, 112, -1, 0.008983198553323746), (987515173, 1982, 'autre_refus', 112, -1, 144, -1, 9.156258897746739e-07), (987515173, 1982, 'autre_refus', 144, -1, 144, -1, 2.9112368338246597e-06), (987515173, 1982, 'autre_refus', 176, -1, 144, -1, 1.0349880540161394e-05), (987515173, 1982, 'autre_refus', 208, -1, 144, -1, 0.00013496392057277262), (987515173, 1982, 'autre_refus', 240, -1, 144, -1, 0.0003474707482382655), (987515173, 1982, 'autre_refus', 272, -1, 144, -1, 0.004943825304508209), (987515173, 1982, 'autre_refus', 304, -1, 144, -1, 0.04345330595970154), (987515173, 1982, 'autre_refus', 336, -1, 144, -1, 0.09543640911579132), (987515173, 1982, 'autre_refus', 112, -1, 176, -1, 1.5132874978007749e-05), (987515173, 1982, 'autre_refus', 144, -1, 176, -1, 0.00012739170051645488), (987515173, 1982, 'autre_refus', 176, -1, 176, -1, 0.0002364585525356233), (987515173, 1982, 'autre_refus', 208, -1, 176, -1, 0.0008103065192699432), (987515173, 1982, 'autre_refus', 240, -1, 176, -1, 0.0006546415970660746), (987515173, 1982, 'autre_refus', 272, -1, 176, -1, 0.004316359758377075), (987515173, 1982, 'autre_refus', 304, -1, 176, -1, 0.023308690637350082), (987515173, 1982, 'autre_refus', 336, -1, 176, -1, 0.018677936866879463), (987515173, 1982, 'autre_refus', 112, -1, 208, -1, 8.827361307339743e-05), (987515173, 1982, 'autre_refus', 144, -1, 208, -1, 0.00018597193411551416), (987515173, 1982, 'autre_refus', 176, -1, 208, -1, 0.00031969472183845937), (987515173, 1982, 'autre_refus', 208, -1, 208, -1, 0.0003578861360438168), (987515173, 1982, 'autre_refus', 240, -1, 208, -1, 0.00019908715330529958), (987515173, 1982, 'autre_refus', 272, -1, 208, -1, 0.0002866231952793896), (987515173, 1982, 'autre_refus', 304, -1, 208, -1, 0.00020181240688543767), (987515173, 1982, 'autre_refus', 336, -1, 208, -1, 0.0002442473778501153), (987515173, 1982, 'autre_refus', 112, -1, 240, -1, 0.0002338645572308451), (987515173, 1982, 'autre_refus', 144, -1, 240, -1, 0.0001084935647668317), (987515173, 1982, 'autre_refus', 176, -1, 240, -1, 6.493357795989141e-05), (987515173, 1982, 'autre_refus', 208, -1, 240, -1, 2.532877078920137e-05), (987515173, 1982, 'autre_refus', 240, -1, 240, -1, 7.284621096914634e-05), (987515173, 1982, 'autre_refus', 272, -1, 240, -1, 0.00013979557843413204), (987515173, 1982, 'autre_refus', 304, -1, 240, -1, 8.923102723201737e-05), (987515173, 1982, 'autre_refus', 336, -1, 240, -1, 8.176133269444108e-05), (987515173, 1982, 'autre_refus', 112, -1, 272, -1, 0.0002683814091142267), (987515173, 1982, 'autre_refus', 144, -1, 272, -1, 0.00011149910278618336), (987515173, 1982, 'autre_refus', 176, -1, 272, -1, 0.00012519092706497759), (987515173, 1982, 'autre_refus', 208, -1, 272, -1, 5.118093031342141e-05), (987515173, 1982, 'autre_refus', 240, -1, 272, -1, 2.919871076301206e-05), (987515173, 1982, 'autre_refus', 272, -1, 272, -1, 4.2785708501469344e-05), (987515173, 1982, 'autre_refus', 304, -1, 272, -1, 6.842734728707e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.00014225176710169762), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.00011554059892660007), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021862855646759272), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.00042531220242381096), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.0004276064282748848), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.595552986254916e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.184090383001603e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1680650459311437e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.878130206023343e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.0002474193461239338), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.00046916891005821526), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003354863147251308), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.0002387602289672941), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.000106784027593676), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.600510384188965e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.00013112752640154213), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007324660546146333)]} ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Step 4442 tile have less inputs used (1) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 4441 detect_points is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 4443 count_percent_refus is not consistent : 4 used against 3 in the step definition ! Step 4444 send_mail_dechet have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 1 of step 4440 have datatype=1 whereas input 0 of step 4443 have datatype=2 WARNING : type of output 1 of step 4441 doesn't seem to be define in the database( WARNING : type of input 4 of step 4443 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 1.0809929370880127 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 7 step1:init_dechet Sat Mar 8 11:34:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec debut step init detect dechets input : temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [987321136] Looping around the photos to save general results len do output : 1 /987321136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.06848359107971191 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.00038433074951171875 time spend to save output : 0.06927251815795898 total time spend for step 1 : 0.0696568489074707 step2:tile Sat Mar 8 11:34:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_correct_upm', 'ETA': 86400, 'new_width': 1500, 'new_height': 20000, 'host': 'www.fotonower.com', 'protocol': 'https', 'photo_tile_type': 1522, 'option_bande': 'True'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {406: 410} map_filenames : {987321136: 'temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 Loaded 0 chid ids of type : 0 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_correct_upm&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 21177857 with name tile_correct_upm feed_id_new_photos : 21177857 filename : temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea.jpg photo_id : 987321136 height_image_input : 439 width_image_input : 562 new_width : 1500 new_height : 20000 stride : 0 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 0 list_bib_to_crops : 1 [(0, 562, 0, 439, 0)] new_crops_tiles : 1 crop_transformed : 0 batch 1 Loaded 1 chid ids of type : 1522 treat the image : temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.011269569396972656 About to upload 1 photos upload in portfolio : 21177857 Result OK ! uploaded one batch 0 Elapsed time : 7.735050439834595 upload mediasElapsed time : 7.746399164199829 , 0Saving 0 CHIs. end of tileElapsed time : 7.768097639083862 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1341831545'] Looping around the photos to save general results len do output : 1 /1341831545Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1341831545', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.012654542922973633 save_final save missing photos in datou_result : time spend for datou_step_exec : 18.780807733535767 time spend to save output : 0.012906789779663086 total time spend for step 2 : 18.79371452331543 step3:detect_points Sat Mar 8 11:34:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : False To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 2 Inside predict_points step exec : nb paths : 1 treate image : temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea_0.jpg size of numpy array img : 2960752 scale method : caffe/skimage size of numpy array img_scale : 2655880 (416, 532, 3) nb_h 7 nb_w 11 size of sub images : (224, 224, 3) size of caffe_input : 46362776 (77, 3, 224, 224) time to do the preprocess : 0.06333041191101074 time to do a prediction : 15.782743692398071 dict_keys(['prob']) shape of output (77, 10, 1, 1) shape of the out_put heatmap (10, 7, 11) number of sub_photos vertical and horizon 7 11 size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) Inside saveOutput : final : False verbose : False Inside savePoints : final : False verbose : False threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 final : False save missing photos in datou_result : time spend for datou_step_exec : 17.718851804733276 time spend to save output : 1.04594087600708 total time spend for step 3 : 18.764792680740356 step4:count_percent_refus Sat Mar 8 11:34:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure debut step count percent refus (987321136, 0.9481481481481482) ('temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) list_photo : [987321136] list_photo_correc : [1341831545] debut step count percent refus Treating photo_id : 987321136 Calcul du count_res count res : ((492774966, 3), (2107752386, 7)) Hashtag_id : 492774966 Hashtag_id : 2107752386 We have 2 classes in this image Inside saveOutput : final : False verbose : False begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 2.4004619121551514 save missing photos in datou_result : time spend for datou_step_exec : 0.8381390571594238 time spend to save output : 2.40088152885437 total time spend for step 4 : 3.239020586013794 step5:brightness Sat Mar 8 11:34:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea.jpg Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.024262428283691406 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 1.138305902481079 save missing photos in datou_result : time spend for datou_step_exec : 0.976405143737793 time spend to save output : 1.1676194667816162 total time spend for step 5 : 2.144024610519409 step6:blur_detection Sat Mar 8 11:34:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1741430050_4154994_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.008625984191894531 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.23891425132751465 save missing photos in datou_result : time spend for datou_step_exec : 0.4270288944244385 time spend to save output : 0.25281548500061035 total time spend for step 6 : 0.6798443794250488 step7:send_mail_dechet Sat Mar 8 11:34:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail dechet senders@fotonower.com retour de l'envoi du mail : None Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1341831545'] Looping around the photos to save general results len do output : 1 /987321136. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1341831545', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.14173674583435059 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.9408168792724609 time spend to save output : 0.14223670959472656 total time spend for step 7 : 1.0830535888671875 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 30.0, 61.64383561643836, {'carton': 3, 'Papier_Magazine': 7}, {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 0.6164383561643836)} ############################### TEST image_temperature_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_temperature_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.6299009323120117 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_temperature_detection Sat Mar 8 11:34:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanche_jaune_detection treat image : temp/1741430096_4154994_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 time spend for datou_step_exec : 0.6865494251251221 time spend to save output : 7.43865966796875e-05 total time spend for step 1 : 0.6866238117218018 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {984484223: [(984484223, 1.004309911525615, 492630606)]} {984484223: [(984484223, 1.004309911525615, 492630606)]} ############################### TEST broca ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 1.8551661968231201 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:split_time_score Sat Mar 8 11:34:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec split portfolio by speed calcul order for each photo with time calcul time for a portfolio 2021-12-01 10:11:30 2021-12-01 10:11:32 2021-12-01 10:11:30 2021-12-01 10:11:34 2021-12-01 10:11:32 2021-12-01 10:11:40 2021-12-01 10:11:34 2021-12-01 10:12:17 2021-12-01 10:11:40 2021-12-01 10:12:24 2021-12-01 10:12:17 2021-12-01 10:12:27 2021-12-01 10:12:24 2021-12-01 10:12:29 2021-12-01 10:12:27 2021-12-01 10:12:56 2021-12-01 10:12:29 2021-12-01 10:13:04 2021-12-01 10:12:56 2021-12-01 10:13:13 2021-12-01 10:13:04 2021-12-01 10:13:04 distance 1.4513659170185111 2021-12-01 10:13:13 2021-12-01 10:13:22 2021-12-01 10:13:13 2021-12-01 10:13:30 2021-12-01 10:13:22 2021-12-01 10:16:14 2021-12-01 10:13:30 2021-12-01 10:13:30 distance 8.382409567451603 2021-12-01 10:16:14 2021-12-01 10:16:18 2021-12-01 10:16:14 2021-12-01 10:16:47 2021-12-01 10:16:18 2021-12-01 10:16:53 2021-12-01 10:16:47 2021-12-01 10:16:47 distance 8.03396608896571 2021-12-01 10:16:53 2021-12-01 10:16:57 2021-12-01 10:16:53 dict_time_useful: {0: [1098136690, 1098136784, 48.864288393888884, 2.19199505125, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94]], 1: [1098136974, 1098137007, 48.86291258986111, 2.19361357125, [datetime.datetime(2021, 12, 1, 10, 16, 14), datetime.datetime(2021, 12, 1, 10, 16, 47), 33]]} get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "CS"; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "OM"; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 time spend for datou_step_exec : 7.541757583618164 time spend to save output : 9.250640869140625e-05 total time spend for step 1 : 7.5418500900268555 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(21177874, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]} résultat du premier test BROCA : True True ############################### TEST crop_conditional ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1335 frcnn is not linked in the step_by_step architecture ! WARNING : step 1336 crop_condition is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] origin We have 1 , BBBFBFBFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 time to download the photos : 0.8541154861450195 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:frcnn Sat Mar 8 11:35:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! Inside try reload ! To loadFromThcl() model_param file didn't exist model_name : learn_piece_voiture_0808_v2 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/learn_piece_voiture_0808_v2 /data/models_weight/learn_piece_voiture_0808_v2/caffemodel size_local : 350215080 size in s3 : 350215080 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 caffemodel already exist and didn't need to update /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt size_local : 7166 size in s3 : 7166 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt caffemodel : /data/models_weight/learn_piece_voiture_0808_v2/caffemodel Loaded network /data/models_weight/learn_piece_voiture_0808_v2/caffemodel About to compute detect_faster_rcnn : len(args) : 6 Inside frcnn step exec : nb paths : 6 image_path : temp/1741430107_4154994_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg image_size (294, 285, 3) [[[ 29 29 29] [ 29 29 29] [ 30 30 30] ... [182 172 165] [141 131 124] [103 94 90]] [[ 29 29 29] [ 29 29 29] [ 31 31 31] ... [231 220 212] [202 193 184] [164 154 147]] [[ 30 30 30] [ 27 27 27] [ 26 26 26] ... [223 211 199] [229 219 209] [228 217 209]] ... [[ 22 27 25] [ 16 21 19] [ 11 16 14] ... [166 145 123] [168 147 125] [170 149 127]] [[ 20 25 23] [ 17 22 20] [ 15 20 18] ... [163 142 120] [165 144 122] [166 145 123]] [[ 13 18 16] [ 17 22 20] [ 20 25 23] ... [162 141 119] [163 142 120] [163 142 121]]] Detection took 0.157s for 300 object proposals image_path : temp/1741430107_4154994_950003813_e28be02dfcce79cce594a390a9911a0a.jpg image_size (254, 229, 3) [[[202 190 186] [205 193 189] [205 194 190] ... [ 81 70 56] [ 80 69 55] [ 78 67 53]] [[198 187 183] [200 189 185] [198 189 185] ... [ 50 41 28] [ 44 36 23] [ 45 36 23]] [[192 187 184] [191 186 183] [191 186 183] ... [ 36 30 23] [ 32 29 21] [ 33 27 20]] ... [[187 186 190] [186 185 189] [188 184 189] ... [ 43 38 35] [ 37 33 28] [ 33 28 25]] [[184 185 189] [183 184 188] [184 183 187] ... [ 28 23 22] [ 29 24 21] [ 33 28 27]] [[181 185 186] [180 184 185] [182 184 185] ... [ 23 15 16] [ 22 14 14] [ 24 16 17]]] Detection took 0.101s for 300 object proposals image_path : temp/1741430107_4154994_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg image_size (2160, 3840, 3) [[[111 118 91] [113 120 93] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 24 41 38]] [[111 118 91] [112 119 92] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 23 40 37]] [[113 118 91] [114 119 92] [115 120 93] ... [ 22 39 36] [ 23 40 37] [ 23 40 37]] ... [[120 125 94] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 23 37 35]] [[119 124 93] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]] [[118 123 91] [117 122 90] [117 122 91] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]]] Detection took 0.672s for 300 object proposals image_path : temp/1741430107_4154994_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg image_size (480, 640, 3) [[[36 41 44] [36 41 44] [35 40 43] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[37 42 45] [36 41 44] [35 40 43] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[37 42 45] [36 41 44] [35 40 43] ... [ 3 5 5] [ 4 6 6] [ 4 6 6]] ... [[42 47 50] [41 46 49] [40 45 48] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[41 46 49] [41 46 49] [40 45 48] ... [ 0 2 2] [10 12 12] [22 24 24]] [[40 45 48] [40 45 48] [40 45 48] ... [10 12 12] [17 19 19] [26 28 28]]] Detection took 0.074s for 300 object proposals image_path : temp/1741430107_4154994_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg image_size (480, 614, 3) [[[ 44 44 44] [ 49 51 51] [ 42 44 44] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 43 43 43] [ 36 38 38] [ 39 41 41] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[ 70 70 70] [ 40 42 42] [ 41 43 43] ... [ 4 6 6] [ 4 6 6] [ 4 6 6]] ... [[103 101 101] [110 108 108] [ 61 59 59] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 98 96 96] [115 113 113] [ 73 71 71] ... [ 0 2 2] [ 11 13 13] [ 21 23 23]] [[ 92 90 90] [114 112 112] [ 87 82 83] ... [ 10 12 12] [ 18 20 20] [ 25 27 27]]] Detection took 0.100s for 300 object proposals image_path : temp/1741430107_4154994_950003696_11e3a77b72af4b332d366d98984039c7.jpg image_size (2160, 3264, 3) [[[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 48 60 64] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 47 59 63]] ... [[167 164 160] [167 164 160] [167 164 160] ... [ 44 59 61] [ 44 59 61] [ 44 59 61]] [[165 162 158] [165 162 158] [165 162 158] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]] [[164 161 157] [164 161 157] [164 161 157] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]]] Detection took 2.123s for 300 object proposals len de result frcnn : 6 time spend for datou_step_exec : 10.141620635986328 time spend to save output : 0.0003135204315185547 total time spend for step 1 : 10.141934156417847 step2:crop_condition Sat Mar 8 11:35:17 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 757 Loading chi in step crop for subpids : 6 ! batch 1 Loaded 32 chid ids of type : 757 begin to crop the class : phare param for this class : {'margin_type': 'margin', 'margin_value': 300, 'feed_id_new_photos': 1097966} filtre for class : phare hashtag_id of this class : 492624020 WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : phare begin to crop the class : aile-avant param for this class : {} filtre for class : aile-avant hashtag_id of this class : 2106233860 WARNING : margin is only used for type bib ! now we use margin_relative for the photo_id : 926687666 now we use margin_relative for the photo_id : 950003812 map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : aile-avant time spend for datou_step_exec : 6.508961915969849 time spend to save output : 7.152557373046875e-05 total time spend for step 2 : 6.509033441543579 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {1071808962: [926687666, 'temp/1741430107_4154994_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1741430107_4154994_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1741430107_4154994_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1741430107_4154994_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1741430107_4154994_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]} ############################### TEST image_blanchir ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_blanchir list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.5468261241912842 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_blanchir Sat Mar 8 11:35:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanchir_image https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos:21177905 treat image : temp/1741430124_4154994_990111206_7ca22c7e68dd0a10509c7987af0cf549.png blanchir func Result OK ! time spend for datou_step_exec : 8.30003309249878 time spend to save output : 9.059906005859375e-06 total time spend for step 1 : 8.300042152404785 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[1818, 0, 990111206, 1, 1, 1, None, 1, None]] time used for this insertion : 0.015086174011230469 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(990111206, '1341831943', 0, 300, 0, 381, 1, 1, 'blanc')] [(990111206, '1341831943', 0, 300, 0, 381, 1, 1, 'blanc')] ############################### TEST darker_image ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : darker_image list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 1.2209548950195312 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:darker_image Sat Mar 8 11:35:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec dans la step darker batch 1 Loaded 7 chid ids of type : 2228 +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points treat image : temp/1741430134_4154994_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg Result OK ! batch 1 Loaded 7 chid ids of type : 2228 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! crops sauvegardes time spend for datou_step_exec : 13.07043170928955 time spend to save output : 2.1457672119140625e-05 total time spend for step 1 : 13.07045316696167 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[2085, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.01165008544921875 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, '1341831955', 0, 897, 0, 1431, 1, 1, 'darker')] [(989962950, '1341831955', 0, 897, 0, 1431, 1, 1, 'darker')] batch 1 Loaded 7 chid ids of type : 2228 ############################### TEST img_aug ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : data_aug list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.22411727905273438 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:data_aug Sat Mar 8 11:35:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec numpy.version est ancienne, on utilise l'ancien bit generator numpy.version est ancienne, on utilise l'ancien bit generator batch 1 Loaded 7 chid ids of type : 2228 +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points on traite des points Result OK ! batch 1 Loaded 7 chid ids of type : 2260 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1341831986 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1341831986 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1341831986 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1341831986 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1341831986 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1341831986 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1341831986 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! photo_uploade augmentation faite pour la photo : 989962950 time spend for datou_step_exec : 11.413347959518433 time spend to save output : 5.53131103515625e-05 total time spend for step 1 : 11.413403272628784 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[2041, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.014649152755737305 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, 1341831986, 0, 1431, 0, 897, 1, 1, 'img_aug')] [(989962950, 1341831986, 0, 1431, 0, 897, 1, 1, 'img_aug')] batch 1 Loaded 7 chid ids of type : 2260 ############################### TEST rubbia ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 9.214880228042603 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Sat Mar 8 11:36:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec begin split time score 2022-04-13 10:29:59 0 TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('05', 2), ('07', 25), ('06', 1), ('08', 96), ('09', 44), ('10', 64)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {1: 188, 2: 36, 3: 8} 07092021 4599398 Nombre de photos uploadées : 232 / 23040 (1%) 07092021 4599398 Nombre de photos taguées (types de déchets): 232 / 232 (100%) 07092021 4599398 Nombre de photos taguées (volume) : 232 / 232 (100%) elapsed_time : load_data_split_time_score 6.9141387939453125e-06 elapsed_time : order_list_meta_photo_and_scores 0.0004229545593261719 elapsed_time : fill_and_build_computed_from_old_data 0.02711796760559082 elapsed_time : insert_dashboard_record_day_entry 0.029308557510375977 Creating list_photo_total elapsed_time : select_descriptors 19.929964542388916 07092021 4599398 Nombre de photos avec descriptors (type 3963) : 232 / 232 (100%) ERROR : Hum hum, what can we do for different size of descriptors (ignore the difference ) : 0 vs 2048 photo_id : 1049293230 photo_id_prec : 0 0:00:00|ON:0:27:28.999934|OFF:1:46:59.999878|ON:0:00:20.000007|OFF:0:01:51.000162|ON:0:12:18.999909|OFF:0:01:01.000055|ON:0:08:50.000116|OFF:0:00:09.999867|ON:0:00:19.999899|OFF:0:00:09.000058|ON:0:00:29.999860|OFF:0:01:40.000249|ON:0:00:30.999931|OFF:0:07:40.000107|ON:0:00:28.999981|OFF:0:00:09.999968|ON:0:00:10.999986|OFF:0:08:09.999919|ON:0:00:40.000176|OFF:0:01:08.999784|ON:0:00:11.000245|OFF:0:00:39.999921|ON:0:00:19.000004|OFF:0:06:31.000039|ON:0:02:09.999929|OFF:0:01:40.000021|ON:0:00:39.000031|OFF:0:07:10.999966|ON:0:12:30.000101|OFF:0:00:18.999765|ON:0:00:39.999946|OFF:0:00:11.000212|ON:0:00:29.999851|OFF:0:00:20.000150|ON:0:00:30.000042|OFF:0:00:18.999771|ON:0:07:31.000243|OFF:0:00:09.999942|ON:0:00:08.999822|OFF:0:00:11.000172|ON:0:00:39.999914|OFF:0:00:20|ON:0:31:10.000147|OFF:0:12:18.999857|ON:0:01:39.999950|OFF:0:00:19.999947|ON:0:00:21.000213|OFF:0:00:28.999911|ON:0:00:21.000117|OFF:0:00:40.000020|ON:0:10:58.999762|OFF:0:00:41.000023|ON:0:00:09.000008|OFF:0:00:21.000234|ON:0:00:29.999765|OFF:0:00:28.999920|ON:0:00:21.000174|OFF:0:00:30.000078|ON:0:00:29.999938|OFF:0:00:29.999871|ON:0:00:08.999965|OFF:0:09:31.000234|ON:0:00:09.999916|OFF:0:00:20.000049|ON:0:04:09.999926|OFF:0:01:09.000014|ON:0:02:00.999957|OFF:0:00:08.999951|ON:0:00:21.000053|OFF:0:00:18.999927|ON:0:00:39.999997|OFF:0:00:30.000158|ON: 07092021 Removing 115 photos because of the 'same image' condition Total on : 7859.999814999999 list_time_on Total off : 10509.0002 list_time_off dist_desc begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 232 time used for this insertion : 4.647794723510742 photos_removed : len 115 elapsed_time : remove_photo_duplicate 5.017802476882935 Creating list_photo_total XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX elapsed_time : count_sum_diff_and_build_graph 0.10761737823486328 Total photos : 232 ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 10 hashtag : 2107750911 photo_id =1049308384 : rungis_plastique_fonce ..can't find max_score_info ...can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .....Change port : 25 hashtag : 2107750908 photo_id =1049311795 : rungis_papier ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 4 hashtag : 2107750911 photo_id =1049311961 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 1 hashtag : 2107750908 photo_id =1049312208 : rungis_papier .....Change port : 5 hashtag : 2107750911 photo_id =1049312420 : rungis_plastique_fonce .Change port : 1 hashtag : 2107750908 photo_id =1049312422 : rungis_papier ..can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049312438 : rungis_plastique_fonce ....can't find max_score_info ...can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info ....Change port : 12 hashtag : 2107750908 photo_id =1049312556 : rungis_papier .can't find max_score_info ..can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750911 photo_id =1049312984 : rungis_plastique_fonce ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .........can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 17 hashtag : 2107751280 photo_id =1049317359 : rungis_aluminium .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750913 photo_id =1049317524 : rungis_tetrapak .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049318212 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..........Change port : 12 hashtag : 2107750908 photo_id =1049318287 : rungis_papier ...can't find max_score_info ..Change port : 4 hashtag : 2107750911 photo_id =1049318294 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info . Total photos : 232 Number of lists : 15 counter photos in port : 117 hashtag : rungis_aluminium(2107751280) : 8 photos in 1 portfolios ! hashtag : rungis_carton(2107750907) : 0 photos in 0 portfolios ! hashtag : rungis_papier(2107750908) : 33 photos in 6 portfolios ! hashtag : rungis_plastique_clair(2107750909) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_dur(2107750910) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_fonce(2107750911) : 74 photos in 7 portfolios ! hashtag : rungis_tapis_vide(2107750912) : 0 photos in 0 portfolios ! hashtag : rungis_tetrapak(2107750913) : 2 photos in 1 portfolios ! elapsed_time : group_photo_by_moyenne_exp 0.015878677368164062 elapsed_time : compute_and_correct_tag_with_moyenne_mobile 5.4836273193359375e-06 today str has not a value , we define it as the date of the first image todaystr_first : 07092021 attention , prev_timestamp is 0 , we do nothing *******o** BIG TIME 550.0000100135803 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049307693 2021-09-07 07:45:54.010041 id_data : 12 * BIG TIME 168.99982810020447 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049308235 2021-09-07 07:48:43.009869 id_data : 13 ** BIG TIME 499.9998118877411 (191.00026988983154, 2, 0, 0, 0.93498826, 0, 0, 0, 0, 0, 0.9018999995946884, 0.002, 0.002999984407424927, 2.9, 0.019000051021575926, -0.01666705330212911) on 7 1049309345 2021-09-07 07:58:43.009858 id_data : 20 * BIG TIME 371.0001440048218 (271.00081276893616, 5, 0.24365342, 0, 0, 0, 0, 0.38418204, 0, 0.34177557, 0.9720000085830689, 0.0041, 0.014999971222877502, 2.9, 0.061000473976135255, -0.2516670016447703) on 15 1049310132 2021-09-07 08:09:54.010082 id_data : 51 * BIG TIME 461.0001001358032 (301.0006546974182, 0, 0.5752453, 0, 0, 0, 0, 0, 0, 0, 1.0228999980926514, 0.0056, 0.02589997522830963, 2.9, 0.009999968051910401, -0.0) on 18 1049310905 2021-09-07 08:18:54.009936 id_data : 60 * BIG TIME 370.0001759529114 (359.0003435611725, 5, 0, 0, 0, 0, 0, 0.85983855, 0, 0, 1.0898999773979188, 0.0078, 0.04109999623298645, 2.9, 0.008999762058258056, -0.0) on 24 1049311767 2021-09-07 08:28:24.010105 id_data : 81 *** BIG TIME 411.0001149177551 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312208 2021-09-07 08:40:04.010052 id_data : 97 * BIG TIME 549.9999330043793 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312363 2021-09-07 08:49:14.009985 id_data : 98 ** BIG TIME 168.99987387657166 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312508 2021-09-07 08:58:23.009966 id_data : 123 * BIG TIME 259.99999809265137 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312556 2021-09-07 09:02:43.009964 id_data : 124 * BIG TIME 190.00016593933105 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312803 2021-09-07 09:07:34.010149 id_data : 135 * BIG TIME 180.0000081062317 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312984 2021-09-07 09:10:34.010157 id_data : 136 * BIG TIME 1480.0000269412994 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316209 2021-09-07 09:35:23.009898 id_data : 138 * BIG TIME 668.9998891353607 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316332 2021-09-07 09:47:53.009987 id_data : 147 * BIG TIME 649.9999890327454 (1086.000019311905, 5, 0, 0, 0, 0, 0, 0.69907516, 0, 0.23055789, 1.6300000094890594, 0.0168, 0.08039999685287476, 2.9, 0.01100021505355835, -0.0) on 68 1049317197 2021-09-07 10:02:34.010134 id_data : 168 * BIG TIME 540.0001981258392 (1189.0002081394196, 0, 0.8074409, 0, 0, 0, 0, 0, 0, 0, 1.7199999867916107, 0.0194, 0.09519999706745148, 2.9, 0.009999808073043823, -0.0) on 78 1049318212 2021-09-07 10:16:24.010117 id_data : 198 * BIG TIME 190.00007104873657 (1199.0000162124634, 5, 0, 0.22708784, 0, 0, 0, 0.7244179, 0, 0, 1.781000003194809, 0.0202, 0.10109996955394746, 2.9, 0.011000241041183472, -0.0) on 79 1049318219 2021-09-07 10:20:04.010153 id_data : 202 **Count Time bigger than 30s : 31 #Number Photos for regression : {'07092021': {2107751280: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 80.99965000152588, 2107751017: 0}, 2107750907: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 49.000049114227295, 2107751017: 0}, 2107750908: {2107751013: 0, 2107751014: 11.000201940536499, 2107751015: 0, 2107751016: 534.9997780323029, 2107751017: 0}, 2107750909: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750910: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 31.000057697296143, 2107751017: 0}, 2107750911: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 564.0013737678528, 2107751017: 19.999656200408936}, 2107750912: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750913: {2107751013: 0, 2107751014: 8.999944925308228, 2107751015: 0, 2107751016: 89.99986672401428, 2107751017: 9.999994993209839}}} 07092021|rungis_aluminium, 05102018_papier_non_papier_dense:0 07092021|rungis_aluminium, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_aluminium, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_aluminium, 05102018_papier_non_papier_tres_dense:80.99965000152588 07092021|rungis_aluminium, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_carton, 05102018_papier_non_papier_tres_dense:49.000049114227295 07092021|rungis_carton, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_papier, 05102018_papier_non_papier_dense:0 07092021|rungis_papier, 05102018_papier_non_papier_peu_dense:11.000201940536499 07092021|rungis_papier, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_papier, 05102018_papier_non_papier_tres_dense:534.9997780323029 07092021|rungis_papier, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_dense:31.000057697296143 07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_dense:564.0013737678528 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_peu_dense:19.999656200408936 07092021|rungis_tapis_vide, 05102018_papier_non_papier_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_dense:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_peu_dense:8.999944925308228 07092021|rungis_tetrapak, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_dense:89.99986672401428 07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_peu_dense:9.999994993209839 #Number Photos for regression amount gros magasin papier (time_diff then nb_photo) : We have not displayed the number of photos removed for one material since Rungis_Papier wasn't in the thcl used ! 07092021_time_diff_distrib Number amount portfolio for this type of dechet : aluminium 8 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_aluminium_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177938 with name like 07092021_aluminium_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : carton 6 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_carton_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177939 with name like 07092021_carton_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : papier 29 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_papier_05102018_papier_non_papier_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177940 with name like 07092021_papier_05102018_papier_non_papier_peu_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_papier_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177941 with name like 07092021_papier_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : plastique_clair 0 Number amount portfolio for this type of dechet : plastique_dur 2 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_dur_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177942 with name like 07092021_plastique_dur_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : plastique_fonce 42 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_fonce_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177943 with name like 07092021_plastique_fonce_05102018_papier_non_papier_tres_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_fonce_05102018_papier_non_papier_tres_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177944 with name like 07092021_plastique_fonce_05102018_papier_non_papier_tres_peu_dense Number amount portfolio for this type of dechet : tapis_vide 0 Number amount portfolio for this type of dechet : tetrapak 11 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177945 with name like 07092021_tetrapak_05102018_papier_non_papier_peu_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177946 with name like 07092021_tetrapak_05102018_papier_non_papier_tres_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_tres_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 21177947 with name like 07092021_tetrapak_05102018_papier_non_papier_tres_peu_dense NUMBER BATCH : 15 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['pcnc', 'pcm', 'jrm', 'flux_dev', 'pehd_pp', 'papier', 'carton', 'plastique_dur', 'plastique_clair', 'pet_clair', 'plastique_fonce', 'tetrapak', 'aluminium', 'carton_emr', 'grands_cartons', 'gros_de_magasin', 'tapis_vide'] We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 10, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 191.00026988983154, 3: 0, 4: 0}, 'duration': 9008.999763965607, 'nb_balles_papier': 0.19500026988983155, 'begin_time_port': 'image_07092021_05_20_04_010050m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.19500026988983155 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 25, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 225.99965572357178, 3: 0, 4: 0}, 'duration': 2329.999878883362, 'nb_balles_papier': 0.2299996557235718, 'begin_time_port': 'image_07092021_07_50_23_010046m0.jpg 0.010000231981277466 for time 10.000231981277466, id_amount 3 this amount prod time diff : 0.010000231981277466'} Production hashtag (incorrect ponderation at 20-10-18) : 0.2299996557235718 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 4, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 141.00006794929504, 3: 0, 4: 0}, 'duration': 189.9997718334198, 'nb_balles_papier': 0.14100006794929504, 'begin_time_port': 'image_07092021_08_29_24_010122m0.jpg 0.011000197172164917 for time 11.000197172164917, id_amount 3 this amount prod time diff : 0.011000197172164917'} Production hashtag (incorrect ponderation at 20-10-18) : 0.14100006794929504 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 1, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 9.000005006790161, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.00900000500679016, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.00900000500679016 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 5, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 148.99991703033447, 3: 0, 4: 0}, 'duration': 698.9998500347137, 'nb_balles_papier': 0.15099991703033447, 'begin_time_port': 'image_07092021_08_40_04_010052m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.15099991703033447 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 1, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 11.000216960906982, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.011000216960906983, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.011000216960906983 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 2, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 28.9998140335083, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.0289998140335083, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.0289998140335083 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 1, 1: 0, 2: 11, 3: 0, 4: 0}, 'map_time_amount': {0: 11.000201940536499, 1: 0, 2: 111.00051093101501, 3: 0, 4: 0}, 'duration': 330.0000479221344, 'nb_balles_papier': 0.12300071287155151, 'begin_time_port': 'image_07092021_08_52_53_009918m0.jpg 0.01 for time 10.0, id_amount 3 this amount prod time diff : 0.01'} Production hashtag (incorrect ponderation at 20-10-18) : 0.12300071287155151 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 8, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 60.99999809265137, 3: 0, 4: 0}, 'duration': 291.0001850128174, 'nb_balles_papier': 0.06299999809265137, 'begin_time_port': 'image_07092021_09_02_43_009964m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.06299999809265137 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 1, 1: 0, 2: 16, 3: 0, 4: 0}, 'map_time_amount': {0: 8.999944925308228, 1: 0, 2: 148.99963188171387, 3: 0, 4: 0}, 'duration': 3119.999976873398, 'nb_balles_papier': 0.16199957680702212, 'begin_time_port': 'image_07092021_09_10_34_010157m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.16199957680702212 We filter photos on hashtag condition ! result_one_balle_Type_aluminium:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 7, 3: 1, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 72.00012874603271, 3: 9.999994993209839, 4: 0}, 'duration': 150.00017404556274, 'nb_balles_papier': 0.08200012373924254, 'begin_time_port': 'image_07092021_10_03_24_009930m0.jpg 0.009999775886535644 for time 9.999775886535645, id_amount 3 this amount prod time diff : 0.009999775886535644'} Production hashtag (incorrect ponderation at 20-10-18) : 0.08200012373924254 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_tetrapak:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 2, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 19.99965810775757, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.01999965810775757, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.01999965810775757 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 10, 3: 2, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 81.00049686431885, 3: 19.999656200408936, 4: 0}, 'duration': 428.99973487854004, 'nb_balles_papier': 0.10300015306472779, 'begin_time_port': 'image_07092021_10_16_24_010117m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.10300015306472779 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 4, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 40.00009298324585, 3: 0, 4: 0}, 'duration': 38.99979090690613, 'nb_balles_papier': 0.04000009298324585, 'begin_time_port': 'image_07092021_10_23_44_010139m0.jpg 0.011000287055969239 for time 11.000287055969238, id_amount 3 this amount prod time diff : 0.011000287055969239'} Production hashtag (incorrect ponderation at 20-10-18) : 0.04000009298324585 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 6, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 60.00031113624573, 3: 0, 4: 0}, 'duration': 98.99990916252136, 'nb_balles_papier': 0.060000311136245724, 'begin_time_port': 'image_07092021_10_24_34_010156m0.jpg 0.011000226020812989 for time 11.000226020812988, id_amount 3 this amount prod time diff : 0.011000226020812989'} Production hashtag (incorrect ponderation at 20-10-18) : 0.060000311136245724 We filter photos on hashtag condition ! We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 15 list_same_port_ids : [13545772] find same portfolio which already exist 13545772 , we will use it list_same_port_ids : [13545774] find same portfolio which already exist 13545774 , we will use it Catched exception ! Connect or reconnect ! list_same_port_ids : [13545777] find same portfolio which already exist 13545777 , we will use it list_same_port_ids : [5570414] find same portfolio which already exist 5570414 , we will use it list_same_port_ids : [13545779] find same portfolio which already exist 13545779 , we will use it list_same_port_ids : [13545780] find same portfolio which already exist 13545780 , we will use it list_same_port_ids : [13545783] find same portfolio which already exist 13545783 , we will use it list_same_port_ids : [13545785] find same portfolio which already exist 13545785 , we will use it list_same_port_ids : [13545787] find same portfolio which already exist 13545787 , we will use it list_same_port_ids : [13545788] find same portfolio which already exist 13545788 , we will use it list_same_port_ids : [13543473] find same portfolio which already exist 13543473 , we will use it list_same_port_ids : [13543474] find same portfolio which already exist 13543474 , we will use it list_same_port_ids : [13543475] find same portfolio which already exist 13543475 , we will use it list_same_port_ids : [13543476] find same portfolio which already exist 13543476 , we will use it list_same_port_ids : [13543477] find same portfolio which already exist 13543477 , we will use it # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545772 To do Qualite : 0.005814130015432098 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545774 AND mptpi.`type`=4199 To do Qualite : 0.1888521086140681 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545777 To do Qualite : 0.007415846836419753 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5570414 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545779 To do Qualite : 0.004572120949074074 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545780 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545783 To do Qualite : 0.00907640496399177 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545785 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545787 To do Qualite : 0.01485129824918373 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545788 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543473 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543474 To do Qualite : 0.003848153410463827 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543475 AND mptpi.`type`=4199 To do Qualite : 0.11478003563407302 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543476 To do Qualite : 0.019897576026366652 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543477 AND mptpi.`type`=4199 To do elapsed_time : count_nb_balles_and_create_portfolio 822.3374917507172 # DISPLAY ALL COLLECTED DATA : {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}} time spend for datou_step_exec : 847.9359905719757 time spend to save output : 0.0002498626708984375 total time spend for step 1 : 847.9362404346466 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230] Looping around the photos to save general results len do output : 1 /4599398Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318362', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318360', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318358', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318356', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318342', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318339', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318337', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318311', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318310', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318309', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318294', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318293', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318291', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318289', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318288', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318287', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318279', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318276', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318273', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318271', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318268', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318265', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318260', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318257', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318253', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318250', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318247', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318246', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318222', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318219', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318216', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318214', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318213', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318212', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317554', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317551', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317549', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317546', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317542', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317536', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317526', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317525', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317524', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317522', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317520', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317517', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317497', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317493', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317491', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317489', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317487', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317485', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317468', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317461', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317457', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317453', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317444', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317440', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317359', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317333', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317282', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317225', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317210', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049317197', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316790', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316785', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316782', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316778', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316752', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316749', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316610', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316600', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316597', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316594', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316588', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316582', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316545', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316543', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316540', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316537', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316534', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316520', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316338', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316336', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316332', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316331', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316257', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316255', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316222', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316216', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316214', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316212', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316210', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049316209', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049313025', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312984', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312803', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312588', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312585', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312583', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312579', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312574', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312573', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312571', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312568', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312566', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312562', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312556', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312508', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312489', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312488', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312487', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312485', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312484', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312464', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312463', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312462', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312461', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312460', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312449', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312445', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312444', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312442', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312440', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312438', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312429', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312426', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312424', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312422', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312420', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312409', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312406', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312404', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312363', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049312208', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311964', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311963', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311962', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311961', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311960', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311943', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311938', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311937', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311935', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311934', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311932', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311795', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311793', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311791', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311771', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311767', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311267', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311266', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311263', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311252', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311199', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311136', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311073', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311009', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049311006', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310994', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310992', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310991', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310984', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310982', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310981', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310919', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310914', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310911', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310909', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310907', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310905', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310165', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310162', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310159', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310145', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310141', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310139', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310138', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310134', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310132', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309737', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309734', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309732', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309706', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309703', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309701', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309686', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309681', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309677', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309675', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309672', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309670', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309658', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309657', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309656', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309655', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309653', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309651', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309605', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309603', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309599', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309597', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309595', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309592', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309385', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309383', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309382', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309381', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309380', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309379', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309345', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308384', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308381', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308376', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308280', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308276', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308275', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308235', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049307693', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306823', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306804', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306792', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306791', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306635', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306205', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049304810', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049303925', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049296996', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049296121', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049294990', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049293230', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 233 time used for this insertion : 1.4845280647277832 save_final save missing photos in datou_result : After save, about to update current ! Result test rubbia : {'4599398': ([[0, 7, 8, 9, 11, 12, 13, 14, 17, 18], [19, 20, 22, 23, 25, 26, 27, 38, 39, 40, 51, 52, 53, 55, 60, 61, 62, 63, 71, 76, 77, 81, 82, 83, 84], [85, 86, 91, 92], [93], [97, 98, 99, 100, 101], [102], [103, 104], [107, 108, 109, 110, 112, 113, 116, 117, 118, 121, 122, 123], [124, 126, 128, 129, 130, 131, 134, 135], [136, 137, 138, 147, 148, 149, 150, 151, 152, 153, 154, 157, 158, 161, 162, 167, 168], [173, 176, 177, 178, 182, 183, 187, 188], [189, 193], [198, 201, 202, 207, 208, 209, 210, 211, 212, 213, 214, 215], [216, 217, 218, 220], [221, 224, 225, 226, 227, 231]], {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, {13545772: {'list_of_photos': [1049293230, 1049306791, 1049294990, 1049306792, 1049306823, 1049307693, 1049308235, 1049308275, 1049308376, 1049308381], 'hashtag': 'papier'}, 13545774: {'list_of_photos': [1049308384, 1049309345, 1049309380, 1049309381, 1049309383, 1049309385, 1049309592, 1049309658, 1049309670, 1049309672, 1049310132, 1049310134, 1049310138, 1049310141, 1049310905, 1049310907, 1049310909, 1049310911, 1049310994, 1049311199, 1049311252, 1049311767, 1049311771, 1049311791, 1049311793], 'hashtag': 'plastique_fonce'}, 13545777: {'list_of_photos': [1049311795, 1049311932, 1049311943, 1049311960], 'hashtag': 'papier'}, 5570414: {'list_of_photos': [1049311961], 'hashtag': 'plastique_fonce'}, 13545779: {'list_of_photos': [1049312208, 1049312363, 1049312404, 1049312406, 1049312409], 'hashtag': 'papier'}, 13545780: {'list_of_photos': [1049312420], 'hashtag': 'plastique_fonce'}, 13545783: {'list_of_photos': [1049312422, 1049312424], 'hashtag': 'papier'}, 13545785: {'list_of_photos': [1049312438, 1049312440, 1049312442, 1049312444, 1049312449, 1049312460, 1049312463, 1049312464, 1049312484, 1049312488, 1049312489, 1049312508], 'hashtag': 'plastique_fonce'}, 13545787: {'list_of_photos': [1049312556, 1049312566, 1049312571, 1049312573, 1049312574, 1049312579, 1049312588, 1049312803], 'hashtag': 'papier'}, 13545788: {'list_of_photos': [1049312984, 1049313025, 1049316209, 1049316332, 1049316336, 1049316338, 1049316520, 1049316534, 1049316537, 1049316540, 1049316543, 1049316588, 1049316594, 1049316610, 1049316749, 1049316790, 1049317197], 'hashtag': 'plastique_fonce'}, 13543473: {'list_of_photos': [1049317359, 1049317453, 1049317457, 1049317461, 1049317489, 1049317491, 1049317520, 1049317522], 'hashtag': 'aluminium'}, 13543474: {'list_of_photos': [1049317524, 1049317542], 'hashtag': 'tetrapak'}, 13543475: {'list_of_photos': [1049318212, 1049318216, 1049318219, 1049318253, 1049318257, 1049318260, 1049318265, 1049318268, 1049318271, 1049318273, 1049318276, 1049318279], 'hashtag': 'plastique_fonce'}, 13543476: {'list_of_photos': [1049318287, 1049318288, 1049318289, 1049318293], 'hashtag': 'papier'}, 13543477: {'list_of_photos': [1049318294, 1049318311, 1049318337, 1049318339, 1049318342, 1049318362], 'hashtag': 'plastique_fonce'}}, {2107751280: 8, 2107750907: 0, 2107750908: 33, 2107750909: 0, 2107750910: 0, 2107750911: 74, 2107750912: 0, 2107750913: 2}, {'amount_uploaded_and_tagged': {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}}, 'map_all_result_after_group_moy_exp': {'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}, 'map_info_after_moyenne_mobile': {'07092021': {'distrib_time_diff': {'nb': 207, 'mean': 12.512076641626404, 'stddev': 12.296218880583977, 'min': 0.0, 'max': 119.00011491775513, 'quantil_10': {'min': [8.999778032302856], 'max': [11.000291109085083]}, 'quantil_100': {'min': [8.999709129333496], 'max': [61.00024700164795]}, 'quantil_1000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_5000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_10000': {'min': [0.0], 'max': [119.00011491775513]}}, 'time_diff': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 80.99965000152588, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 49.000049114227295, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000201940536499, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 534.9997780323029, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 31.000057697296143, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 564.0013737678528, '05102018_papier_non_papier_tres_peu_dense': 19.999656200408936}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 8.999944925308228, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 89.99986672401428, '05102018_papier_non_papier_tres_peu_dense': 9.999994993209839}}, 'time_diff_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000208854675293, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 158.99993062019348, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 101.00013995170593, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 18.999826192855835, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 168.99926328659058, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 21.00012707710266, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 489.9993727207184, '05102018_papier_non_papier_tres_peu_dense': 11.000169038772583}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 38.99988508224487, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 170.0003685951233, '05102018_papier_non_papier_tres_peu_dense': 0}}, 'nb_photos': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 8, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 6, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 28, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 2, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 40, '05102018_papier_non_papier_tres_peu_dense': 2}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 9, '05102018_papier_non_papier_tres_peu_dense': 1}}, 'nb_photos_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 5, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 2, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 46, '05102018_papier_non_papier_tres_peu_dense': 1}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 4, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 17, '05102018_papier_non_papier_tres_peu_dense': 0}}}}, 'map_amount_per_hashtag': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, 'count': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}})}| Result context_with_local_rubbia.cache_model_config.map_io test rubbia : {'input': {}, 'output': {}}| ############################### TEST rubbia_split_dark ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score_with_photo list_input_json : [] origin We have 1 , BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFFFFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 11.535047769546509 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score_with_photo Sat Mar 8 11:50:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec ----- Debut du copier-coller des param necessaire pour fonction main de STS ----- TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('18', 4), ('19', 5), ('20', 5), ('24', 8), ('26', 6), ('17', 1), ('27', 9), ('51', 7), ('28', 2), ('21', 4), ('52', 2), ('25', 6), ('50', 3), ('22', 2)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 06102021 4608689 Nombre de photos uploadées : 64 / 23040 (0%) 06102021 4608689 Nombre de photos taguées (types de déchets): 0 / 64 (0%) 06102021 4608689 Nombre de photos taguées (volume) : 0 / 64 (0%) elapsed_time : load_data_split_time_score 5.0067901611328125e-06 elapsed_time : order_list_meta_photo_and_scores 0.0002677440643310547 ???????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.010511636734008789 elapsed_time : insert_dashboard_record_day_entry 0.1562328338623047 ***** BEGIN SPLIT BY DARK ***** To DO 08/10/21 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 2.744154453277588 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 2 time used for this insertion : 0.0899202823638916 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 1.6072356700897217 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 4 time used for this insertion : 3.3229801654815674 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 5 time used for this insertion : 2.0262532234191895 elapsed_time : SPLIT_BY_DARK 10.870973348617554 ***** END SPLIT BY DARK ***** ((1055001085,), (1055008638,), (1055010730,), (1055011086,), (1055012686,)) ***** BEGIN SPLIT TIME ***** False [12, 20, 29, 38, 51] 1633478400.0 `1633508253.0 1633507200.0 `1633508284.0 1633507200.0 `1633508316.0 1633507200.0 `1633508323.0 1633507200.0 `1633508333.0 1633507200.0 `1633508340.0 1633507200.0 `1633508358.0 1633507200.0 `1633508369.0 1633507200.0 `1633508385.0 1633507200.0 `1633508398.0 1633507200.0 `1633508416.0 1633507200.0 `1633508425.0 1633507200.0 `1633508434.0 1633507200.0 `1633508449.0 1633507200.0 `1633508458.0 1633507200.0 `1633508470.0 1633507200.0 `1633508491.0 1633507200.0 `1633508499.0 1633507200.0 `1633508508.0 1633507200.0 `1633508521.0 1633507200.0 `1633508524.0 1633507200.0 `1633508644.0 1633507200.0 `1633508652.0 1633507200.0 `1633508657.0 1633507200.0 `1633508669.0 1633507200.0 `1633508678.0 1633507200.0 `1633508684.0 1633507200.0 `1633508690.0 1633507200.0 `1633508696.0 1633507200.0 `1633508701.0 1633507200.0 `1633508725.0 1633507200.0 `1633508733.0 1633507200.0 `1633508739.0 1633507200.0 `1633508748.0 1633507200.0 `1633508759.0 1633507200.0 `1633508766.0 1633507200.0 `1633508772.0 1633507200.0 `1633508778.0 1633507200.0 `1633508781.0 1633507200.0 `1633508805.0 1633507200.0 `1633508813.0 1633507200.0 `1633508820.0 1633507200.0 `1633508824.0 1633507200.0 `1633508829.0 1633507200.0 `1633508833.0 1633507200.0 `1633508849.0 1633507200.0 `1633508858.0 1633507200.0 `1633508865.0 1633507200.0 `1633508871.0 1633507200.0 `1633508877.0 1633507200.0 `1633508886.0 1633507200.0 `1633508891.0 1633507200.0 `1633524639.0 1633521600.0 `1633524648.0 1633521600.0 `1633524657.0 1633521600.0 `1633524662.0 1633521600.0 `1633524675.0 1633521600.0 `1633524680.0 1633521600.0 `1633524683.0 1633521600.0 `1633524694.0 1633521600.0 `1633524700.0 1633521600.0 `1633524718.0 1633521600.0 `1633524722.0 1633521600.0 `1633524741.0 1633521600.0 list printed: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.014179706573486328 ***** END SPLIT TIME ***** NUMBER BATCH : 7 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['refus', 'jrm'] ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 172.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_101733.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 72.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102049.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 52.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102404.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 53.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102525.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 81.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102645.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Empty batch, bug or could have been filtered ! ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 102.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_145039.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 6 list_same_port_ids : [4938484] find same portfolio which already exist 4938484 , we will use it list_same_port_ids : [4938485] find same portfolio which already exist 4938485 , we will use it list_same_port_ids : [4938486] find same portfolio which already exist 4938486 , we will use it list_same_port_ids : [4938487] find same portfolio which already exist 4938487 , we will use it list_same_port_ids : [4938488] find same portfolio which already exist 4938488 , we will use it list_same_port_ids : [4756245] find same portfolio which already exist 4756245 , we will use it Qualite : 0.049377889059021136 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938484 AND mptpi.`type`=4038 To do Qualite : 0.051267724965616025 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938485 AND mptpi.`type`=4038 To do Qualite : 0.06573670331612967 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938486 AND mptpi.`type`=4038 To do Qualite : 0.12804353375222421 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938487 AND mptpi.`type`=4038 To do Qualite : 0.07355022343618775 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938488 AND mptpi.`type`=4038 To do Qualite : 0.10644996740301908 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4756245 AND mptpi.`type`=4038 To do elapsed_time : count_nb_balles_and_create_portfolio 8.151649475097656 # DISPLAY ALL COLLECTED DATA : {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} ------ Fin du Copier-Coller ------ ---------- ONE RESULT --------- ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}) ---------- END de ONE RESULT ---------- Suppression des photos Telecharges time spend for datou_step_exec : 27.888275623321533 time spend to save output : 0.0009920597076416016 total time spend for step 1 : 27.889267683029175 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score_with_photo we use saveGeneral [1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441] Looping around the photos to save general results len do output : 5 /[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]] /{'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]} /{4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}} /{2107757407: 59} /{'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}} before output type Managing all output in save final without adding information in the mtr_datou_result ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003131', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055002045', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001545', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001542', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001092', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001085', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000228', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000070', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000068', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000063', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000059', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000055', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008597', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008184', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008181', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007992', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007953', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007950', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004798', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004627', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004608', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004600', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004278', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004217', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003679', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011086', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011076', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011074', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011072', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010743', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010739', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010737', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010730', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010725', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010723', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010143', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008638', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008599', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003357', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003348', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003292', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003278', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003266', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003261', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003259', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003249', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003202', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003198', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003197', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003185', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003134', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013727', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013724', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013693', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012727', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012722', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012686', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012684', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011740', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011733', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011726', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011459', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011454', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011441', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 64 time used for this insertion : 0.0821540355682373 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}) Result test split dark : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}})| ############################### TEST rubbia_append ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 3.4852917194366455 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Sat Mar 8 11:51:04 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('30', 1), ('36', 1), ('43', 1)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21092021 4599006 Nombre de photos uploadées : 3 / 23040 (0%) 21092021 4599006 Nombre de photos taguées (types de déchets): 0 / 3 (0%) 21092021 4599006 Nombre de photos taguées (volume) : 0 / 3 (0%) elapsed_time : load_data_split_time_score 6.67572021484375e-06 elapsed_time : order_list_meta_photo_and_scores 1.2636184692382812e-05 ??? elapsed_time : fill_and_build_computed_from_old_data 0.00043320655822753906 elapsed_time : insert_dashboard_record_day_entry 0.3593719005584717 ---------- APPEND TASK BEGIN ---------- ---------- APPEND TASK END ---------- We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21092021': {'nb_upload': 3, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('12', 1), ('-0', 3)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21092021 4505992 Nombre de photos uploadées : 1 / 23040 (0%) 21092021 4505992 Nombre de photos taguées (types de déchets): 0 / 1 (0%) 21092021 4505992 Nombre de photos taguées (volume) : 0 / 1 (0%) elapsed_time : load_data_split_time_score 7.867813110351562e-06 elapsed_time : order_list_meta_photo_and_scores 1.2159347534179688e-05 ? elapsed_time : fill_and_build_computed_from_old_data 0.0006351470947265625 elapsed_time : insert_dashboard_record_day_entry 0.043351173400878906 ---------- APPEND TASK BEGIN ---------- ---------- APPEND TASK END ---------- We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4505992_21-09-2021.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4505992 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21092021': {'nb_upload': 1, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} time spend for datou_step_exec : 4.470231056213379 time spend to save output : 4.935264587402344e-05 total time spend for step 1 : 4.470280408859253 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1054572537, 1054572534, 1054572532, 1051605195] Looping around the photos to save general results len do output : 2 /4599006Didn't retrieve data . /4505992Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572537', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572534', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572532', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4505992', '1051605195', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.15883398056030273 save_final save missing photos in datou_result : After save, about to update current ! ############################### TEST rubbia_horaire ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 1.804213523864746 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Sat Mar 8 11:51:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('02', 6), ('05', 8), ('06', 19)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 08032021 3609515 Nombre de photos uploadées : 33 / 23040 (0%) 08032021 3609515 Nombre de photos taguées (types de déchets): 0 / 33 (0%) 08032021 3609515 Nombre de photos taguées (volume) : 0 / 33 (0%) elapsed_time : load_data_split_time_score 1.239776611328125e-05 elapsed_time : order_list_meta_photo_and_scores 1.8358230590820312e-05 ????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.003261566162109375 elapsed_time : insert_dashboard_record_day_entry 0.14437580108642578 Creating list_photo_total elapsed_time : select_descriptors 0.2228710651397705 08032021 3609515 Nombre de photos avec descriptors (type 3963) : 0 / 33 (0%) Missing descriptors for photos 0 and 1014054233 0:00:00|ON:Missing descriptors for photos 1014054233 and 1014054232 Missing descriptors for photos 1014054232 and 1014054231 Missing descriptors for photos 1014054231 and 1014054230 Missing descriptors for photos 1014054230 and 1014054235 Missing descriptors for photos 1014054235 and 1014054234 Missing descriptors for photos 1014054234 and 1014097492 Missing descriptors for photos 1014097492 and 1014097499 Missing descriptors for photos 1014097499 and 1014097497 Missing descriptors for photos 1014097497 and 1014097580 Missing descriptors for photos 1014097580 and 1014097924 Missing descriptors for photos 1014097924 and 1014098236 Missing descriptors for photos 1014098236 and 1014098602 Missing descriptors for photos 1014098602 and 1014099035 Missing descriptors for photos 1014099035 and 1014105778 Missing descriptors for photos 1014105778 and 1014105777 Missing descriptors for photos 1014105777 and 1014105784 Missing descriptors for photos 1014105784 and 1014105783 Missing descriptors for photos 1014105783 and 1014105782 Missing descriptors for photos 1014105782 and 1014105781 Missing descriptors for photos 1014105781 and 1014105786 Missing descriptors for photos 1014105786 and 1014105785 Missing descriptors for photos 1014105785 and 1014105791 Missing descriptors for photos 1014105791 and 1014105790 Missing descriptors for photos 1014105790 and 1014105798 Missing descriptors for photos 1014105798 and 1014105797 Missing descriptors for photos 1014105797 and 1014105796 Missing descriptors for photos 1014105796 and 1014105795 Missing descriptors for photos 1014105795 and 1014105800 Missing descriptors for photos 1014105800 and 1014105799 Missing descriptors for photos 1014105799 and 1014106095 Missing descriptors for photos 1014106095 and 1014106094 Missing descriptors for photos 1014106094 and 1014106093 08032021 Removing 0 photos because of the 'same image' condition Total on : 0 Total off : 0.0 list_time_off Warning in study_and_display_distrib_list : min=max : 0.0 0.0 dist_desc Warning in study_and_display_distrib_list : min=max : -1 -1 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 33 Catched exception ! Connect or reconnect ! time used for this insertion : 5.972604513168335 photos_removed : len 0 elapsed_time : remove_photo_duplicate 6.008451461791992 To do, maybe not at the correct place ! .................................force hashtag to JRM elapsed_time : CREATE_PORT_BATCH_BY_HOUR 0.008452653884887695 NUMBER BATCH : 3 list_ponderation used : [1e-05, 1e-05, 1e-05, 1e-05, 1e-05] , list_hashtag_class_create_as_list : ['jrm'] We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 0 elapsed_time : count_nb_balles_and_create_portfolio 0.029201984405517578 # DISPLAY ALL COLLECTED DATA : {'08032021': {'nb_upload': 33, 'nb_taggue_class': 0, 'nb_taggue_densite': 0, 'nb_descriptors': 0}} time spend for datou_step_exec : 7.280999422073364 time spend to save output : 5.2928924560546875e-05 total time spend for step 1 : 7.281052350997925 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230] Looping around the photos to save general results len do output : 1 /3609515Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106095', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106094', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106093', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105800', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105799', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105798', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105797', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105796', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105795', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105791', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105790', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105786', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105785', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105784', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105783', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105782', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105781', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105778', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105777', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014099035', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014098602', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014098236', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097924', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097580', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097499', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097497', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097492', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054235', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054234', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054233', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054232', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054231', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054230', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 34 time used for this insertion : 0.10170674324035645 save_final save missing photos in datou_result : After save, about to update current ! got : {'Rungis_JRM': []} expected : {'Rungis_JRM': [(0, 1), (1, 2), (2, 3)]} ERROR rubbia_horaire FAILED ############################### TEST rle_unique_nms_with_priority ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.689429521560669 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Sat Mar 8 11:51:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 10 chid ids of type : 2804 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 10 nb_hashtags : 2 time to prepare the origin masks : 3.7508888244628906 time for calcul the mask position with numpy : 0.007512569427490234 nb_pixel_total : 217207 time to create 1 rle with new method : 0.6661763191223145 time for calcul the mask position with numpy : 0.004176139831542969 nb_pixel_total : 1008 time to create 1 rle with old method : 0.002991199493408203 time for calcul the mask position with numpy : 0.004010915756225586 nb_pixel_total : 751 time to create 1 rle with old method : 0.0017390251159667969 time for calcul the mask position with numpy : 0.00475001335144043 nb_pixel_total : 722 time to create 1 rle with old method : 0.0017108917236328125 time for calcul the mask position with numpy : 0.0036017894744873047 nb_pixel_total : 2949 time to create 1 rle with old method : 0.011103391647338867 time for calcul the mask position with numpy : 0.003944873809814453 nb_pixel_total : 497 time to create 1 rle with old method : 0.0011949539184570312 time for calcul the mask position with numpy : 0.004272937774658203 nb_pixel_total : 1086 time to create 1 rle with old method : 2.387364387512207 time for calcul the mask position with numpy : 0.02793264389038086 nb_pixel_total : 1924 time to create 1 rle with old method : 0.006505727767944336 time for calcul the mask position with numpy : 0.003622770309448242 nb_pixel_total : 413 time to create 1 rle with old method : 0.0010302066802978516 time for calcul the mask position with numpy : 0.003894805908203125 nb_pixel_total : 526 time to create 1 rle with old method : 0.0012242794036865234 create new chi : 3.1509227752685547 time to delete rle : 0.036180973052978516 batch 1 Loaded 10 chid ids of type : 2805 Number RLEs to save : 1674 TO DO : save crop sub photo not yet done ! save time : 1.9788689613342285 map_output_result : {998957128: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms time spend for datou_step_exec : 17.18630313873291 time spend to save output : 0.00045013427734375 total time spend for step 1 : 17.186753273010254 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {998957128: (0.0, 'Should be the crop_list due to order', 0)} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.3989732265472412 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Sat Mar 8 11:51:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 10 chid ids of type : 4169 seulement à utiliser dans la step consolidation batch 1 Loaded 10 chid ids of type : 2805 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 1.2360200881958008 map_output_result : {1066511071: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms time spend for datou_step_exec : 5.093925714492798 time spend to save output : 8.082389831542969e-05 total time spend for step 1 : 5.094006538391113 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1066511071: (0.0, 'Should be the crop_list due to order', 0)} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.49433159828186035 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Sat Mar 8 11:51:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 91 chid ids of type : 2596 +++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 43 nb_hashtags : 2 time to prepare the origin masks : 27.853745698928833 time for calcul the mask position with numpy : 0.32954835891723633 nb_pixel_total : 5233657 time to create 1 rle with new method : 0.9904534816741943 time for calcul the mask position with numpy : 0.03203725814819336 nb_pixel_total : 11972 time to create 1 rle with old method : 0.026133298873901367 time for calcul the mask position with numpy : 0.03198862075805664 nb_pixel_total : 15054 time to create 1 rle with old method : 0.033621788024902344 time for calcul the mask position with numpy : 0.03190469741821289 nb_pixel_total : 13954 time to create 1 rle with old method : 0.030712604522705078 time for calcul the mask position with numpy : 0.033249855041503906 nb_pixel_total : 4888 time to create 1 rle with old method : 0.010904073715209961 time for calcul the mask position with numpy : 0.0511775016784668 nb_pixel_total : 1188492 time to create 1 rle with new method : 0.7432148456573486 time for calcul the mask position with numpy : 0.034800052642822266 nb_pixel_total : 184585 time to create 1 rle with new method : 1.2519493103027344 time for calcul the mask position with numpy : 0.03543448448181152 nb_pixel_total : 18620 time to create 1 rle with old method : 0.0417170524597168 time for calcul the mask position with numpy : 0.03341484069824219 nb_pixel_total : 62945 time to create 1 rle with old method : 0.14345049858093262 time for calcul the mask position with numpy : 0.049933433532714844 nb_pixel_total : 9427 time to create 1 rle with old method : 0.023040294647216797 time for calcul the mask position with numpy : 0.03465557098388672 nb_pixel_total : 9081 time to create 1 rle with old method : 0.02594161033630371 time for calcul the mask position with numpy : 0.08275270462036133 nb_pixel_total : 15987 time to create 1 rle with old method : 0.09732556343078613 time for calcul the mask position with numpy : 0.04614543914794922 nb_pixel_total : 33276 time to create 1 rle with old method : 0.09773015975952148 time for calcul the mask position with numpy : 0.05866241455078125 nb_pixel_total : 17533 time to create 1 rle with old method : 0.08161234855651855 time for calcul the mask position with numpy : 0.05342388153076172 nb_pixel_total : 4876 time to create 1 rle with old method : 0.017796039581298828 time for calcul the mask position with numpy : 0.047353506088256836 nb_pixel_total : 25226 time to create 1 rle with old method : 0.0959925651550293 time for calcul the mask position with numpy : 0.04820561408996582 nb_pixel_total : 30773 time to create 1 rle with old method : 0.1392359733581543 time for calcul the mask position with numpy : 0.035481929779052734 nb_pixel_total : 65671 time to create 1 rle with old method : 0.1690516471862793 time for calcul the mask position with numpy : 0.04356837272644043 nb_pixel_total : 12230 time to create 1 rle with old method : 0.038192033767700195 time for calcul the mask position with numpy : 0.05619502067565918 nb_pixel_total : 29560 time to create 1 rle with old method : 0.10608029365539551 time for calcul the mask position with numpy : 0.03763985633850098 nb_pixel_total : 14310 time to create 1 rle with old method : 0.033963680267333984 time for calcul the mask position with numpy : 0.03515148162841797 nb_pixel_total : 15117 time to create 1 rle with old method : 0.03780794143676758 time for calcul the mask position with numpy : 0.03954577445983887 nb_pixel_total : 301487 time to create 1 rle with new method : 1.5835793018341064 time for calcul the mask position with numpy : 0.032334089279174805 nb_pixel_total : 29821 time to create 1 rle with old method : 0.06648874282836914 time for calcul the mask position with numpy : 0.0323786735534668 nb_pixel_total : 40299 time to create 1 rle with old method : 0.08776021003723145 time for calcul the mask position with numpy : 0.034850358963012695 nb_pixel_total : 12680 time to create 1 rle with old method : 0.030798673629760742 time for calcul the mask position with numpy : 0.03282880783081055 nb_pixel_total : 9449 time to create 1 rle with old method : 0.02149057388305664 time for calcul the mask position with numpy : 0.033263444900512695 nb_pixel_total : 15168 time to create 1 rle with old method : 0.03432583808898926 time for calcul the mask position with numpy : 0.03311491012573242 nb_pixel_total : 11140 time to create 1 rle with old method : 0.02587413787841797 time for calcul the mask position with numpy : 0.03317737579345703 nb_pixel_total : 29065 time to create 1 rle with old method : 0.06356477737426758 time for calcul the mask position with numpy : 0.03242301940917969 nb_pixel_total : 22774 time to create 1 rle with old method : 0.05147385597229004 time for calcul the mask position with numpy : 0.032266855239868164 nb_pixel_total : 13880 time to create 1 rle with old method : 0.029795169830322266 time for calcul the mask position with numpy : 0.03434920310974121 nb_pixel_total : 155366 time to create 1 rle with new method : 0.6592581272125244 time for calcul the mask position with numpy : 0.03211832046508789 nb_pixel_total : 63941 time to create 1 rle with old method : 0.14805388450622559 time for calcul the mask position with numpy : 0.042185306549072266 nb_pixel_total : 7836 time to create 1 rle with old method : 0.018946409225463867 time for calcul the mask position with numpy : 0.03224587440490723 nb_pixel_total : 7460 time to create 1 rle with old method : 0.01733851432800293 time for calcul the mask position with numpy : 0.03181910514831543 nb_pixel_total : 44600 time to create 1 rle with old method : 0.10321855545043945 time for calcul the mask position with numpy : 0.03632545471191406 nb_pixel_total : 11879 time to create 1 rle with old method : 0.02734065055847168 time for calcul the mask position with numpy : 0.035215139389038086 nb_pixel_total : 44195 time to create 1 rle with old method : 0.0960080623626709 time for calcul the mask position with numpy : 0.044115543365478516 nb_pixel_total : 23652 time to create 1 rle with old method : 0.05392003059387207 time for calcul the mask position with numpy : 0.03146719932556152 nb_pixel_total : 30006 time to create 1 rle with old method : 0.06495451927185059 time for calcul the mask position with numpy : 0.031185626983642578 nb_pixel_total : 15880 time to create 1 rle with old method : 0.03567838668823242 time for calcul the mask position with numpy : 0.033274173736572266 nb_pixel_total : 29845 time to create 1 rle with old method : 0.06984472274780273 time for calcul the mask position with numpy : 0.03575468063354492 nb_pixel_total : 144263 time to create 1 rle with old method : 0.31473875045776367 create new chi : 10.009405374526978 time to delete rle : 0.7063734531402588 batch 1 Loaded 44 chid ids of type : 2805 Number RLEs to save : 27884 TO DO : save crop sub photo not yet done ! save time : 38.95206356048584 map_output_result : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)} End step rle-unique-nms time spend for datou_step_exec : 82.80624437332153 time spend to save output : 0.012261390686035156 total time spend for step 1 : 82.81850576400757 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)} batch 1 Loaded 54 chid ids of type : 2805 ############################### TEST random_deformation ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : random_deformation list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.738095760345459 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:random_deformation Sat Mar 8 11:58:55 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of datou step random deformation get user info for portfolio 3288640 About to upload 4 photos upload in portfolio : 3287159 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741431543_4154994 we have uploaded 4 photos in the portfolio 3287159 time of upload the photos Elapsed time : 3.375035047531128 time spend for datou_step_exec : 8.997082710266113 time spend to save output : 9.441375732421875e-05 total time spend for step 1 : 8.997177124023438 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : random_deformation we use saveGeneral [1006293201] Looping around the photos to save general results len do output : 4 /1341838083Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341838084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341838085Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341838086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('2896', None, None, None, None, None, None, None, None) ('2896', '3288640', '1006293201', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.01709890365600586 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1341838083: ['1006293201', 'temp/1006293201_random_deformation_0.png', []], 1341838084: ['1006293201', 'temp/1006293201_random_deformation_1.png', []], 1341838085: ['1006293201', 'temp/1006293201_random_deformation_2.png', []], 1341838086: ['1006293201', 'temp/1006293201_random_deformation_3.png', []]} name 'urllib' is not defined can't unload the photo : 1006293201 t ############################### TEST tile ################################ test tile avec chi rectangles, rles, polygones Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : tile list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 1.1563949584960938 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:tile Sat Mar 8 11:59:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec verbose : False param_json : {'ETA': 3600, 'new_width': 640, 'new_height': 640, 'token': '5d93a4b2b749464f208c339a1324b78f', 'stride': 0, 'stride_relative': 0, 'portfolio_name': 'results_test_tile', 'crop_hashtag_type_tiled': 3243, 'crop_hashtag_type': 3242, 'arg_aux_upload': {'type_upload': 'python'}, 'host': 'www.fotonower.com'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {3242: 3243} TO DEPRECATE VR 14-6-18 map_filenames : {1008283903: 'temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 Loaded 2 chid ids of type : 3242 ++https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=results_test_tile&access_token=5d93a4b2b749464f208c339a1324b78f created feed_id_new_photos : 21178537 with name results_test_tile feed_id_new_photos : 21178537 filename : temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg photo_id : 1008283903 height_image_input : 2464 width_image_input : 3280 new_width : 640 new_height : 640 stride : 0 stride_relative : 0 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 2 list_bib_to_crops : 24 [(0, 640, 0, 640, 0), (0, 640, 640, 1280, 1), (0, 640, 1280, 1920, 2), (0, 640, 1824, 2464, 3), (640, 1280, 0, 640, 4), (640, 1280, 640, 1280, 5), (640, 1280, 1280, 1920, 6), (640, 1280, 1824, 2464, 7), (1280, 1920, 0, 640, 8), (1280, 1920, 640, 1280, 9), (1280, 1920, 1280, 1920, 10), (1280, 1920, 1824, 2464, 11), (1920, 2560, 0, 640, 12), (1920, 2560, 640, 1280, 13), (1920, 2560, 1280, 1920, 14), (1920, 2560, 1824, 2464, 15), (2560, 3200, 0, 640, 16), (2560, 3200, 640, 1280, 17), (2560, 3200, 1280, 1920, 18), (2560, 3200, 1824, 2464, 19), (2640, 3280, 0, 640, 20), (2640, 3280, 640, 1280, 21), (2640, 3280, 1280, 1920, 22), (2640, 3280, 1824, 2464, 23)] new_crops_tiles : 24 crop_transformed : 7 batch 1 Loaded 24 chid ids of type : 17 treat the image : temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 before upload mediasElapsed time : 0.4942514896392822 on upload les photos avec python init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741431736_4154994 we have uploaded 24 photos in the portfolio 21178537 Importing ! upload mediasElapsed time : 8.599761247634888 , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23Saving 7 CHIs. batch 1 Loaded 7 chid ids of type : 3243 Number RLEs to save : 2937 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 12.867473840713501 time spend for datou_step_exec : 194.79138827323914 time spend to save output : 0.00039577484130859375 total time spend for step 1 : 194.79178404808044 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1341838913': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg'], '1341838914': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg'], '1341838915': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg'], '1341838916': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg'], '1341838918': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg'], '1341838919': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg'], '1341838920': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg'], '1341838921': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg'], '1341838922': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg'], '1341838923': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg'], '1341838924': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg'], '1341838925': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg'], '1341838926': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg'], '1341838927': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg'], '1341838928': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg'], '1341838929': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg'], '1341838930': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg'], '1341838931': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg'], '1341838932': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg'], '1341838933': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg'], '1341838934': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg'], '1341838935': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg'], '1341838936': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg'], '1341838937': ['temp/1741431553_4154994_1008283903_6d008d31a1477b2e98cbafa96bd48e53_23.jpg']} batch 1 Loaded 7 chid ids of type : 3243 ++++++++++++++fin du test de tile ############################### TEST rotate_chi ################################ test rotate avec chi rectangles, rles, polygones Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.7906098365783691 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rotate Sat Mar 8 12:02:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a linear step without datou_depend ! rotate photos of 0,90,180,270 degres batch 1 Loaded 16 chid ids of type : 3086 ++++++++++++++++ map_chi of length : 1 https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 21178554 time for calcul the mask position with numpy : 0.1316530704498291 nb_pixel_total : 110633 time to create 1 rle with old method : 0.2623474597930908 .time for calcul the mask position with numpy : 0.016828298568725586 nb_pixel_total : 15826 time to create 1 rle with old method : 0.04387259483337402 .time for calcul the mask position with numpy : 0.011026859283447266 nb_pixel_total : 5286 time to create 1 rle with old method : 0.01321101188659668 .time for calcul the mask position with numpy : 0.012722969055175781 nb_pixel_total : 1633 time to create 1 rle with old method : 0.003802061080932617 .time for calcul the mask position with numpy : 0.012639522552490234 nb_pixel_total : 105533 time to create 1 rle with old method : 0.24329018592834473 .time for calcul the mask position with numpy : 0.012502193450927734 nb_pixel_total : 4393 time to create 1 rle with old method : 0.009905815124511719 .time for calcul the mask position with numpy : 0.012310981750488281 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015850067138671875 .time for calcul the mask position with numpy : 0.03107762336730957 nb_pixel_total : 62627 time to create 1 rle with old method : 0.13609933853149414 .time for calcul the mask position with numpy : 0.022115707397460938 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07282900810241699 .time for calcul the mask position with numpy : 0.026185274124145508 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08690023422241211 .time for calcul the mask position with numpy : 0.028644084930419922 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11626148223876953 .time for calcul the mask position with numpy : 0.2510101795196533 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.17822527885437012 .time for calcul the mask position with numpy : 0.011253833770751953 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005428791046142578 .time for calcul the mask position with numpy : 0.01147770881652832 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005543708801269531 .time for calcul the mask position with numpy : 0.013278961181640625 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007050275802612305 .time for calcul the mask position with numpy : 0.012252330780029297 nb_pixel_total : 1662 time to create 1 rle with old method : 0.00423884391784668 .Needs to change image size ! time for calcul the mask position with numpy : 0.013488054275512695 nb_pixel_total : 110633 time to create 1 rle with old method : 0.2726898193359375 .time for calcul the mask position with numpy : 0.010238885879516602 nb_pixel_total : 15826 time to create 1 rle with old method : 0.035532236099243164 .time for calcul the mask position with numpy : 0.009871482849121094 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012453794479370117 .time for calcul the mask position with numpy : 0.011296987533569336 nb_pixel_total : 1633 time to create 1 rle with old method : 0.004204273223876953 .time for calcul the mask position with numpy : 0.011179924011230469 nb_pixel_total : 105533 time to create 1 rle with old method : 0.2265169620513916 .time for calcul the mask position with numpy : 0.012164831161499023 nb_pixel_total : 4393 time to create 1 rle with old method : 0.013890743255615234 .time for calcul the mask position with numpy : 0.012301445007324219 nb_pixel_total : 632 time to create 1 rle with old method : 0.0016245841979980469 .time for calcul the mask position with numpy : 0.012647151947021484 nb_pixel_total : 62627 time to create 1 rle with old method : 0.1378927230834961 .time for calcul the mask position with numpy : 0.014965057373046875 nb_pixel_total : 33681 time to create 1 rle with old method : 0.0762174129486084 .time for calcul the mask position with numpy : 0.011574268341064453 nb_pixel_total : 37724 time to create 1 rle with old method : 0.0838625431060791 .time for calcul the mask position with numpy : 0.009868621826171875 nb_pixel_total : 48775 time to create 1 rle with old method : 0.10831260681152344 .time for calcul the mask position with numpy : 0.04458427429199219 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.16236352920532227 .time for calcul the mask position with numpy : 0.010365486145019531 nb_pixel_total : 2310 time to create 1 rle with old method : 0.0055272579193115234 .time for calcul the mask position with numpy : 0.010099172592163086 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005967378616333008 .time for calcul the mask position with numpy : 0.009796619415283203 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0075337886810302734 .time for calcul the mask position with numpy : 0.009706258773803711 nb_pixel_total : 1662 time to create 1 rle with old method : 0.006579160690307617 .time for calcul the mask position with numpy : 0.010841846466064453 nb_pixel_total : 110633 time to create 1 rle with old method : 0.3055579662322998 .time for calcul the mask position with numpy : 0.011099815368652344 nb_pixel_total : 15826 time to create 1 rle with old method : 0.035660505294799805 .time for calcul the mask position with numpy : 0.010561227798461914 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012912511825561523 .time for calcul the mask position with numpy : 0.010029077529907227 nb_pixel_total : 1633 time to create 1 rle with old method : 0.003905773162841797 .time for calcul the mask position with numpy : 0.011221170425415039 nb_pixel_total : 105533 time to create 1 rle with old method : 0.2520408630371094 .time for calcul the mask position with numpy : 0.011803388595581055 nb_pixel_total : 4393 time to create 1 rle with old method : 0.012596607208251953 .time for calcul the mask position with numpy : 0.010461807250976562 nb_pixel_total : 632 time to create 1 rle with old method : 0.001697540283203125 .time for calcul the mask position with numpy : 0.010659933090209961 nb_pixel_total : 62627 time to create 1 rle with old method : 0.1452479362487793 .time for calcul the mask position with numpy : 0.011484861373901367 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07411551475524902 .time for calcul the mask position with numpy : 0.012342453002929688 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08819460868835449 .time for calcul the mask position with numpy : 0.012103080749511719 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11315417289733887 .time for calcul the mask position with numpy : 0.05080819129943848 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.178938627243042 .time for calcul the mask position with numpy : 0.012239933013916016 nb_pixel_total : 2310 time to create 1 rle with old method : 0.006356477737426758 .time for calcul the mask position with numpy : 0.01082158088684082 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005818367004394531 .time for calcul the mask position with numpy : 0.009662866592407227 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007317304611206055 .time for calcul the mask position with numpy : 0.01131749153137207 nb_pixel_total : 1662 time to create 1 rle with old method : 0.00516510009765625 .Needs to change image size ! time for calcul the mask position with numpy : 0.012429475784301758 nb_pixel_total : 110633 time to create 1 rle with old method : 0.25328779220581055 .time for calcul the mask position with numpy : 0.010477304458618164 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03811836242675781 .time for calcul the mask position with numpy : 0.010196924209594727 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012911558151245117 .time for calcul the mask position with numpy : 0.010145187377929688 nb_pixel_total : 1633 time to create 1 rle with old method : 0.004160881042480469 .time for calcul the mask position with numpy : 0.010543346405029297 nb_pixel_total : 105533 time to create 1 rle with old method : 0.23569321632385254 .time for calcul the mask position with numpy : 0.009705066680908203 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010341882705688477 .time for calcul the mask position with numpy : 0.011274576187133789 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015773773193359375 .time for calcul the mask position with numpy : 0.011922836303710938 nb_pixel_total : 62627 time to create 1 rle with old method : 0.15043044090270996 .time for calcul the mask position with numpy : 0.009890317916870117 nb_pixel_total : 33681 time to create 1 rle with old method : 0.08389115333557129 .time for calcul the mask position with numpy : 0.011431694030761719 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08449816703796387 .time for calcul the mask position with numpy : 0.011866331100463867 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11156249046325684 .time for calcul the mask position with numpy : 0.05494070053100586 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.5478999614715576 .time for calcul the mask position with numpy : 0.009944677352905273 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005462169647216797 .time for calcul the mask position with numpy : 0.010653972625732422 nb_pixel_total : 2256 time to create 1 rle with old method : 0.009029150009155273 .time for calcul the mask position with numpy : 0.01009678840637207 nb_pixel_total : 3112 time to create 1 rle with old method : 0.00821065902709961 .time for calcul the mask position with numpy : 0.011925697326660156 nb_pixel_total : 1662 time to create 1 rle with old method : 0.004358053207397461 . About to upload 4 photos upload in portfolio : 21178554 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741431772_4154994 we have uploaded 4 photos in the portfolio 21178554 time of upload the photos Elapsed time : 1.6603550910949707 Len new_chis : 4 Len list_new_chi_with_photo_id : 64 of type : 3230 batch 1 Loaded 64 chid ids of type : 3230 Number RLEs to save : 24654 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 52.4280264377594 time spend to save output : 0.005908012390136719 total time spend for step 1 : 52.433934450149536 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1341838980: ['1003369118', 'temp/1741431748_4154994_1003369118_58171420504d0b5f05a1233b6c515509_658263370.jpg', [, , , , , , , , , , , , , , , ]], 1341838981: ['1003369118', 'temp/1741431748_4154994_1003369118_58171420504d0b5f05a1233b6c515509_6582633790.jpg', [, , , , , , , , , , , , , , , ]], 1341838982: ['1003369118', 'temp/1741431748_4154994_1003369118_58171420504d0b5f05a1233b6c515509_65826337180.jpg', [, , , , , , , , , , , , , , , ]], 1341838983: ['1003369118', 'temp/1741431748_4154994_1003369118_58171420504d0b5f05a1233b6c515509_65826337270.jpg', [, , , , , , , , , , , , , , , ]]} batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++fin du test de rotate_chi Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21) name : rubbia_carac_pet_clair_0121 not run because too long ############################### TEST rubbia_carac_pet_clair_0121_no_cnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 6479 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 6480 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7445 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 6509 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 6479 doesn't seem to be define in the database( WARNING : type of input 1 of step 6480 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 6479 doesn't seem to be define in the database( WARNING : type of input 1 of step 7445 doesn't seem to be define in the database( WARNING : type of output 1 of step 7445 doesn't seem to be define in the database( WARNING : type of input 3 of step 6509 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! List Step Type Loaded in datou : merge_mask_thcl_custom, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 1.2403168678283691 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 4 step1:merge_mask_thcl_custom Sat Mar 8 12:03:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step merge_mask_thcl_custom batch 1 Loaded 82 chid ids of type : 2800 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++As expected we have just one thcl present begin to find the sub_photo_id : begin to find the sub_photo_id : batch 1 Loaded 76 chid ids of type : 2913 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 76 chid ids of type : 2913 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++End of step merge_mask_thcl_custom time spend for datou_step_exec : 135.80691695213318 time spend to save output : 0.039351463317871094 total time spend for step 1 : 135.84626841545105 step2:rle_unique_nms_with_priority Sat Mar 8 12:05:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 43 chid ids of type : 2913 +++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.770836114883423 create new chi : 0.041428327560424805 time to delete rle : 0.03735971450805664 save time : 2.5987625122070312e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 3.1915807723999023 create new chi : 0.003393411636352539 time to delete rle : 0.014114856719970703 save time : 8.821487426757812e-06 map_output_result : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms time spend for datou_step_exec : 6.422076225280762 time spend to save output : 9.870529174804688e-05 total time spend for step 2 : 6.42217493057251 step3:ventilate_hashtags_in_portfolio Sat Mar 8 12:05:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 3373196 get user id for portfolio 3373196 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3373196 AND mptpi.`type`=2913 AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 391.92885851860046 time spend to save output : 0.0452725887298584 total time spend for step 3 : 391.9741311073303 step4:final Sat Mar 8 12:12:17 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 1.808732509613037 time spend to save output : 0.00014448165893554688 total time spend for step 4 : 1.8088769912719727 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} new output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} [1009068683, 1009068724] Looping around the photos to save general results len do output : 2 /1009068683.Didn't retrieve data . /1009068724.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('2719', None, None, None, None, None, None, None, None) ('2719', '3373196', '1009068683', None, None, None, None, None, None) ('2719', None, None, None, None, None, None, None, None) ('2719', '3373196', '1009068724', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 1.5581004619598389 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 4 output : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} ############################### TEST rubbia_carac_jrm_no_mask_detect ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Step 7557 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 7556 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 7561 merge_mask_and_thcl is not consistent : 3 used against 1 in the step definition ! WARNING : number of inputs for step 7558 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7560 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7559 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7560 doesn't seem to be define in the database( WARNING : type of input 3 of step 7559 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7556 have datatype=6 whereas input 0 of step 7561 have datatype=20 WARNING : type of output 1 of step 7561 doesn't seem to be define in the database( WARNING : type of input 1 of step 7558 doesn't seem to be define in the database( WARNING : type of output 2 of step 7561 doesn't seem to be define in the database( WARNING : type of input 1 of step 7560 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : crop_condition, thcl, argmax, merge_mask_and_thcl, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBBBBFBFBFBFBFBFBFBFFBFBFFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 14 ; length of list_pids : 14 ; length of list_args : 14 time to download the photos : 3.371483325958252 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 7 step1:crop_condition Sat Mar 8 12:12:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 3336 Loading chi in step crop for list_pids : 14 ! batch 1 Loaded 121 chid ids of type : 3336 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : teint_dans_la_masse param for this class : {'min_score': 0.7} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : teint_dans_la_masse begin to crop the class : autre_refus param for this class : {'min_score': 0.7} filtre for class : autre_refus hashtag_id of this class : 2107752406 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : autre_refus begin to crop the class : carton_gris param for this class : {'min_score': 0.7} filtre for class : carton_gris hashtag_id of this class : 2107753020 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : carton_gris begin to crop the class : cartonnette param for this class : {'min_score': 0.7} filtre for class : cartonnette hashtag_id of this class : 702398920 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : cartonnette begin to crop the class : carton_brun param for this class : {'min_score': 0.7} filtre for class : carton_brun hashtag_id of this class : 2107753024 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : carton_brun begin to crop the class : plastique param for this class : {'min_score': 0.7} filtre for class : plastique hashtag_id of this class : 492725882 begin to crop the class : kraft param for this class : {'min_score': 0.7} filtre for class : kraft hashtag_id of this class : 493202403 begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 time spend for datou_step_exec : 18.238709926605225 time spend to save output : 0.003016233444213867 total time spend for step 1 : 18.24172616004944 step2:thcl Sat Mar 8 12:12:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! Beginning of datou step Thcl ! no input time spend for datou_step_exec : 0.01197361946105957 time spend to save output : 0.00016498565673828125 total time spend for step 2 : 0.012138605117797852 step3:argmax Sat Mar 8 12:12:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! Beginning of datou_step Argmax ! no input time spend for datou_step_exec : 0.00039505958557128906 time spend to save output : 1.2159347534179688e-05 total time spend for step 3 : 0.00040721893310546875 step4:merge_mask_and_thcl Sat Mar 8 12:12:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! debut de la step merge mask and classif time spend for datou_step_exec : 0.0006663799285888672 time spend to save output : 1.2874603271484375e-05 total time spend for step 4 : 0.0006792545318603516 step5:rle_unique_nms_with_priority Sat Mar 8 12:12:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 26 chid ids of type : 3418 ++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 4.226124286651611 create new chi : 0.0090179443359375 time to delete rle : 1.0312139987945557 save time : 5.125999450683594e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 3.835376262664795 create new chi : 0.009953975677490234 time to delete rle : 0.5622799396514893 save time : 6.0558319091796875e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.6927871704101562 create new chi : 0.007413387298583984 time to delete rle : 0.9480929374694824 save time : 1.3589859008789062e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.663719892501831 create new chi : 6.556510925292969e-05 time to delete rle : 0.8901031017303467 save time : 2.6464462280273438e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 4.177592992782593 create new chi : 5.1975250244140625e-05 time to delete rle : 0.8141179084777832 save time : 1.6927719116210938e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.9150915145874023 create new chi : 0.00853109359741211 time to delete rle : 0.6656093597412109 save time : 1.6450881958007812e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.5033226013183594 create new chi : 6.771087646484375e-05 time to delete rle : 1.443103551864624 save time : 5.6743621826171875e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.45180082321167 create new chi : 9.441375732421875e-05 time to delete rle : 0.9352717399597168 save time : 1.430511474609375e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.4822170734405518 create new chi : 4.57763671875e-05 time to delete rle : 1.0534331798553467 save time : 1.239776611328125e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.4833283424377441 create new chi : 0.008737325668334961 time to delete rle : 0.47976231575012207 save time : 1.239776611328125e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.790863275527954 create new chi : 4.1961669921875e-05 time to delete rle : 0.7850844860076904 save time : 2.5272369384765625e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.539961338043213 create new chi : 4.267692565917969e-05 time to delete rle : 0.8899307250976562 save time : 1.8358230590820312e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.5190305709838867 create new chi : 0.009116888046264648 time to delete rle : 0.9280960559844971 save time : 1.3113021850585938e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.294674873352051 create new chi : 0.01019597053527832 time to delete rle : 1.2336277961730957 save time : 5.459785461425781e-05 map_output_result : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908)} End step rle-unique-nms time spend for datou_step_exec : 51.632715463638306 time spend to save output : 0.12415027618408203 total time spend for step 5 : 51.75686573982239 step6:ventilate_hashtags_in_portfolio Sat Mar 8 12:13:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! To do loadFromThcl(), then load ParamDescType : thcl2456 thcls : [{'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'}] thcl {'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'} Update svm_hashtag_type_desc : 4999 Iterating over portfolio : 3364276 get user id for portfolio 3364276 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3535038 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('refus','papier')) AND mptpi.`min_score`=0.7 To do Catched exception ! Connect or reconnect ! To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3364276 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('refus','papier')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 53.96628499031067 time spend to save output : 0.0006902217864990234 total time spend for step 6 : 53.96697521209717 step7:final Sat Mar 8 12:14:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 0.08144497871398926 time spend to save output : 3.457069396972656e-05 total time spend for step 7 : 0.08147954940795898 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',)} new output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',)} [1008921601, 1008921600, 1008922095, 1008922073, 1008922072, 1008921657, 1008921656, 1008921602, 1008922003, 1008922002, 1008921786, 1008922130, 1008922101, 1008922097] Looping around the photos to save general results len do output : 14 /1008921601.Didn't retrieve data . /1008921600.Didn't retrieve data . /1008922095.Didn't retrieve data . /1008922073.Didn't retrieve data . /1008922072.Didn't retrieve data . /1008921657.Didn't retrieve data . /1008921656.Didn't retrieve data . /1008921602.Didn't retrieve data . /1008922003.Didn't retrieve data . /1008922002.Didn't retrieve data . /1008921786.Didn't retrieve data . /1008922130.Didn't retrieve data . /1008922101.Didn't retrieve data . /1008922097.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921601', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921600', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922095', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922073', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922072', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921657', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921656', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921602', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922003', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922002', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921786', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922130', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922101', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922097', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 42 time used for this insertion : 0.23951506614685059 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',)} {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',)} ############################### TEST ventilate_hashtags_in_portfolio ################################ DELETE FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5486631; Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : ventilate_hashtags_in_portfolio list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.9289994239807129 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:ventilate_hashtags_in_portfolio Sat Mar 8 12:14:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 5363525 get user id for portfolio 5363525 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5363525 AND mptpi.`type`=4268 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','error','environment')) AND mptpi.`min_score`=0.3 To do To do ! Use context local managing function ! time spend for datou_step_exec : 3.666746139526367 time spend to save output : 0.00029397010803222656 total time spend for step 1 : 3.6670401096343994 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1075306598, 1075306564, 1075306534, 1075306522, 1075304668] Looping around the photos to save general results len do output : 1 /5363525. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306598', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306564', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306534', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306522', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075304668', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 5.378211259841919 save_final save missing photos in datou_result : After save, about to update current ! Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21) name : merge_qualipapia_like not run because too long ############################### TEST poly_ro_rle ################################ test creation de rle a partir de polygon Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : poly_to_rle list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2115612030029297 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:poly_to_rle Sat Mar 8 12:14:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec can't find the hashtag_type_input ,set the output_type same as the input_type batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++time for calcul the mask position with numpy : 0.013265371322631836 nb_pixel_total : 110633 time to create 1 rle with old method : 0.24307942390441895 time for calcul the mask position with numpy : 0.014325618743896484 nb_pixel_total : 15826 time to create 1 rle with old method : 0.037604570388793945 time for calcul the mask position with numpy : 0.00862741470336914 nb_pixel_total : 5286 time to create 1 rle with old method : 0.013206243515014648 time for calcul the mask position with numpy : 0.00823664665222168 nb_pixel_total : 1633 time to create 1 rle with old method : 0.003859281539916992 time for calcul the mask position with numpy : 0.007941722869873047 nb_pixel_total : 105533 time to create 1 rle with old method : 0.23247981071472168 time for calcul the mask position with numpy : 0.007612705230712891 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010376214981079102 time for calcul the mask position with numpy : 0.008130311965942383 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015959739685058594 time for calcul the mask position with numpy : 0.009847402572631836 nb_pixel_total : 62627 time to create 1 rle with old method : 0.13950061798095703 time for calcul the mask position with numpy : 0.021906137466430664 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07958197593688965 time for calcul the mask position with numpy : 0.024074792861938477 nb_pixel_total : 37724 time to create 1 rle with old method : 0.09259152412414551 time for calcul the mask position with numpy : 0.025377273559570312 nb_pixel_total : 48775 time to create 1 rle with old method : 0.12796688079833984 time for calcul the mask position with numpy : 0.06274271011352539 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.4233376979827881 time for calcul the mask position with numpy : 0.008547306060791016 nb_pixel_total : 2310 time to create 1 rle with old method : 0.006143331527709961 time for calcul the mask position with numpy : 0.009305000305175781 nb_pixel_total : 2256 time to create 1 rle with old method : 0.007977485656738281 time for calcul the mask position with numpy : 0.00913548469543457 nb_pixel_total : 3112 time to create 1 rle with old method : 0.01068425178527832 time for calcul the mask position with numpy : 0.008008480072021484 nb_pixel_total : 1662 time to create 1 rle with old method : 0.004611968994140625 batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 5.144351005554199 time spend to save output : 0.0004107952117919922 total time spend for step 1 : 5.144761800765991 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1003369118: 'temp/1741432478_4154994_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg'} batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++fin du test de poly_to_rle ############################### TEST cod_sts ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.8731119632720947 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Sat Mar 8 12:14:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('48', 4), ('42', 3)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 17082021 4453840 Nombre de photos uploadées : 7 / 23040 (0%) 17082021 4453840 Nombre de photos taguées (types de déchets): 0 / 7 (0%) 17082021 4453840 Nombre de photos taguées (volume) : 0 / 7 (0%) elapsed_time : load_data_split_time_score 8.368492126464844e-05 elapsed_time : order_list_meta_photo_and_scores 0.00010943412780761719 ??????? elapsed_time : fill_and_build_computed_from_old_data 0.003422260284423828 elapsed_time : insert_dashboard_record_day_entry 0.09128165245056152 TODO 20-09-21 https://github.com/fotonower/raspi-fotonower-x/issues/253#issuecomment-923099773 TODO 20-9-21 TODO 20-9-21 ***** BEGIN SPLIT TIME ***** False 1629158400.0 `1629186488.0 1629186300.0 `1629186488.0 1629186300.0 `1629186512.0 1629186300.0 `1629186512.0 1629186300.0 `1629189733.0 1629189600.0 `1629189733.0 1629189600.0 `1629189736.0 1629189600.0 list printed: [[0, 1, 2, 3], [4, 5, 6]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.007817506790161133 ***** END SPLIT TIME ***** NUMBER BATCH : 2 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['jrm'] ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 24.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_094808.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 3.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_104213.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 2 list_same_port_ids : [4453926] find same portfolio which already exist 4453926 , we will use it list_same_port_ids : [4652336] find same portfolio which already exist 4652336 , we will use it Qualite : 0.5219450480102605 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4453926 AND mptpi.`type`=4038 To do Qualite : 0.1657989516453152 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4652336 AND mptpi.`type`=4038 To do elapsed_time : count_nb_balles_and_create_portfolio 11.700726747512817 # DISPLAY ALL COLLECTED DATA : {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} time spend for datou_step_exec : 11.871448755264282 time spend to save output : 0.00039649009704589844 total time spend for step 1 : 11.871845245361328 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106] Looping around the photos to save general results len do output : 1 /4453840Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302186', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302153', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302152', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302146', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302113', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302110', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302106', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.7063477039337158 save_final save missing photos in datou_result : After save, about to update current ! Result test cod : {4453840: ([[0, 1, 2, 3], [4, 5, 6]], {'Rungis_jrm': [(0, 1), (1, 2)]}, {4453926: {'list_of_photos': [1050302106, 1050302146, 1050302110, 1050302152], 'hashtag': 'jrm'}, 4652336: {'list_of_photos': [1050302113, 1050302153, 1050302186], 'hashtag': 'jrm'}}, {2107757407: 7}, {'amount_uploaded_and_tagged': {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2)]}})}| ############################### TEST cod_download ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data [] [] ############################### TEST sendgrid ################################ test sendgrid senders@fotonower.com no problem of authentification, for test if the email can be received, try with a real receiver fin du test de sendgrid ############################### TEST rym_consolidate ################################ test_rym_consolidate Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 9321 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 9357 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 9318 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 9318 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 9410 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 9319 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 9328 blur_detection have less inputs used (0) than in the step definition (1) : maybe we manage optionnal inputs ! Step 9327 brightness have less inputs used (0) than in the step definition (1) : maybe we manage optionnal inputs ! Step 9326 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 9321 have datatype=11 whereas input 0 of step 9318 have datatype=2 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 9357 doesn't seem to be define in the database( WARNING : type of input 1 of step 9318 doesn't seem to be define in the database( WARNING : type of output 1 of step 9357 doesn't seem to be define in the database( WARNING : type of input 3 of step 9319 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 9410 doesn't seem to be define in the database( WARNING : output 1 of step 9318 have datatype=7 whereas input 1 of step 9410 have datatype=None WARNING : type of output 1 of step 9410 doesn't seem to be define in the database( WARNING : type of input 4 of step 9319 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 9410 have datatype=10 whereas input 3 of step 9326 have datatype=6 WARNING : type of output 1 of step 9321 doesn't seem to be define in the database( WARNING : type of input 1 of step 9357 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, send_mail_cod list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.4828159809112549 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 8 step1:copy_chis Sat Mar 8 12:15:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 0 chid ids of type : 0 time spend for datou_step_exec : 1.264963150024414 time spend to save output : 4.363059997558594e-05 total time spend for step 1 : 1.2650067806243896 step2:consolidate_hashtags_from_manual_portfolio Sat Mar 8 12:15:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 4709558 on est dans le IF portfolio mere 26T SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4683188 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673496 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673497 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673498 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673500 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673501 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673502 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673503 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673504 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673505 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673506 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673507 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673508 AND mpp.hide_status=0 ORDER BY ph.size desc To test ! Use context local managing function ! time spend for datou_step_exec : 338.683956861496 time spend to save output : 0.1293776035308838 total time spend for step 2 : 338.81333446502686 step3:rle_unique_nms_with_priority Sat Mar 8 12:20:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 65 chid ids of type : 4016 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 3 time to prepare the origin masks : 19.48187279701233 create new chi : 6.103515625e-05 time to delete rle : 0.2784574031829834 save time : 3.9577484130859375e-05 nb_obj : 0 nb_hashtags : 3 time to prepare the origin masks : 21.520835876464844 create new chi : 3.0994415283203125e-05 time to delete rle : 0.18719244003295898 save time : 1.2874603271484375e-05 map_output_result : {1057289467: (0.0, 'Should be the crop_list due to order', 0.0), 1057289546: (0.0, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms time spend for datou_step_exec : 42.85660743713379 time spend to save output : 0.0003821849822998047 total time spend for step 3 : 42.85698962211609 step4:ventilate_hashtags_in_portfolio Sat Mar 8 12:21:25 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 4709558 get user id for portfolio 4709558 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','mal_croppe','pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 2.7604684829711914 time spend to save output : 9.703636169433594e-05 total time spend for step 4 : 2.7605655193328857 step5:final Sat Mar 8 12:21:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 0.028344154357910156 time spend to save output : 6.365776062011719e-05 total time spend for step 5 : 0.028407812118530273 step6:blur_detection Sat Mar 8 12:21:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection toutes les photos sont déjà traitées, on saute les calculs time spend for datou_step_exec : 0.061591386795043945 time spend to save output : 0.00033783912658691406 total time spend for step 6 : 0.06192922592163086 step7:brightness Sat Mar 8 12:21:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness toutes les photos sont déjà traitées, on saute les calculs time spend for datou_step_exec : 0.009069681167602539 time spend to save output : 4.458427429199219e-05 total time spend for step 7 : 0.009114265441894531 step8:send_mail_cod Sat Mar 8 12:21:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/temp in order to get the selector url, please entre the license of selector results_COD_P4709558_08-03-2025_12_21_27.pdf 4673494 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734941741432888 4673496 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734961741432888 4673497 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734971741432891 4673498 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734981741432893 4673500 change filename to text .change filename to text .imagette46735001741432895 4673501 change filename to text .imagette46735011741432895 4673502 imagette46735021741432895 4673503 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735031741432895 4673504 imagette46735041741432897 4673505 imagette46735051741432897 4673506 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735061741432897 4673507 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735071741432899 4673508 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735081741432900 velour_link : ce batch n'est pas dans un dashboard, on n'envoi pas de mail. si vous voulez quand même des mail , veuillez configurer no_mail = 2 args[1057289467] : ((1057289467, -4.424440243329978, 492609224), (1057289467, -0.4062218880770088, 496442774), '0.009511382621534484') We are sending mail with results at marine@fotonower.com args[1057289546] : ((1057289546, -4.333383571220791, 492609224), (1057289546, -0.5998675991292823, 501862349), '0.009511382621534484') We are sending mail with results at marine@fotonower.com refus_total : 0.009511382621534484 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4709558 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 current_id not found list index out of range start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_08-03-2025_12_21_27.pdf results_COD_P4709558_08-03-2025_12_21_27.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_08-03-2025_12_21_27.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3818','4709558','results_COD_P4709558_08-03-2025_12_21_27.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_08-03-2025_12_21_27.pdf','pdf','','0.48','0.009511382621534484') time spend for datou_step_exec : 18.37259554862976 time spend to save output : 6.341934204101562e-05 total time spend for step 8 : 18.3726589679718 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 8 output : fin du test de rym_consolidate ############################### TEST generate_new_image_add_crop ################################ test_generate_new_image_add_crop Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : generate_new_image list_input_json : [] origin We have 1 , BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.14982318878173828 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:generate_new_image Sat Mar 8 12:21:50 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4789595 ORDER BY ph.size desc We have 1 , we need 3 photos there is already 3 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 3 photo needed from our local_cache list of photo_id_missing : [] batch 1 Loaded 3 chid ids of type : 4021 +++we need 2 photos there is already 2 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 2 photo needed from our local_cache list of photo_id_missing : [] begin to treate photo :1057314774 add chi : 2208326711 , rotate : 130 (901, 901) (488, 898, 128, 563) (435, 410, 3) (435, 410) (901, 901, 3) time for calcul the mask position with numpy : 0.013353347778320312 nb_pixel_total : 65382 time to create 1 rle with old method : 0.15846943855285645 batch 1 Loaded 0 chid ids of type : 0 time for calcul the mask position with numpy : 0.001844644546508789 nb_pixel_total : 65382 time to create 1 rle with old method : 0.14436841011047363 begin to treate photo :1057314768 add chi : 2208326712 , rotate : 178 (661, 661) (290, 530, 7, 566) (559, 240, 3) (559, 240) (661, 661, 3) time for calcul the mask position with numpy : 0.002110719680786133 nb_pixel_total : 98273 time to create 1 rle with old method : 0.23157596588134766 batch 1 Loaded 2 chid ids of type : 4021 ++time for calcul the mask position with numpy : 0.001962423324584961 nb_pixel_total : 80840 time to create 1 rle with old method : 0.1881558895111084 time for calcul the mask position with numpy : 0.0018148422241210938 nb_pixel_total : 98273 time to create 1 rle with old method : 0.21541857719421387 time for calcul the mask position with numpy : 0.0012416839599609375 nb_pixel_total : 8755 time to create 1 rle with old method : 0.02155137062072754 begin to treate photo :1057314766 add chi : 2208326718 , rotate : 94 (683, 683) (20, 98, 13, 363) (350, 78, 3) (350, 78) (683, 683, 3) time for calcul the mask position with numpy : 0.0013246536254882812 nb_pixel_total : 8864 time to create 1 rle with old method : 0.02111053466796875 batch 1 Loaded 3 chid ids of type : 4021 +++time for calcul the mask position with numpy : 0.0018291473388671875 nb_pixel_total : 65325 time to create 1 rle with old method : 0.14406967163085938 time for calcul the mask position with numpy : 0.0013508796691894531 nb_pixel_total : 8864 time to create 1 rle with old method : 0.05144357681274414 time for calcul the mask position with numpy : 0.0022444725036621094 nb_pixel_total : 89409 time to create 1 rle with old method : 0.2017345428466797 time for calcul the mask position with numpy : 0.0013225078582763672 nb_pixel_total : 6807 time to create 1 rle with old method : 0.0460352897644043 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741432918_4154994 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 1 chid ids of type : 4086 Number RLEs to save : 440 TO DO : save crop sub photo not yet done ! we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741432925_4154994 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 3 chid ids of type : 4086 Number RLEs to save : 979 TO DO : save crop sub photo not yet done ! we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1741432927_4154994 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 4 chid ids of type : 4086 Number RLEs to save : 1840 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 19.276217937469482 time spend to save output : 0.0001544952392578125 total time spend for step 1 : 19.27637243270874 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : None fin du test de generate_new_image ############################### TEST velours_tree ################################ test velours_tree - Retrieving photos to tag... query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4837217 ORDER BY ph.size desc - Loading descriptors... Size : 512 len(descriptors) : 5 Compute structured hierarchical clustering... ward : AgglomerativeClustering(n_clusters=5) ward.labels_ : [4 3 2 1 0] Elapsed time: 0.04970073699951172 graph_id used : 1145 - Beta version, working pretty good on 11-5-16 ! fin du test de velours_tree ############################### TEST step ACP ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 1.3117599487304688 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Sat Mar 8 12:22:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. ATTENTION : le size etait trop grand : 20, on a changé sa valeur à : 9 find save_photo_desc_type : 5705 On sauvegarde les nouveaux descripteurs dans le photo desc type : 5705 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5705, 'ACP_from_type_5619_size_9', 9, 9, 'ACP_from_type_5619_size_9', None, 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2022, 6, 16, 11, 6, 15), None) time to traite the descriptors : 0.0006747245788574219 storage_type for insertDescriptorsMulti : 3 Missing photo l117 : 1069306708 Missing photo l117 : 1069306710 Missing photo l117 : 1069306805 Missing photo l117 : 1069306815 Missing photo l117 : 1069306841 Missing photo l117 : 1069306843 Missing photo l117 : 1069306844 Missing photo l117 : 1069306954 Missing photo l117 : 1069306964 Missing photo l117 : 1069306967 To insert : 1069306708 To insert : 1069306710 To insert : 1069306805 To insert : 1069306815 To insert : 1069306841 To insert : 1069306843 To insert : 1069306844 To insert : 1069306954 To insert : 1069306964 To insert : 1069306967 time to insert the descriptors : 3.4260756969451904 res : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'} time spend for datou_step_exec : 7.918319940567017 time spend to save output : 6.580352783203125e-05 total time spend for step 1 : 7.918385744094849 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results len do output : 10 /1069306708Didn't retrieve data . /1069306710Didn't retrieve data . /1069306805Didn't retrieve data . /1069306815Didn't retrieve data . /1069306841Didn't retrieve data . /1069306843Didn't retrieve data . /1069306844Didn't retrieve data . /1069306954Didn't retrieve data . /1069306964Didn't retrieve data . /1069306967Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306967', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306964', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306954', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306844', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306843', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306841', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306815', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306805', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306710', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306708', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.837604284286499 save_final save missing photos in datou_result : After save, about to update current ! Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.016500234603881836 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Sat Mar 8 12:22:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. ATTENTION : le size etait trop grand : 20, on a changé sa valeur à : 9 Sauvegarde du modèle et envoi sur OVH Attention : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 existe, son contenu risque d'être écrasé model_param file didn't exist model didn't exist , save the new model in s3 local folder to save in s3 : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 update : 1741432946.0108519 done ! 1741432946.1597054 {'files': [{'name': 'pca_model.pkl', 'size': 103314, 'last_modified': '2025-03-08T11:22:26.023530', 'hash': 'd7e2c6aa9a1ef592ffdfc4abe9c66263'}], 'directories': []} Création d'un nouveau thème de classification Le thème de classification 'ACP_from_port_5709050_type_5619_size_9' existe déjà, merci de relancer avec un nouveau nom dans les params-json. time spend for datou_step_exec : 3.0267021656036377 time spend to save output : 0.0009224414825439453 total time spend for step 1 : 3.0276246070861816 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results object of type 'int' has no len() begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.1269216537475586 save_final save missing photos in datou_result : After save, about to update current ! Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.01489567756652832 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Sat Mar 8 12:22:26 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. To do loadFromThcl(), then load ParamDescType : thcl3412 thcls : [{'id': 3412, 'mtr_user_id': 31, 'name': 'ACP_from_port_5709050_type_5619_size_9', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '', 'svm_portfolios_learning': '0', 'photo_hashtag_type': 4398, 'photo_desc_type': 5706, 'type_classification': 'ACP', 'hashtag_id_list': '0'}] thcl {'id': 3412, 'mtr_user_id': 31, 'name': 'ACP_from_port_5709050_type_5619_size_9', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '', 'svm_portfolios_learning': '0', 'photo_hashtag_type': 4398, 'photo_desc_type': 5706, 'type_classification': 'ACP', 'hashtag_id_list': '0'} Update svm_hashtag_type_desc : 5706 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5706, 'ACP_from_port_5709050_type_5619_size_9', 9, 9, 'ACP_from_port_5709050_type_5619_size_9', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2022, 6, 16, 11, 8, 14), datetime.datetime(2022, 6, 16, 11, 8, 14)) model_param file didn't exist model_name : ACP_from_port_5709050_type_5619_size_9 model_type : acp list file need : ['pca_model.pkl'] file exist in s3 : ['pca_model.pkl'] file manque in s3 : [] local folder : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 /data/models_weight/ACP_from_port_5709050_type_5619_size_9/pca_model.pkl size_local : 103314 size in s3 : 103314 create time local : 2025-03-08 12:22:24 create time in s3 : 2025-03-08 11:22:26 pca_model.pkl already exist and didn't need to update model_name : ACP_from_port_5709050_type_5619_size_9 On sauvegarde les nouveaux descripteurs dans le photo desc type : 5706 time to traite the descriptors : 0.0003540515899658203 storage_type for insertDescriptorsMulti : 3 Missing photo l117 : 1069306708 Missing photo l117 : 1069306710 Missing photo l117 : 1069306805 Missing photo l117 : 1069306815 Missing photo l117 : 1069306841 Missing photo l117 : 1069306843 Missing photo l117 : 1069306844 Missing photo l117 : 1069306954 Missing photo l117 : 1069306964 Missing photo l117 : 1069306967 To insert : 1069306708 To insert : 1069306710 To insert : 1069306805 To insert : 1069306815 To insert : 1069306841 To insert : 1069306843 To insert : 1069306844 To insert : 1069306954 To insert : 1069306964 To insert : 1069306967 time to insert the descriptors : 6.177014589309692 res : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'} time spend for datou_step_exec : 8.265496730804443 time spend to save output : 0.00018715858459472656 total time spend for step 1 : 8.265683889389038 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results len do output : 10 /1069306708Didn't retrieve data . /1069306710Didn't retrieve data . /1069306805Didn't retrieve data . /1069306815Didn't retrieve data . /1069306841Didn't retrieve data . /1069306843Didn't retrieve data . /1069306844Didn't retrieve data . /1069306954Didn't retrieve data . /1069306964Didn't retrieve data . /1069306967Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306967', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306964', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306954', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306844', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306843', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306841', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306815', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306805', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306710', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306708', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.027025222778320312 save_final save missing photos in datou_result : After save, about to update current ! fin du test de la step acp ############################### TEST blur_crop ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : crop_condition list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 1.2478346824645996 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:crop_condition Sat Mar 8 12:22:36 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 4356 Loading chi in step crop for list_pids : 2 ! batch 1 Loaded 3 chid ids of type : 4356 +++++ begin to crop the class : Papier_Magazine param for this class : {} filtre for class : Papier_Magazine hashtag_id of this class : 2107752386 begin to crop the class : carton_brun param for this class : {} filtre for class : carton_brun hashtag_id of this class : 2107753024 begin to crop the class : carton_gris param for this class : {} filtre for class : carton_gris hashtag_id of this class : 2107753020 begin to crop the class : cartonnette param for this class : {} filtre for class : cartonnette hashtag_id of this class : 702398920 begin to crop the class : kraft param for this class : {} filtre for class : kraft hashtag_id of this class : 493202403 begin to crop the class : autre_refus param for this class : {} filtre for class : autre_refus hashtag_id of this class : 2107752406 begin to crop the class : metal param for this class : {} filtre for class : metal hashtag_id of this class : 492628673 begin to crop the class : plastique param for this class : {} filtre for class : plastique hashtag_id of this class : 492725882 begin to crop the class : teint_dans_la_masse param for this class : {} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 begin to crop the class : environnement param for this class : {} filtre for class : environnement hashtag_id of this class : 493012381 begin to crop the class : contaminant param for this class : {} filtre for class : contaminant hashtag_id of this class : 681467679 map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : contaminant time spend for datou_step_exec : 6.93563985824585 time spend to save output : 0.000118255615234375 total time spend for step 1 : 6.935758113861084 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1105701500, 1105701516] Looping around the photos to save general results len do output : 3 /1105703688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1105703689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1105703686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3990', None, None, None, None, None, None, None, None) ('3990', '6135916', '1105701500', None, None, None, None, None, None) ('3990', None, None, None, None, None, None, None, None) ('3990', '6135916', '1105701516', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.016687393188476562 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1105703688: [1105701500, 'temp/1741432955_4154994_1105701500_b57a1caec2d74ede6814095fdd28cb27_polygon_blur_2436373819_1.jpg', (108, 300, 16, 138)], 1105703689: [1105701500, 'temp/1741432955_4154994_1105701500_b57a1caec2d74ede6814095fdd28cb27_polygon_blur_2436374262_1.jpg', (47, 300, 91, 247)], 1105703686: [1105701516, 'temp/1741432955_4154994_1105701516_047b0ce16fe5e308d8512c83125c4058_polygon_blur_2436374092_1.jpg', (25, 175, 137, 235)]} fin du test de la step crop option blur ############################### TEST pma_consolidate ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 12666 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12667 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 12664 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 12664 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12671 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 12666 have datatype=11 whereas input 0 of step 12664 have datatype=2 WARNING : type of output 1 of step 12667 doesn't seem to be define in the database( WARNING : type of input 3 of step 12665 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 12667 doesn't seem to be define in the database( WARNING : type of input 1 of step 12664 doesn't seem to be define in the database( WARNING : type of input 1 of step 12671 doesn't seem to be define in the database( WARNING : output 1 of step 12664 have datatype=7 whereas input 1 of step 12671 have datatype=None WARNING : type of output 1 of step 12671 doesn't seem to be define in the database( WARNING : type of input 4 of step 12665 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 12666 doesn't seem to be define in the database( WARNING : type of input 1 of step 12667 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.2668147087097168 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 5 step1:copy_chis Sat Mar 8 12:22:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 21 chid ids of type : 4482 batch 1 Loaded 88 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 6.526966571807861 time spend to save output : 3.933906555175781e-05 total time spend for step 1 : 6.527005910873413 step2:consolidate_hashtags_from_manual_portfolio Sat Mar 8 12:22:49 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 6549724 on est dans le IF portfolio mere 26T SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=9778120 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755323 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755324 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755325 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755326 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755327 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755328 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755329 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755330 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755331 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755332 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755333 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755334 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755335 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755336 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755337 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755338 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755339 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755340 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755341 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 48 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755342 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : To test ! Use context local managing function ! time spend for datou_step_exec : 9.257487297058105 time spend to save output : 5.7697296142578125e-05 total time spend for step 2 : 9.257544994354248 step3:rle_unique_nms_with_priority Sat Mar 8 12:22:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 88 chid ids of type : 4490 seulement à utiliser dans la step consolidation batch 1 Loaded 38 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.03470563888549805 seulement à utiliser dans la step consolidation batch 1 Loaded 50 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.034616708755493164 map_output_result : {1114046597: (0.09264291817443555, 'Should be the crop_list due to order', 0.042237513638672064), 1114046377: (0.09264291817443555, 'Should be the crop_list due to order', 0.14304832271019904)} End step rle-unique-nms time spend for datou_step_exec : 8.818153619766235 time spend to save output : 7.081031799316406e-05 total time spend for step 3 : 8.818224430084229 step4:ventilate_hashtags_in_portfolio Sat Mar 8 12:23:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 6549724 get user id for portfolio 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do lien utilise dans velours : https://www.fotonower.com/velours/9755323,9755324,9755325,9755326,9755327,9755328,9755329,9755330,9755331,9755332,9755333,9755334,9755335,9755336,9755337,9755338,9755339,9755340,9755341,9755342,9755344,9755345?tags=papier,carton,metal,pet_clair,pehd,pet_fonce,pet_opaque,barquette_opaque,film_plastique,ela,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement,mal_croppe,flou&datou_id_consolidate=4387&port_consolidate=6549724 time spend for datou_step_exec : 1.4184160232543945 time spend to save output : 4.410743713378906e-05 total time spend for step 4 : 1.4184601306915283 step5:final Sat Mar 8 12:23:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 1.721745252609253 time spend to save output : 6.866455078125e-05 total time spend for step 5 : 1.7218139171600342 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1114046597: ('0.3133414848207032',), 1114046377: ('0.3133414848207032',)} new output for save of step final : {1114046597: ('0.3133414848207032',), 1114046377: ('0.3133414848207032',)} [1114046597, 1114046377] Looping around the photos to save general results len do output : 2 /1114046597.Didn't retrieve data . /1114046377.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('4492', None, None, None, None, None, None, None, None) ('4492', '6549724', '1114046597', None, None, None, None, None, None) ('4492', None, None, None, None, None, None, None, None) ('4492', '6549724', '1114046377', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.41061830520629883 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 5 output : {1114046597: ('0.3133414848207032',), 1114046377: ('0.3133414848207032',)} fin du test de portfolio mere absolue dans consolidate ############################### TEST pma_ventilate ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 12795 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12796 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 12793 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 12793 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 0 of step 12795 have datatype=11 whereas input 0 of step 12793 have datatype=2 WARNING : type of output 1 of step 12796 doesn't seem to be define in the database( WARNING : type of input 1 of step 12793 doesn't seem to be define in the database( WARNING : type of input 1 of step 12800 doesn't seem to be define in the database( WARNING : output 1 of step 12793 have datatype=7 whereas input 1 of step 12800 have datatype=None WARNING : type of output 1 of step 12795 doesn't seem to be define in the database( WARNING : type of input 1 of step 12796 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.31754112243652344 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 4 step1:copy_chis Sat Mar 8 12:23:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 21 chid ids of type : 4482 batch 1 Loaded 88 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.12334060668945312 time spend to save output : 4.696846008300781e-05 total time spend for step 1 : 0.12338757514953613 step2:consolidate_hashtags_from_manual_portfolio Sat Mar 8 12:23:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755323 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755324 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755325 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755326 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755327 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755328 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755329 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755330 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755331 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755332 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755333 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755334 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755335 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755336 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755337 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755338 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755339 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755340 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755341 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 48 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755342 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : To test ! Use context local managing function ! time spend for datou_step_exec : 2.23679256439209 time spend to save output : 5.1975250244140625e-05 total time spend for step 2 : 2.236844539642334 step3:rle_unique_nms_with_priority Sat Mar 8 12:23:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 88 chid ids of type : 4490 seulement à utiliser dans la step consolidation batch 1 Loaded 38 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.1825120449066162 seulement à utiliser dans la step consolidation batch 1 Loaded 50 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.11379075050354004 map_output_result : {1114046597: (0.09264291817443555, 'Should be the crop_list due to order', 0.042237513638672064), 1114046377: (0.09264291817443555, 'Should be the crop_list due to order', 0.14304832271019904)} End step rle-unique-nms time spend for datou_step_exec : 1.4182746410369873 time spend to save output : 4.315376281738281e-05 total time spend for step 3 : 1.4183177947998047 step4:ventilate_hashtags_in_portfolio Sat Mar 8 12:23:15 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 6549724 get user id for portfolio 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=9974548 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do lien utilise dans velours : https://www.fotonower.com/velours/9755323,9755324,9755325,9755326,9755327,9755328,9755329,9755330,9755331,9755332,9755333,9755334,9755335,9755336,9755337,9755338,9755339,9755340,9755341,9755342,9755344,9755345?tags=papier,carton,metal,pet_clair,pehd,pet_fonce,pet_opaque,barquette_opaque,film_plastique,ela,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement,mal_croppe,flou&datou_id_consolidate=4387&port_consolidate=6549724 time spend for datou_step_exec : 1.9658336639404297 time spend to save output : 6.67572021484375e-05 total time spend for step 4 : 1.9659004211425781 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1114046597, 1114046377] Looping around the photos to save general results len do output : 1 /6549724. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4553', None, None, None, None, None, None, None, None) ('4553', '6549724', '1114046597', None, None, None, None, None, None) ('4553', None, None, None, None, None, None, None, None) ('4553', '6549724', '1114046377', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 1.015308141708374 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 4 output : {6549724: [{'papier': 9755346, 'carton': 9755347, 'metal': 9755348, 'pet_clair': 9755349, 'pehd': 9755350, 'pet_fonce': 9755351, 'pet_opaque': 9755352, 'barquette_opaque': 9755353, 'film_plastique': 9755354, 'ela': 9755355, 'sac': 9755356, 'textiles': 9755357, 'verre': 9755358, 'organique': 9755359, 'dasri': 9755360, 'masque': 9755361, 'encombrant': 9755362, 'autre_emballage': 9755363, 'autre_non_emballage': 9755364, 'environnement': 9755365, 'mal_croppe': 9755366, 'flou': 9755367}]} fin du test de portfolio mere absolue dans consolidate #&_#_#&_# TEST sam SUCCEEDED #&_#_#&_# #&_#_#&_# TEST frcnn SUCCEEDED #&_#_#&_# #&_#_#&_# TEST thcl SUCCEEDED #&_#_#&_# #&_#_#&_# TEST tfhub2 SUCCEEDED #&_#_#&_# #&_#_#&_# TEST ordonner SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rotate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST data_augmentation_ellipse_varroa_tile_rotate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST flip SUCCEEDED #&_#_#&_# #&_#_#&_# TEST crop_rles SUCCEEDED #&_#_#&_# #&_#_#&_# TEST angular_coeff SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detection_filter_by_crop SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detection_filter_by_classif SUCCEEDED #&_#_#&_# #&_#_#&_# TEST blur_detection SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detect_point_224x224 SUCCEEDED #&_#_#&_# #&_#_#&_# TEST certificat_qualite_papier SUCCEEDED #&_#_#&_# #&_#_#&_# TEST image_temperature_detection SUCCEEDED #&_#_#&_# #&_#_#&_# TEST broca SUCCEEDED #&_#_#&_# #&_#_#&_# TEST crop_conditional SUCCEEDED #&_#_#&_# #&_#_#&_# TEST image_blanchir SUCCEEDED #&_#_#&_# #&_#_#&_# TEST darker_image SUCCEEDED #&_#_#&_# #&_#_#&_# TEST img_aug SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_split_dark SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_append SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_horaire FAILED #&_#_#&_# #&_#_#&_# TEST rle_unique_nms_with_priority SUCCEEDED #&_#_#&_# #&_#_#&_# TEST random_deformation SUCCEEDED #&_#_#&_# #&_#_#&_# TEST tile SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rotate_chi SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_carac_pet_clair_0121_no_cnn SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_carac_jrm_no_mask_detect SUCCEEDED #&_#_#&_# #&_#_#&_# TEST ventilate_hashtags_in_portfolio SUCCEEDED #&_#_#&_# #&_#_#&_# TEST poly_ro_rle SUCCEEDED #&_#_#&_# #&_#_#&_# TEST cod_sts SUCCEEDED #&_#_#&_# #&_#_#&_# TEST cod_download SUCCEEDED #&_#_#&_# #&_#_#&_# TEST sendgrid SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rym_consolidate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST generate_new_image_add_crop SUCCEEDED #&_#_#&_# #&_#_#&_# TEST velours_tree SUCCEEDED #&_#_#&_# #&_#_#&_# TEST step ACP SUCCEEDED #&_#_#&_# #&_#_#&_# TEST blur_crop SUCCEEDED #&_#_#&_# #&_#_#&_# TEST pma_consolidate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST pma_ventilate SUCCEEDED #&_#_#&_# #&_# TEST FAILED #&_# : tests/datou_test #&_# #&_# END OF TEST #&_# : tests/datou_test #&_# #&_# BEGIN OF TEST : mtr/database_queries/CacheModelData_queries #&_# /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelData_queries.py Test Cache Model Data test a faire VR 27-9-17 #&_# TEST SUCCEEDED #&_# : mtr/database_queries/CacheModelData_queries #&_# #&_# END OF TEST #&_# : mtr/database_queries/CacheModelData_queries #&_# #&_# BEGIN OF TEST : tests/cache_photo_data_test #&_# /home/admin/workarea/git/Velours/python/tests/cache_photo_data_test.py Test local_cache_photo ############################### test_download_photos_by_local_cache ################################ test download portfolio 1162416 : 574 photos We have 1 , we need 574 photos there is already 574 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 574 photo needed from our local_cache list of photo_id_missing : [] test download a list if photos : 10 photos (6 exist in ovh and 4 missing in ovh) we need 10 photos there is already 6 photos exist in our local_cache we have to download 4 photos download_photo : 1 to 2000 BBBBHTTP Error 404: Not Found can't download the photo : 1109585109 FHTTP Error 404: Not Found can't download the photo : 1109585436 FHTTP Error 404: Not Found can't download the photo : 1109585121 FHTTP Error 404: Not Found can't download the photo : 1109585120 Fwe have successful downloaded 0 photos there are 4 photos missing finally, we can get 6 photo needed from our local_cache list of photo_id_missing : [1109585109, 1109585436, 1109585121, 1109585120] ############################### test_update_time_created ################################ we need 1 photos there is already 1 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 1 photo needed from our local_cache list of photo_id_missing : [] #&_#_#&_# TEST cache photo data SUCCEEDED #&_#_#&_# #&_# TEST SUCCEEDED #&_# : tests/cache_photo_data_test #&_# #&_# END OF TEST #&_# : tests/cache_photo_data_test #&_# #&_# BEGIN OF TEST : mtr/mask_rcnn/prepare_maskdata #&_# /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/prepare_maskdata.py test prepare mask data 2096875719 599722655 batch 1 Loaded 20 chid ids of type : 840 ++++++++++++++++++++batch 1 Loaded 16 chid ids of type : 840 +++++++++batch 1 Loaded 20 chid ids of type : 840 ++++++++++++++++++++batch 1 Loaded 19 chid ids of type : 840 ++++++++++batch 1 Loaded 20 chid ids of type : 840 ++++++++++++{2096875719: 'Plaque-immatriculation', 599722655: 'capot'} logo-marque 2096875717 907850592 x0 : 38 y1 : 269 width : 32, height : 38, area : 1216, score : 1.0 None pare-choc 624624117 907850592 x0 : 0 y1 : 559 width : 362, height : 235, area : 85070, score : 1.0 None coffre 495920967 907850592 x0 : 0 y1 : 398 width : 302, height : 336, area : 101472, score : 1.0 None coffre 495920967 907850592 x0 : 626 y1 : 206 width : 18, height : 35, area : 630, score : 1.0 None aile-arriere 2106233861 907850592 x0 : 189 y1 : 476 width : 282, height : 235, area : 66270, score : 1.0 None roue 492689227 907850592 x0 : 275 y1 : 620 width : 170, height : 215, area : 36550, score : 1.0 None Plaque-immatriculation 2096875719 907850592 x0 : 10 y1 : 454 width : 108, height : 84, area : 9072, score : 1.0 None feu-arriere 2096875713 907850592 x0 : 192 y1 : 359 width : 147, height : 96, area : 14112, score : 1.0 None poignee 499500794 907850592 x0 : 437 y1 : 315 width : 42, height : 37, area : 1554, score : 1.0 None poignee 499500794 907850592 x0 : 567 y1 : 282 width : 25, height : 31, area : 775, score : 1.0 None Pot-echappement 2096875720 907850592 x0 : 155 y1 : 532 width : 21, height : 20, area : 420, score : 1.0 None toit 492731002 907850592 x0 : 136 y1 : 74 width : 281, height : 21, area : 5901, score : 1.0 None Pare-brise 2096875709 907850592 x0 : 1 y1 : 241 width : 285, height : 165, area : 47025, score : 1.0 None porte 492654799 907850592 x0 : 354 y1 : 442 width : 210, height : 380, area : 79800, score : 1.0 None porte 492654799 907850592 x0 : 501 y1 : 389 width : 135, height : 324, area : 43740, score : 1.0 None Info-modele 2096875721 907850592 x0 : 132 y1 : 295 width : 53, height : 31, area : 1643, score : 1.0 None vitre 492925064 907850592 x0 : 407 y1 : 201 width : 123, height : 122, area : 15006, score : 1.0 None vitre 492925064 907850592 x0 : 377 y1 : 201 width : 64, height : 118, area : 7552, score : 1.0 None vitre 492925064 907850592 x0 : 522 y1 : 203 width : 102, height : 120, area : 12240, score : 1.0 None Essuie-glace 2096875722 907850592 x0 : 7 y1 : 250 width : 72, height : 61, area : 4392, score : 1.0 None /data/data_root/test_preparedata/train/907850592.jpg logo-marque 2096875717 907862724 x0 : 100 y1 : 282 width : 35, height : 41, area : 1435, score : 1.0 None pare-choc 624624117 907862724 x0 : 1 y1 : 483 width : 435, height : 194, area : 84390, score : 1.0 None aile-arriere 2106233861 907862724 x0 : 399 y1 : 411 width : 110, height : 128, area : 14080, score : 1.0 None retroviseur 492844413 907862724 x0 : 600 y1 : 203 width : 41, height : 37, area : 1517, score : 1.0 None coffre 495920967 907862724 x0 : 5 y1 : 351 width : 369, height : 270, area : 99630, score : 1.0 None Cache-reservoir 2096875718 907862724 x0 : 417 y1 : 289 width : 50, height : 61, area : 3050, score : 1.0 None roue 492689227 907862724 x0 : 367 y1 : 541 width : 121, height : 190, area : 22990, score : 1.0 None poignee 499500794 907862724 x0 : 478 y1 : 275 width : 33, height : 25, area : 825, score : 1.0 None toit 492731002 907862724 x0 : 226 y1 : 110 width : 223, height : 36, area : 8028, score : 1.0 None feu-arriere 2096875713 907862724 x0 : 294 y1 : 331 width : 112, height : 146, area : 16352, score : 1.0 None poignee 499500794 907862724 x0 : 566 y1 : 252 width : 20, height : 27, area : 540, score : 1.0 None Plaque-immatriculation 2096875719 907862724 x0 : 55 y1 : 436 width : 142, height : 54, area : 7668, score : 1.0 None Pare-brise 2096875709 907862724 x0 : 6 y1 : 225 width : 366, height : 132, area : 48312, score : 1.0 None porte 492654799 907862724 x0 : 395 y1 : 391 width : 170, height : 311, area : 52870, score : 1.0 None porte 492654799 907862724 x0 : 508 y1 : 354 width : 101, height : 269, area : 27169, score : 1.0 None vitre 492925064 907862724 x0 : 451 y1 : 202 width : 85, height : 99, area : 8415, score : 1.0 None vitre 492925064 907862724 x0 : 524 y1 : 202 width : 70, height : 99, area : 6930, score : 1.0 None vitre 492925064 907862724 x0 : 428 y1 : 208 width : 50, height : 102, area : 5100, score : 1.0 None Essuie-glace 2096875722 907862724 x0 : 114 y1 : 218 width : 186, height : 27, area : 5022, score : 1.0 None Info-modele 2096875721 907862724 x0 : 10 y1 : 306 width : 41, height : 33, area : 1353, score : 1.0 None /data/data_root/test_preparedata/train/907862724.jpg porte 492654799 907863602 x0 : 129 y1 : 380 width : 300, height : 279, area : 83700, score : 1.0 None porte 492654799 907863602 x0 : 193 y1 : 345 width : 228, height : 215, area : 49020, score : 1.0 None porte 492654799 907863602 x0 : 379 y1 : 385 width : 257, height : 286, area : 73502, score : 1.0 None porte 492654799 907863602 x0 : 400 y1 : 365 width : 195, height : 248, area : 48360, score : 1.0 None roue 492689227 907863602 x0 : 0 y1 : 419 width : 124, height : 148, area : 18352, score : 1.0 None roue 492689227 907863602 x0 : 0 y1 : 411 width : 127, height : 129, area : 16383, score : 1.0 None roue 492689227 907863602 x0 : 580 y1 : 449 width : 60, height : 129, area : 7740, score : 1.0 None roue 492689227 907863602 x0 : 584 y1 : 444 width : 50, height : 126, area : 6300, score : 1.0 None retroviseur 492844413 907863602 x0 : 165 y1 : 225 width : 53, height : 51, area : 2703, score : 1.0 None vitre 492925064 907863602 x0 : 211 y1 : 218 width : 197, height : 109, area : 21473, score : 1.0 None vitre 492925064 907863602 x0 : 217 y1 : 214 width : 193, height : 99, area : 19107, score : 1.0 None vitre 492925064 907863602 x0 : 409 y1 : 214 width : 188, height : 98, area : 18424, score : 1.0 None vitre 492925064 907863602 x0 : 414 y1 : 206 width : 187, height : 96, area : 17952, score : 1.0 None poignee 499500794 907863602 x0 : 320 y1 : 271 width : 46, height : 19, area : 874, score : 1.0 None poignee 499500794 907863602 x0 : 321 y1 : 270 width : 44, height : 19, area : 836, score : 1.0 None poignee 499500794 907863602 x0 : 553 y1 : 270 width : 62, height : 25, area : 1550, score : 1.0 None poignee 499500794 907863602 x0 : 565 y1 : 266 width : 42, height : 16, area : 672, score : 1.0 None capot 599722655 907863602 x0 : -1 y1 : 215 width : 135, height : 37, area : 4995, score : 1.0 None capot 599722655 907863602 x0 : 242 y1 : 123 width : 330, height : 52, area : 17160, score : 1.0 None /data/data_root/test_preparedata/train/907863602.jpg porte 492654799 907863940 x0 : 109 y1 : 426 width : 283, height : 265, area : 74995, score : 1.0 None porte 492654799 907863940 x0 : 179 y1 : 399 width : 203, height : 221, area : 44863, score : 1.0 None porte 492654799 907863940 x0 : 354 y1 : 425 width : 277, height : 266, area : 73682, score : 1.0 None porte 492654799 907863940 x0 : 370 y1 : 407 width : 179, height : 233, area : 41707, score : 1.0 None roue 492689227 907863940 x0 : -1 y1 : 463 width : 98, height : 141, area : 13818, score : 1.0 None roue 492689227 907863940 x0 : 0 y1 : 468 width : 88, height : 147, area : 12936, score : 1.0 None roue 492689227 907863940 x0 : 523 y1 : 493 width : 117, height : 146, area : 17082, score : 1.0 None roue 492689227 907863940 x0 : 525 y1 : 493 width : 111, height : 147, area : 16317, score : 1.0 None retroviseur 492844413 907863940 x0 : 138 y1 : 278 width : 46, height : 44, area : 2024, score : 1.0 None vitre 492925064 907863940 x0 : 182 y1 : 266 width : 200, height : 95, area : 19000, score : 1.0 None vitre 492925064 907863940 x0 : 385 y1 : 257 width : 172, height : 90, area : 15480, score : 1.0 None vitre 492925064 907863940 x0 : 394 y1 : 256 width : 144, height : 86, area : 12384, score : 1.0 None vitre 492925064 907863940 x0 : 549 y1 : 253 width : 58, height : 53, area : 3074, score : 1.0 None poignee 499500794 907863940 x0 : 300 y1 : 306 width : 59, height : 17, area : 1003, score : 1.0 None poignee 499500794 907863940 x0 : 307 y1 : 313 width : 51, height : 24, area : 1224, score : 1.0 None poignee 499500794 907863940 x0 : 527 y1 : 308 width : 53, height : 22, area : 1166, score : 1.0 None poignee 499500794 907863940 x0 : 536 y1 : 310 width : 45, height : 24, area : 1080, score : 1.0 None capot 599722655 907863940 x0 : 219 y1 : 181 width : 370, height : 38, area : 14060, score : 1.0 None logo-roue 2106233859 907863940 x0 : 7 y1 : 419 width : 27, height : 43, area : 1161, score : 1.0 None logo-roue 2106233859 907863940 x0 : 594 y1 : 441 width : 26, height : 25, area : 650, score : 1.0 None /data/data_root/test_preparedata/val/907863940.jpg #&_#_#&_# TEST prepare mask data poly SUCCEEDED #&_#_#&_# 2107755846 batch 1 Loaded 15 chid ids of type : 2622 {2107755846: 'pet_clair'} environment 492622729 964453879 x0 : 0 y1 : 479 width : 112, height : 415, area : 46480, score : 1.0 None error 501120777 964453879 x0 : 142 y1 : 479 width : 28, height : 20, area : 560, score : 1.0 None error 501120777 964453879 x0 : 151 y1 : 99 width : 23, height : 42, area : 966, score : 1.0 None error 501120777 964453879 x0 : 175 y1 : 59 width : 117, height : 59, area : 6903, score : 1.0 None error 501120777 964453879 x0 : 282 y1 : 479 width : 35, height : 62, area : 2170, score : 1.0 None error 501120777 964453879 x0 : 315 y1 : 460 width : 19, height : 26, area : 494, score : 1.0 None error 501120777 964453879 x0 : 353 y1 : 361 width : 32, height : 41, area : 1312, score : 1.0 None error 501120777 964453879 x0 : 403 y1 : 215 width : 47, height : 51, area : 2397, score : 1.0 None error 501120777 964453879 x0 : 497 y1 : 253 width : 89, height : 55, area : 4895, score : 1.0 None error 501120777 964453879 x0 : 546 y1 : 195 width : 58, height : 68, area : 3944, score : 1.0 None error 501120777 964453879 x0 : 613 y1 : 323 width : 20, height : 32, area : 640, score : 1.0 None error 501120777 964453879 x0 : 638 y1 : 225 width : 17, height : 27, area : 459, score : 1.0 None pet_clair 2107755846 964453879 x0 : 0 y1 : 479 width : 719, height : 479, area : 344401, score : 1.0 None error 501120777 964453879 x0 : 546 y1 : 195 width : 60, height : 68, area : 4080, score : 1.0 None error 501120777 964453879 x0 : 613 y1 : 323 width : 22, height : 32, area : 704, score : 1.0 None /data/data_root/test_preparedata/val/964453879.jpg #&_#_#&_# TEST prepare mask data rle SUCCEEDED #&_#_#&_# time for calcul the mask position with numpy : 0.0003807544708251953 nb_pixel_total : 6 time to create 1 rle with old method : 6.437301635742188e-05 Sanity check PASSED : sum_rle_size : 12 height * width : 12 7 #&_#_#&_# TEST prepare mask data mat SUCCEEDED #&_#_#&_# #&_# TEST SUCCEEDED #&_# : mtr/mask_rcnn/prepare_maskdata #&_# #&_# END OF TEST #&_# : mtr/mask_rcnn/prepare_maskdata #&_# #&_# BEGIN OF TEST : mtr/database_queries/portfolio_queries #&_# /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py test portfolio queries #&_# TEST SUCCEEDED #&_# : mtr/database_queries/portfolio_queries #&_# #&_# END OF TEST #&_# : mtr/database_queries/portfolio_queries #&_# #&_# BEGIN OF TEST : prod/memo/memo #&_# /home/admin/workarea/git/Velours/python/prod/memo/memo.py SLA Mensuel python version used : 3 ############################### TEST memo ################################ Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier nb_day : (0, 31) VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select id from MTRUser.mtr_portfolios where id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(select last_run_id from MTRPhoto.dashboard_entry_day where dashboard_place_id in (select id from MTRPhoto.dashboard_places where name = 'Romainville_Presse_2' and date like '%2022-08%') and created_at like '%2022-08%'))); nb_day : (0, 31) after unwanted_material_data nb_day : (0, 31) after coverage_data after number_of_batch date_start : 2022-08-01 : dt_date_just_month_year : 2022-08-01 00:00:00 : VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! Error parsing crontab ! [Errno 2] No such file or directory: '' You better check your LOGRASPI env !!! after pl.get_datou_sts_from_crontab : verbose : False no sts found, try to find from database SELECT dri.id FROM MTRPhoto.dashboard_run_ids dri, MTRPhoto.dashboard_entry_day ded, MTRPhoto.dashboard_places dp WHERE dp.name= "Romainville_Presse_2" AND ded.dashboard_place_id=dp.id AND dri.dashboard_entry_day=ded.id AND dri.id=ded.last_run_id AND ded.date >= "2022-08-01" AND ded.date <= "2022-08-31" apple3 {'gm': {'mat': 'gm', 'pht': 4209, 'datou_carac_id': 3994, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'emr': {'mat': 'emr', 'pht': 4207, 'datou_carac_id': 3993, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'carton,background'}, 'jrm': {'mat': 'jrm', 'pht': 3726, 'datou_carac_id': 3459, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'ela': {'mat': 'ela', 'pht': 4203, 'datou_carac_id': 3991, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'ela,background'}, 'pehd_pp': {'mat': 'pehd_pp', 'pht': 4211, 'datou_carac_id': 3995, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pehd,background'}, 'pet_fonce': {'mat': 'pet_fonce', 'pht': 4200, 'datou_carac_id': 4153, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_fonce,background'}, 'aluminium': {'mat': 'aluminium', 'pht': 4205, 'datou_carac_id': 3992, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'metal,background'}, 'refus': {'mat': 'refus', 'pht': 3594, 'datou_carac_id': 3318, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'refus,background'}, 'pet_clair': {'mat': 'pet_clair', 'pht': 3327, 'datou_carac_id': 3804, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_clair,bouchon,etiquette,barquette_avec_film,background'}} SELECT h.hashtag as unwanted_material, substr(dr.hashtag,8) as main_material, ptp.type as pht_type, sum(pcr.value*dr.nombre_balle)/sum(dr.nombre_balle) as ratio, count(distinct mpp.mtr_photo_id) as nb_photo, group_concat(distinct ptp.mtr_portfolio_id_2) as list_port_cont, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_port, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", h.hashtag, ":", cast(ptp.type as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_mat FROM MTRPhoto.dashboard_results dr, MTRPhoto.mtr_port_to_port_ids ptp, MTRUser.mtr_portfolio_photos mpp, MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h WHERE dr.dashboard_run_id IN (448643,449568,454450,454465,454459,454472,456239,458795,461498,454567,457375,457649,459844,460382,461634,463066,472528,465865,469297,468129,475039,472453,474892,474538,475316,476524,479832,481665,479903) AND dr.mtr_portfolio_id=ptp.mtr_portfolio_id_1 AND dr.qualite >= 0 AND mpp.mtr_portfolio_id=ptp.mtr_portfolio_id_2 AND pcr.portfolio_id=ptp.mtr_portfolio_id_1 AND h.hashtag_id = pcr.hashtag_id AND ptp.type = pcr.hashtag_type AND mpp.hide_status = 0 AND ptp.hashtag_id=h.hashtag_id AND ptp.type IN (4209,4207,3726,4203,4211,4200,4205,3594,3327) group by h.hashtag, dr.hashtag, ptp.type; VR TODO TO BETTER PARSE ! ({'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0001728785023265608, 'nb_photo': 18, 'list_port_cont': '6861407,6862123,6862547,6867908,6868674,6869389,6869558,6877707,6877972,6881690,6891352,6894202,6907047,6922714,6925593', 'assoc_port': '6790887:6862547,6794193:6862123,6834822:6877972,6840554:6877707,6845438:6869389,6846972:6869558,6851003:6868674,6853215:6861407,6864848:6867908,6881492:6881690,6889190:6891352,6894094:6894202,6906036:6907047,6921897:6922714,6925484:6925593', 'assoc_mat': '6790887:barquette_opaque:4207:6862547,6794193:barquette_opaque:4207:6862123,6834822:barquette_opaque:4207:6877972,6840554:barquette_opaque:4207:6877707,6845438:barquette_opaque:4207:6869389,6846972:barquette_opaque:4207:6869558,6851003:barquette_opaque:4207:6868674,6853215:barquette_opaque:4207:6861407,6864848:barquette_opaque:4207:6867908,6881492:barquette_opaque:4207:6881690,6889190:barquette_opaque:4207:6891352,6894094:barquette_opaque:4207:6894202,6906036:barquette_opaque:4207:6907047,6921897:barquette_opaque:4207:6922714,6925484:barquette_opaque:4207:6925593'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 8.469862336652651e-05, 'nb_photo': 44, 'list_port_cont': '6626450,6627857,6628405,6628829,6644425,6664932,6665236,6666075,6666652,6669243,6669771,6706891,6712605,6719637,6720391,6720629,6722154,6744912,6762879,6775287,6835808,6841133,6842925,6846016,6852775,6853230', 'assoc_port': '6625827:6626450,6627447:6627857,6627801:6628405,6628455:6628829,6630580:6664932,6630581:6665236,6630822:6666075,6631618:6669771,6639123:6644425,6665718:6666652,6668655:6669243,6706262:6706891,6708643:6712605,6719441:6719637,6719951:6720391,6720202:6720629,6721827:6722154,6744086:6744912,6762224:6762879,6774283:6775287,6834821:6835808,6840554:6841133,6842390:6842925,6845438:6846016,6852117:6852775,6852533:6853230', 'assoc_mat': '6625827:barquette_opaque:4209:6626450,6627447:barquette_opaque:4209:6627857,6627801:barquette_opaque:4209:6628405,6628455:barquette_opaque:4209:6628829,6630580:barquette_opaque:4209:6664932,6630581:barquette_opaque:4209:6665236,6630822:barquette_opaque:4209:6666075,6631618:barquette_opaque:4209:6669771,6639123:barquette_opaque:4209:6644425,6665718:barquette_opaque:4209:6666652,6668655:barquette_opaque:4209:6669243,6706262:barquette_opaque:4209:6706891,6708643:barquette_opaque:4209:6712605,6719441:barquette_opaque:4209:6719637,6719951:barquette_opaque:4209:6720391,6720202:barquette_opaque:4209:6720629,6721827:barquette_opaque:4209:6722154,6744086:barquette_opaque:4209:6744912,6762224:barquette_opaque:4209:6762879,6774283:barquette_opaque:4209:6775287,6834821:barquette_opaque:4209:6835808,6840554:barquette_opaque:4209:6841133,6842390:barquette_opaque:4209:6842925,6845438:barquette_opaque:4209:6846016,6852117:barquette_opaque:4209:6852775,6852533:barquette_opaque:4209:6853230'}, {'unwanted_material': 'carton', 'main_material': 'aluminium', 'pht_type': 4205, 'ratio': 0.01022994561299725, 'nb_photo': 16, 'list_port_cont': '6625532,6880743,6880786', 'assoc_port': '6625304:6625532,6880715:6880786,6880716:6880743', 'assoc_mat': '6625304:carton:4205:6625532,6880715:carton:4205:6880786,6880716:carton:4205:6880743'}, {'unwanted_material': 'carton', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.9683717828305286, 'nb_photo': 2565, 'list_port_cont': '6860769,6860801,6860841,6860928,6860987,6861035,6861210,6861405,6861437,6861808,6861831,6861929,6862023,6862130,6862206,6862328,6862365,6862540,6862581,6862649,6862984,6863819,6864050,6866304,6866467,6866495,6866955,6867660,6867837,6867907,6868220,6868254,6868280,6868378,6868398,6868623,6868676,6868731,6868887,6868963,6869104,6869323,6869395,6869455,6869560,6876859,6876893,6876912,6876948,6877006,6877054,6877103,6877189,6877551,6877616,6877676,6877710,6877780,6877820,6877867,6877909,6877915,6877971,6877996,6878027,6881699,6884486,6886468,6886876,6887990,6888294,6889466,6891082,6891177,6891354,6894196,6894492,6895946,6896156,6897897,6898092,6898471,6898488,6900295,6900456,6904480,6904513,6905153,6905998,6906484,6907056,6908162,6908516,6908935,6909121,6914618,6915177,6915209,6915268,6916694,6917250,6917397,6920809,6922717,6925589', 'assoc_port': '6790886:6864050,6790887:6862540,6794193:6862130,6832752:6877915,6832753:6877867,6833565:6878027,6834821:6877996,6834822:6877971,6836050:6877780,6838973:6877676,6838974:6877616,6840554:6877710,6842390:6877820,6845438:6869395,6846972:6869560,6846973:6869455,6848417:6869323,6849526:6868887,6851003:6868676,6852117:6868731,6852533:6869104,6852534:6868963,6853215:6861405,6853569:6861437,6853919:6868623,6855642:6860801,6859123:6861831,6860530:6861210,6860531:6861035,6860532:6860987,6860535:6860928,6860537:6860841,6860538:6860769,6861512:6868220,6861514:6868280,6861516:6868254,6861517:6868378,6861519:6862581,6861520:6862649,6861522:6861808,6861523:6862328,6861524:6862206,6861525:6861929,6861635:6862365,6861636:6862023,6862501:6862984,6863220:6863819,6864848:6867907,6864850:6867837,6864851:6867660,6864853:6866955,6864855:6866495,6865738:6866304,6865739:6866467,6868332:6868398,6876683:6877189,6876684:6876912,6876685:6877006,6876686:6877054,6876687:6877103,6876688:6876893,6876689:6876948,6876691:6876859,6877478:6877909,', 'assoc_mat': '6790886:carton:4207:6864050,6790887:carton:4207:6862540,6794193:carton:4207:6862130,6832752:carton:4207:6877915,6832753:carton:4207:6877867,6833565:carton:4207:6878027,6834821:carton:4207:6877996,6834822:carton:4207:6877971,6836050:carton:4207:6877780,6838973:carton:4207:6877676,6838974:carton:4207:6877616,6840554:carton:4207:6877710,6842390:carton:4207:6877820,6845438:carton:4207:6869395,6846972:carton:4207:6869560,6846973:carton:4207:6869455,6848417:carton:4207:6869323,6849526:carton:4207:6868887,6851003:carton:4207:6868676,6852117:carton:4207:6868731,6852533:carton:4207:6869104,6852534:carton:4207:6868963,6853215:carton:4207:6861405,6853569:carton:4207:6861437,6853919:carton:4207:6868623,6855642:carton:4207:6860801,6859123:carton:4207:6861831,6860530:carton:4207:6861210,6860531:carton:4207:6861035,6860532:carton:4207:6860987,6860535:carton:4207:6860928,6860537:carton:4207:6860841,6860538:carton:4207:6860769,6861512:carton:4207:6868220,6861514:carton:4207:6868280,6861516:carton:4207:6868254,6861517:carton:4'}, {'unwanted_material': 'carton', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.007834080277437061, 'nb_photo': 1462, 'list_port_cont': '6613099,6614127,6614585,6615525,6616029,6626453,6627860,6628395,6628840,6629152,6629205,6629281,6635304,6635369,6635432,6639702,6640661,6641608,6642551,6644436,6646356,6647438,6648160,6648884,6654375,6655819,6657180,6658370,6659145,6659232,6660891,6661075,6664557,6664640,6664706,6664802,6664929,6665234,6666043,6666077,6666648,6666765,6666944,6668004,6668673,6668922,6669250,6669483,6669559,6669701,6669786,6669812,6670114,6671149,6671463,6671574,6671847,6672297,6674151,6674264,6674794,6676772,6678452,6681186,6683438,6683967,6700222,6702222,6704712,6706887,6708974,6709414,6709569,6710040,6710776,6711299,6712611,6716502,6718348,6718717,6718883,6719281,6719649,6719891,6720389,6720622,6722163,6722585,6722885,6723449,6723510,6744917,6745245,6746407,6748615,6748946,6749029,6758826,6760253,6760547,6761445,6761998,6762877,6774108,6774613,6775298,6776475,6776981,6813192,6834126,6834362,6834696,6835803,6836102,6837017,6839291,6839592,6841138,6842928,6846019,6847495,6847556,6849259,6850419,6851368,6852770,6853216,6853519,', 'assoc_port': '6612572:6613099,6613636:6614127,6614336:6614585,6614890:6615525,6614891:6616029,6625827:6626453,6627447:6627860,6627801:6628395,6627802:6629281,6628455:6628840,6628735:6629152,6628736:6629205,6630577:6664557,6630578:6664706,6630579:6664640,6630580:6664929,6630581:6665234,6630822:6666077,6630823:6666043,6631154:6668004,6631384:6668673,6631385:6668922,6631618:6669786,6631619:6669812,6631901:6670114,6632901:6671149,6632902:6671463,6633542:6635432,6633543:6635304,6633544:6671847,6633545:6635369,6633546:6672297,6635651:6661075,6635653:6660891,6639123:6644436,6639124:6639702,6639695:6642551,6640066:6640661,6640928:6641608,6645791:6646356,6646682:6647438,6647202:6648160,6648217:6648884,6652800:6654375,6655088:6655819,6656505:6657180,6656506:6658370,6657819:6659145,6659025:6659232,6663042:6664802,6665067:6666944,6665718:6666648,6665719:6666765,6668654:6669701,6668655:6669250,6668656:6669483,6668657:6669559,6670886:6671574,6671756:6676772,6673385:6674151,6673850:6674264,6674357:6674794,6678032:6678452,6680947:6681186,', 'assoc_mat': '6612572:carton:4209:6613099,6613636:carton:4209:6614127,6614336:carton:4209:6614585,6614890:carton:4209:6615525,6614891:carton:4209:6616029,6625827:carton:4209:6626453,6627447:carton:4209:6627860,6627801:carton:4209:6628395,6627802:carton:4209:6629281,6628455:carton:4209:6628840,6628735:carton:4209:6629152,6628736:carton:4209:6629205,6630577:carton:4209:6664557,6630578:carton:4209:6664706,6630579:carton:4209:6664640,6630580:carton:4209:6664929,6630581:carton:4209:6665234,6630822:carton:4209:6666077,6630823:carton:4209:6666043,6631154:carton:4209:6668004,6631384:carton:4209:6668673,6631385:carton:4209:6668922,6631618:carton:4209:6669786,6631619:carton:4209:6669812,6631901:carton:4209:6670114,6632901:carton:4209:6671149,6632902:carton:4209:6671463,6633542:carton:4209:6635432,6633543:carton:4209:6635304,6633544:carton:4209:6671847,6633545:carton:4209:6635369,6633546:carton:4209:6672297,6635651:carton:4209:6661075,6635653:carton:4209:6660891,6639123:carton:4209:6644436,6639124:carton:4209:6639702,6639695:carton:4'}, {'unwanted_material': 'carton', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.0029313403454804565, 'nb_photo': 10, 'list_port_cont': '6861174,6862613,6886392,6902824,6905431,6920955', 'assoc_port': '6860533:6861174,6861515:6862613,6886257:6886392,6900676:6902824,6905103:6905431,6920015:6920955', 'assoc_mat': '6860533:carton:4209:6861174,6861515:carton:4209:6862613,6886257:carton:4209:6886392,6900676:carton:4209:6902824,6905103:carton:4209:6905431,6920015:carton:4209:6920955'}, {'unwanted_material': 'carton', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.005861756255830517, 'nb_photo': 4, 'list_port_cont': '6647696', 'assoc_port': '6647200:6647696', 'assoc_mat': '6647200:carton:4200:6647696'}, {'unwanted_material': 'ela', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0001359155180754883, 'nb_photo': 1, 'list_port_cont': '6877603', 'assoc_port': '6838974:6877603', 'assoc_mat': '6838974:ela:4207:6877603'}, {'unwanted_material': 'ela', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 5.6302219709875656e-05, 'nb_photo': 3, 'list_port_cont': '6635371,6644435,6657178', 'assoc_port': '6633545:6635371,6639123:6644435,6656505:6657178', 'assoc_mat': '6633545:ela:4209:6635371,6639123:ela:4209:6644435,6656505:ela:4209:6657178'}, {'unwanted_material': 'etiquette', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0004908205603477037, 'nb_photo': 82, 'list_port_cont': '6860802,6860985,6861441,6861842,6861919,6862212,6862342,6862370,6862544,6862643,6868221,6868633,6868729,6869099,6869322,6869388,6869458,6876887,6877052,6877109,6877190,6877547,6877688,6877715,6877814,6877918,6877964,6877998,6878028,6886871,6891088,6891171,6896147,6900293,6900464,6904479,6907053,6908947,6909124,6915211,6915265,6916685,6917252,6920804,6922718', 'assoc_port': '6790887:6862544,6832752:6877918,6833565:6878028,6834821:6877998,6834822:6877964,6838973:6877688,6840554:6877715,6842390:6877814,6845438:6869388,6846973:6869458,6848417:6869322,6852117:6868729,6852533:6869099,6853569:6861441,6853919:6868633,6855642:6860802,6859123:6861842,6860532:6860985,6861512:6868221,6861520:6862643,6861523:6862342,6861524:6862212,6861525:6861919,6861635:6862370,6876683:6877190,6876686:6877052,6876687:6877109,6876688:6876887,6877480:6877547,6886258:6886871,6889185:6891088,6891024:6891171,6895749:6896147,6899246:6900464,6899248:6900293,6900677:6909124,6900678:6908947,6903712:6904479,6906036:6907053,6914258:6915211,6914260:6916685,6914751:6915265,6917033:6917252,6920014:6920804,6921897:6922718', 'assoc_mat': '6790887:etiquette:4207:6862544,6832752:etiquette:4207:6877918,6833565:etiquette:4207:6878028,6834821:etiquette:4207:6877998,6834822:etiquette:4207:6877964,6838973:etiquette:4207:6877688,6840554:etiquette:4207:6877715,6842390:etiquette:4207:6877814,6845438:etiquette:4207:6869388,6846973:etiquette:4207:6869458,6848417:etiquette:4207:6869322,6852117:etiquette:4207:6868729,6852533:etiquette:4207:6869099,6853569:etiquette:4207:6861441,6853919:etiquette:4207:6868633,6855642:etiquette:4207:6860802,6859123:etiquette:4207:6861842,6860532:etiquette:4207:6860985,6861512:etiquette:4207:6868221,6861520:etiquette:4207:6862643,6861523:etiquette:4207:6862342,6861524:etiquette:4207:6862212,6861525:etiquette:4207:6861919,6861635:etiquette:4207:6862370,6876683:etiquette:4207:6877190,6876686:etiquette:4207:6877052,6876687:etiquette:4207:6877109,6876688:etiquette:4207:6876887,6877480:etiquette:4207:6877547,6886258:etiquette:4207:6886871,6889185:etiquette:4207:6891088,6891024:etiquette:4207:6891171,6895749:etiquette:4207:6896147,6'}, {'unwanted_material': 'etiquette', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0008238173967106119, 'nb_photo': 1049, 'list_port_cont': '6613101,6614126,6614587,6616038,6626458,6627864,6628397,6628838,6629141,6629204,6629280,6635302,6635366,6635430,6640667,6641598,6642545,6644428,6646362,6647450,6648167,6648890,6654367,6655834,6657182,6658372,6659142,6659220,6660894,6661081,6664553,6664638,6664707,6664807,6664938,6665231,6666055,6666086,6666643,6666770,6666946,6668001,6668670,6668913,6669257,6669490,6669555,6669702,6669778,6669808,6670117,6671148,6671464,6671561,6671836,6672301,6674153,6674271,6674799,6676771,6678460,6681184,6683436,6683981,6700231,6702226,6704721,6706890,6708962,6709416,6709579,6710035,6710785,6711300,6712603,6716517,6718720,6718877,6719278,6719639,6719881,6720398,6720627,6722161,6722583,6722896,6723446,6723519,6744916,6745258,6746410,6748605,6748955,6749037,6760261,6760532,6761442,6762007,6774112,6774618,6775290,6776483,6776974,6813185,6834132,6834363,6834703,6835800,6836101,6837027,6839290,6839587,6841136,6842924,6846017,6847494,6847557,6849269,6850421,6852780,6853222,6853521,6853980,6854357,6855828,6859399', 'assoc_port': '6612572:6613101,6613636:6614126,6614336:6614587,6614891:6616038,6625827:6626458,6627447:6627864,6627801:6628397,6627802:6629280,6628455:6628838,6628735:6629141,6628736:6629204,6630577:6664553,6630578:6664707,6630579:6664638,6630580:6664938,6630581:6665231,6630822:6666086,6630823:6666055,6631154:6668001,6631384:6668670,6631385:6668913,6631618:6669778,6631619:6669808,6631901:6670117,6632901:6671148,6632902:6671464,6633542:6635430,6633543:6635302,6633544:6671836,6633545:6635366,6633546:6672301,6635651:6661081,6635653:6660894,6639123:6644428,6639695:6642545,6640066:6640667,6640928:6641598,6645791:6646362,6646682:6647450,6647202:6648167,6648217:6648890,6652800:6654367,6655088:6655834,6656505:6657182,6656506:6658372,6657819:6659142,6659025:6659220,6663042:6664807,6665067:6666946,6665718:6666643,6665719:6666770,6668654:6669702,6668655:6669257,6668656:6669490,6668657:6669555,6670886:6671561,6671756:6676771,6673385:6674153,6673850:6674271,6674357:6674799,6678032:6678460,6680947:6681184,6682633:6683436,6683289:6683981,', 'assoc_mat': '6612572:etiquette:4209:6613101,6613636:etiquette:4209:6614126,6614336:etiquette:4209:6614587,6614891:etiquette:4209:6616038,6625827:etiquette:4209:6626458,6627447:etiquette:4209:6627864,6627801:etiquette:4209:6628397,6627802:etiquette:4209:6629280,6628455:etiquette:4209:6628838,6628735:etiquette:4209:6629141,6628736:etiquette:4209:6629204,6630577:etiquette:4209:6664553,6630578:etiquette:4209:6664707,6630579:etiquette:4209:6664638,6630580:etiquette:4209:6664938,6630581:etiquette:4209:6665231,6630822:etiquette:4209:6666086,6630823:etiquette:4209:6666055,6631154:etiquette:4209:6668001,6631384:etiquette:4209:6668670,6631385:etiquette:4209:6668913,6631618:etiquette:4209:6669778,6631619:etiquette:4209:6669808,6631901:etiquette:4209:6670117,6632901:etiquette:4209:6671148,6632902:etiquette:4209:6671464,6633542:etiquette:4209:6635430,6633543:etiquette:4209:6635302,6633544:etiquette:4209:6671836,6633545:etiquette:4209:6635366,6633546:etiquette:4209:6672301,6635651:etiquette:4209:6661081,6635653:etiquette:4209:6660894,6'}, {'unwanted_material': 'etiquette', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.0007589310876444754, 'nb_photo': 16, 'list_port_cont': '6860869,6861178,6861533,6862611,6863435,6876769,6889758,6894819,6902814,6920942', 'assoc_port': '6860533:6861178,6860536:6860869,6861163:6861533,6861515:6862611,6863221:6863435,6876690:6876769,6889188:6889758,6894658:6894819,6900676:6902814,6920015:6920942', 'assoc_mat': '6860533:etiquette:4209:6861178,6860536:etiquette:4209:6860869,6861163:etiquette:4209:6861533,6861515:etiquette:4209:6862611,6863221:etiquette:4209:6863435,6876690:etiquette:4209:6876769,6889188:etiquette:4209:6889758,6894658:etiquette:4209:6894819,6900676:etiquette:4209:6902814,6920015:etiquette:4209:6920942'}, {'unwanted_material': 'etiquette', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.0010885671487047438, 'nb_photo': 5, 'list_port_cont': '6647706', 'assoc_port': '6647200:6647706', 'assoc_mat': '6647200:etiquette:4200:6647706'}, {'unwanted_material': 'film_plastique', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.00029682028164317286, 'nb_photo': 8, 'list_port_cont': '6862648,6866460,6877060,6877718,6877925,6922720,6925580', 'assoc_port': '6832752:6877925,6840554:6877718,6861520:6862648,6865739:6866460,6876686:6877060,6921897:6922720,6925484:6925580', 'assoc_mat': '6832752:film_plastique:4207:6877925,6840554:film_plastique:4207:6877718,6861520:film_plastique:4207:6862648,6865739:film_plastique:4207:6866460,6876686:film_plastique:4207:6877060,6921897:film_plastique:4207:6922720,6925484:film_plastique:4207:6925580'}, {'unwanted_material': 'film_plastique', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 9.746706181821035e-05, 'nb_photo': 9, 'list_port_cont': '6614124,6644434,6702223,6711304,6712598,6744913,6834700', 'assoc_port': '6613636:6614124,6639123:6644434,6701501:6702223,6708643:6712598,6708644:6711304,6744086:6744913,6832752:6834700', 'assoc_mat': '6613636:film_plastique:4209:6614124,6639123:film_plastique:4209:6644434,6701501:film_plastique:4209:6702223,6708643:film_plastique:4209:6712598,6708644:film_plastique:4209:6711304,6744086:film_plastique:4209:6744913,6832752:film_plastique:4209:6834700'}, {'unwanted_material': 'kraft', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0011039430628874177, 'nb_photo': 1, 'list_port_cont': '6917403', 'assoc_port': '6917034:6917403', 'assoc_mat': '6917034:kraft:4207:6917403'}, {'unwanted_material': 'metal', 'main_material': 'aluminium', 'pht_type': 4205, 'ratio': 0.9896715615394742, 'nb_photo': 54, 'list_port_cont': '6625531,6661187,6880740,6880777,6881860,6918649', 'assoc_port': '6625304:6625531,6632479:6661187,6880715:6880777,6880716:6880740,6881491:6881860,6887696:6918649', 'assoc_mat': '6625304:metal:4205:6625531,6632479:metal:4205:6661187,6880715:metal:4205:6880777,6880716:metal:4205:6880740,6881491:metal:4205:6881860,6887696:metal:4205:6918649'}, {'unwanted_material': 'metal', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.002857356890964171, 'nb_photo': 188, 'list_port_cont': '6860922,6860979,6861033,6861211,6861400,6861433,6861833,6862030,6862128,6862210,6862338,6862362,6862534,6862572,6862638,6863807,6864053,6866290,6866466,6866498,6866956,6867841,6867914,6868223,6868261,6868374,6868621,6868667,6868732,6869091,6869331,6869392,6869468,6869545,6876850,6876885,6876957,6877055,6877099,6877198,6877539,6877618,6877678,6877719,6877785,6877818,6877877,6877912,6877928,6877966,6878004,6878040,6884491,6886881,6887977,6888302,6894497,6895947,6896159,6897898,6898087,6898465,6898489,6900299,6900468,6904477,6905148,6905987,6907057,6908937,6909122,6915222,6915272,6916692,6917256,6920805,6925583', 'assoc_port': '6790886:6864053,6790887:6862534,6794193:6862128,6832752:6877928,6832753:6877877,6833565:6878040,6834821:6878004,6834822:6877966,6836050:6877785,6838973:6877678,6838974:6877618,6840554:6877719,6842390:6877818,6845438:6869392,6846972:6869545,6846973:6869468,6848417:6869331,6851003:6868667,6852117:6868732,6852533:6869091,6853215:6861400,6853569:6861433,6853919:6868621,6859123:6861833,6860530:6861211,6860531:6861033,6860532:6860979,6860535:6860922,6861512:6868223,6861516:6868261,6861517:6868374,6861519:6862572,6861520:6862638,6861523:6862338,6861524:6862210,6861635:6862362,6861636:6862030,6863220:6863807,6864848:6867914,6864850:6867841,6864853:6866956,6864855:6866498,6865738:6866290,6865739:6866466,6876683:6877198,6876686:6877055,6876687:6877099,6876688:6876885,6876689:6876957,6876691:6876850,6877478:6877912,6877480:6877539,6884290:6884491,6886258:6886881,6887695:6888302,6887732:6887977,6894095:6894497,6895749:6896159,6895752:6895947,6897575:6898087,6897576:6897898,6898175:6898489,6898176:6898465,6899246:6900468,', 'assoc_mat': '6790886:metal:4207:6864053,6790887:metal:4207:6862534,6794193:metal:4207:6862128,6832752:metal:4207:6877928,6832753:metal:4207:6877877,6833565:metal:4207:6878040,6834821:metal:4207:6878004,6834822:metal:4207:6877966,6836050:metal:4207:6877785,6838973:metal:4207:6877678,6838974:metal:4207:6877618,6840554:metal:4207:6877719,6842390:metal:4207:6877818,6845438:metal:4207:6869392,6846972:metal:4207:6869545,6846973:metal:4207:6869468,6848417:metal:4207:6869331,6851003:metal:4207:6868667,6852117:metal:4207:6868732,6852533:metal:4207:6869091,6853215:metal:4207:6861400,6853569:metal:4207:6861433,6853919:metal:4207:6868621,6859123:metal:4207:6861833,6860530:metal:4207:6861211,6860531:metal:4207:6861033,6860532:metal:4207:6860979,6860535:metal:4207:6860922,6861512:metal:4207:6868223,6861516:metal:4207:6868261,6861517:metal:4207:6868374,6861519:metal:4207:6862572,6861520:metal:4207:6862638,6861523:metal:4207:6862338,6861524:metal:4207:6862210,6861635:metal:4207:6862362,6861636:metal:4207:6862030,6863220:metal:4207:686380'}, {'unwanted_material': 'metal', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.026017127541676587, 'nb_photo': 787, 'list_port_cont': '6613097,6614121,6615521,6616026,6626451,6627868,6628403,6628835,6629153,6629208,6629277,6635307,6635359,6635433,6639705,6640666,6641594,6642552,6644427,6647437,6648164,6648886,6654370,6655828,6657169,6658371,6659139,6660892,6661087,6664549,6664633,6664701,6664796,6664935,6665224,6666046,6666081,6666646,6666766,6666945,6667992,6668663,6668912,6669249,6669482,6669546,6669703,6669773,6669806,6670109,6671159,6671466,6671576,6672296,6674155,6674267,6674795,6676776,6678453,6681189,6683437,6683972,6700233,6702218,6704725,6706893,6708961,6709417,6709566,6710044,6710774,6711296,6712599,6716510,6718358,6718713,6718870,6719287,6719648,6720401,6720631,6722162,6722586,6722893,6723437,6723512,6744909,6745246,6746419,6748619,6748941,6749034,6758839,6760267,6760542,6761443,6762005,6774106,6774607,6775285,6776482,6776987,6813179,6834120,6834369,6834695,6835802,6836110,6837026,6839593,6841134,6846021,6847508,6847568,6849264,6850424,6851374,6852778,6853219,6853526,6854348,6855834,6859395', 'assoc_port': '6612572:6613097,6613636:6614121,6614890:6615521,6614891:6616026,6625827:6626451,6627447:6627868,6627801:6628403,6627802:6629277,6628455:6628835,6628735:6629153,6628736:6629208,6630577:6664549,6630578:6664701,6630579:6664633,6630580:6664935,6630581:6665224,6630822:6666081,6630823:6666046,6631154:6667992,6631384:6668663,6631385:6668912,6631618:6669773,6631619:6669806,6631901:6670109,6632901:6671159,6632902:6671466,6633542:6635433,6633543:6635307,6633545:6635359,6633546:6672296,6635651:6661087,6635653:6660892,6639123:6644427,6639124:6639705,6639695:6642552,6640066:6640666,6640928:6641594,6646682:6647437,6647202:6648164,6648217:6648886,6652800:6654370,6655088:6655828,6656505:6657169,6656506:6658371,6657819:6659139,6663042:6664796,6665067:6666945,6665718:6666646,6665719:6666766,6668654:6669703,6668655:6669249,6668656:6669482,6668657:6669546,6670886:6671576,6671756:6676776,6673385:6674155,6673850:6674267,6674357:6674795,6678032:6678453,6680947:6681189,6682633:6683437,6683289:6683972,6699726:6700233,6700555:6704725,', 'assoc_mat': '6612572:metal:4209:6613097,6613636:metal:4209:6614121,6614890:metal:4209:6615521,6614891:metal:4209:6616026,6625827:metal:4209:6626451,6627447:metal:4209:6627868,6627801:metal:4209:6628403,6627802:metal:4209:6629277,6628455:metal:4209:6628835,6628735:metal:4209:6629153,6628736:metal:4209:6629208,6630577:metal:4209:6664549,6630578:metal:4209:6664701,6630579:metal:4209:6664633,6630580:metal:4209:6664935,6630581:metal:4209:6665224,6630822:metal:4209:6666081,6630823:metal:4209:6666046,6631154:metal:4209:6667992,6631384:metal:4209:6668663,6631385:metal:4209:6668912,6631618:metal:4209:6669773,6631619:metal:4209:6669806,6631901:metal:4209:6670109,6632901:metal:4209:6671159,6632902:metal:4209:6671466,6633542:metal:4209:6635433,6633543:metal:4209:6635307,6633545:metal:4209:6635359,6633546:metal:4209:6672296,6635651:metal:4209:6661087,6635653:metal:4209:6660892,6639123:metal:4209:6644427,6639124:metal:4209:6639705,6639695:metal:4209:6642552,6640066:metal:4209:6640666,6640928:metal:4209:6641594,6646682:metal:4209:664743'}, {'unwanted_material': 'metal', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.03426755560145856, 'nb_photo': 14, 'list_port_cont': '6861172,6863427,6863597,6865059,6876772,6881616,6889760,6902821,6920943', 'assoc_port': '6860533:6861172,6863219:6863597,6863221:6863427,6864849:6865059,6876690:6876772,6881493:6881616,6889188:6889760,6900676:6902821,6920015:6920943', 'assoc_mat': '6860533:metal:4209:6861172,6863219:metal:4209:6863597,6863221:metal:4209:6863427,6864849:metal:4209:6865059,6876690:metal:4209:6876772,6881493:metal:4209:6881616,6889188:metal:4209:6889760,6900676:metal:4209:6902821,6920015:metal:4209:6920943'}, {'unwanted_material': 'papier', 'main_material': 'aluminium', 'pht_type': 4205, 'ratio': 0.0013353682339268438, 'nb_photo': 9, 'list_port_cont': '6625537,6880784', 'assoc_port': '6625304:6625537,6880715:6880784', 'assoc_mat': '6625304:papier:4205:6625537,6880715:papier:4205:6880784'}, {'unwanted_material': 'papier', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.030078019576209876, 'nb_photo': 6564, 'list_port_cont': '6860765,6860809,6860834,6860929,6860974,6861044,6861206,6861412,6861434,6861798,6861840,6861924,6862024,6862135,6862201,6862331,6862374,6862545,6862582,6862651,6862990,6863820,6864046,6866293,6866465,6866499,6866949,6867662,6867830,6867915,6868217,6868247,6868293,6868379,6868403,6868626,6868668,6868733,6868889,6868970,6869094,6869326,6869384,6869464,6869555,6876860,6876889,6876921,6876951,6877008,6877057,6877102,6877186,6877542,6877605,6877685,6877711,6877787,6877810,6877872,6877908,6877920,6877963,6878007,6878032,6881693,6884489,6886474,6886878,6887983,6888296,6889465,6891084,6891165,6891355,6894204,6894504,6895937,6896154,6897893,6898095,6898457,6898491,6900292,6900467,6904483,6904508,6905154,6905995,6906491,6907050,6908161,6908519,6908941,6909123,6914622,6915183,6915210,6915259,6916693,6917258,6917398,6920814,6922713,6925594', 'assoc_port': '6790886:6864046,6790887:6862545,6794193:6862135,6832752:6877920,6832753:6877872,6833565:6878032,6834821:6878007,6834822:6877963,6836050:6877787,6838973:6877685,6838974:6877605,6840554:6877711,6842390:6877810,6845438:6869384,6846972:6869555,6846973:6869464,6848417:6869326,6849526:6868889,6851003:6868668,6852117:6868733,6852533:6869094,6852534:6868970,6853215:6861412,6853569:6861434,6853919:6868626,6855642:6860809,6859123:6861840,6860530:6861206,6860531:6861044,6860532:6860974,6860535:6860929,6860537:6860834,6860538:6860765,6861512:6868217,6861514:6868293,6861516:6868247,6861517:6868379,6861519:6862582,6861520:6862651,6861522:6861798,6861523:6862331,6861524:6862201,6861525:6861924,6861635:6862374,6861636:6862024,6862501:6862990,6863220:6863820,6864848:6867915,6864850:6867830,6864851:6867662,6864853:6866949,6864855:6866499,6865738:6866293,6865739:6866465,6868332:6868403,6876683:6877186,6876684:6876921,6876685:6877008,6876686:6877057,6876687:6877102,6876688:6876889,6876689:6876951,6876691:6876860,6877478:6877908,', 'assoc_mat': '6790886:papier:4207:6864046,6790887:papier:4207:6862545,6794193:papier:4207:6862135,6832752:papier:4207:6877920,6832753:papier:4207:6877872,6833565:papier:4207:6878032,6834821:papier:4207:6878007,6834822:papier:4207:6877963,6836050:papier:4207:6877787,6838973:papier:4207:6877685,6838974:papier:4207:6877605,6840554:papier:4207:6877711,6842390:papier:4207:6877810,6845438:papier:4207:6869384,6846972:papier:4207:6869555,6846973:papier:4207:6869464,6848417:papier:4207:6869326,6849526:papier:4207:6868889,6851003:papier:4207:6868668,6852117:papier:4207:6868733,6852533:papier:4207:6869094,6852534:papier:4207:6868970,6853215:papier:4207:6861412,6853569:papier:4207:6861434,6853919:papier:4207:6868626,6855642:papier:4207:6860809,6859123:papier:4207:6861840,6860530:papier:4207:6861206,6860531:papier:4207:6861044,6860532:papier:4207:6860974,6860535:papier:4207:6860929,6860537:papier:4207:6860834,6860538:papier:4207:6860765,6861512:papier:4207:6868217,6861514:papier:4207:6868293,6861516:papier:4207:6868247,6861517:papier:4'}, {'unwanted_material': 'papier', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.9686131104416358, 'nb_photo': 148871, 'list_port_cont': '6613100,6614114,6614594,6615529,6616034,6626462,6627859,6628399,6628843,6629142,6629212,6629275,6635305,6635363,6635424,6639712,6640665,6641601,6642549,6644429,6646359,6647443,6648156,6648888,6654374,6655830,6657171,6658366,6659151,6659226,6660896,6661088,6664555,6664630,6664696,6664799,6664931,6665226,6666045,6666080,6666650,6666767,6666948,6667998,6668669,6668911,6669246,6669487,6669556,6669704,6669775,6669818,6670116,6671162,6671475,6671569,6671851,6672303,6674157,6674272,6674793,6676777,6678463,6681194,6683426,6683976,6700220,6702213,6704717,6706880,6708963,6709412,6709561,6710050,6710787,6711302,6712597,6716513,6718354,6718721,6718876,6719283,6719641,6719885,6720392,6720623,6722156,6722577,6722881,6723438,6723515,6744914,6745255,6746412,6748609,6748948,6749033,6758834,6760258,6760534,6761446,6762006,6762872,6774101,6774621,6775292,6776474,6776980,6813189,6834129,6834360,6834694,6835804,6836115,6837019,6839283,6839590,6841139,6842937,6846030,6847507,6847554,6849265,6850417,6851367,6852769,6853226,6853512,', 'assoc_port': '6612572:6613100,6613636:6614114,6614336:6614594,6614890:6615529,6614891:6616034,6625827:6626462,6627447:6627859,6627801:6628399,6627802:6629275,6628455:6628843,6628735:6629142,6628736:6629212,6630577:6664555,6630578:6664696,6630579:6664630,6630580:6664931,6630581:6665226,6630822:6666080,6630823:6666045,6631154:6667998,6631384:6668669,6631385:6668911,6631618:6669775,6631619:6669818,6631901:6670116,6632901:6671162,6632902:6671475,6633542:6635424,6633543:6635305,6633544:6671851,6633545:6635363,6633546:6672303,6635651:6661088,6635653:6660896,6639123:6644429,6639124:6639712,6639695:6642549,6640066:6640665,6640928:6641601,6645791:6646359,6646682:6647443,6647202:6648156,6648217:6648888,6652800:6654374,6655088:6655830,6656505:6657171,6656506:6658366,6657819:6659151,6659025:6659226,6663042:6664799,6665067:6666948,6665718:6666650,6665719:6666767,6668654:6669704,6668655:6669246,6668656:6669487,6668657:6669556,6670886:6671569,6671756:6676777,6673385:6674157,6673850:6674272,6674357:6674793,6678032:6678463,6680947:6681194,', 'assoc_mat': '6612572:papier:4209:6613100,6613636:papier:4209:6614114,6614336:papier:4209:6614594,6614890:papier:4209:6615529,6614891:papier:4209:6616034,6625827:papier:4209:6626462,6627447:papier:4209:6627859,6627801:papier:4209:6628399,6627802:papier:4209:6629275,6628455:papier:4209:6628843,6628735:papier:4209:6629142,6628736:papier:4209:6629212,6630577:papier:4209:6664555,6630578:papier:4209:6664696,6630579:papier:4209:6664630,6630580:papier:4209:6664931,6630581:papier:4209:6665226,6630822:papier:4209:6666080,6630823:papier:4209:6666045,6631154:papier:4209:6667998,6631384:papier:4209:6668669,6631385:papier:4209:6668911,6631618:papier:4209:6669775,6631619:papier:4209:6669818,6631901:papier:4209:6670116,6632901:papier:4209:6671162,6632902:papier:4209:6671475,6633542:papier:4209:6635424,6633543:papier:4209:6635305,6633544:papier:4209:6671851,6633545:papier:4209:6635363,6633546:papier:4209:6672303,6635651:papier:4209:6661088,6635653:papier:4209:6660896,6639123:papier:4209:6644429,6639124:papier:4209:6639712,6639695:papier:4'}, {'unwanted_material': 'papier', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.9902901831097267, 'nb_photo': 2955, 'list_port_cont': '6860598,6860874,6861066,6861173,6861345,6861539,6861959,6862604,6863422,6863593,6864917,6865058,6866086,6866174,6876761,6877571,6877655,6877751,6881620,6886395,6888025,6889756,6894811,6902822,6905423,6907081,6909151,6920947', 'assoc_port': '6860529:6861066,6860533:6861173,6860534:6860598,6860536:6860874,6861163:6861539,6861164:6861345,6861513:6861959,6861515:6862604,6863219:6863593,6863221:6863422,6864847:6866174,6864849:6865058,6864854:6864917,6865737:6866086,6876690:6876761,6877477:6877751,6877481:6877655,6877482:6877571,6881493:6881620,6886257:6886395,6887731:6888025,6889188:6889756,6894658:6894811,6899249:6909151,6900676:6902822,6903711:6907081,6905103:6905423,6920015:6920947', 'assoc_mat': '6860529:papier:4209:6861066,6860533:papier:4209:6861173,6860534:papier:4209:6860598,6860536:papier:4209:6860874,6861163:papier:4209:6861539,6861164:papier:4209:6861345,6861513:papier:4209:6861959,6861515:papier:4209:6862604,6863219:papier:4209:6863593,6863221:papier:4209:6863422,6864847:papier:4209:6866174,6864849:papier:4209:6865058,6864854:papier:4209:6864917,6865737:papier:4209:6866086,6876690:papier:4209:6876761,6877477:papier:4209:6877751,6877481:papier:4209:6877655,6877482:papier:4209:6877571,6881493:papier:4209:6881620,6886257:papier:4209:6886395,6887731:papier:4209:6888025,6889188:papier:4209:6889756,6894658:papier:4209:6894811,6899249:papier:4209:6909151,6900676:papier:4209:6902822,6903711:papier:4209:6907081,6905103:papier:4209:6905423,6920015:papier:4209:6920947'}, {'unwanted_material': 'papier', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.010245453120925422, 'nb_photo': 31, 'list_port_cont': '6647707,6861711', 'assoc_port': '6647200:6647707,6861521:6861711', 'assoc_mat': '6647200:papier:4200:6647707,6861521:papier:4200:6861711'}, {'unwanted_material': 'pehd', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 2.520265649788176e-05, 'nb_photo': 2, 'list_port_cont': '6678458', 'assoc_port': '6678032:6678458', 'assoc_mat': '6678032:pehd:4209:6678458'}, {'unwanted_material': 'pet_clair', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0009691335779369147, 'nb_photo': 50, 'list_port_cont': '6862208,6862361,6862535,6862580,6862650,6867917,6868229,6868248,6868281,6868376,6868672,6869102,6869324,6869466,6869550,6877011,6877046,6877191,6877677,6877777,6877812,6877923,6877993,6878035,6886875,6888290,6891353,6896153,6898498,6904509,6907044,6917257,6922719,6925591', 'assoc_port': '6790887:6862535,6832752:6877923,6833565:6878035,6834821:6877993,6836050:6877777,6838973:6877677,6842390:6877812,6846972:6869550,6846973:6869466,6848417:6869324,6851003:6868672,6852533:6869102,6861512:6868229,6861514:6868281,6861516:6868248,6861517:6868376,6861519:6862580,6861520:6862650,6861524:6862208,6861635:6862361,6864848:6867917,6876683:6877191,6876685:6877011,6876686:6877046,6886258:6886875,6887695:6888290,6889190:6891353,6895749:6896153,6898175:6898498,6904057:6904509,6906036:6907044,6917033:6917257,6921897:6922719,6925484:6925591', 'assoc_mat': '6790887:pet_clair:4207:6862535,6832752:pet_clair:4207:6877923,6833565:pet_clair:4207:6878035,6834821:pet_clair:4207:6877993,6836050:pet_clair:4207:6877777,6838973:pet_clair:4207:6877677,6842390:pet_clair:4207:6877812,6846972:pet_clair:4207:6869550,6846973:pet_clair:4207:6869466,6848417:pet_clair:4207:6869324,6851003:pet_clair:4207:6868672,6852533:pet_clair:4207:6869102,6861512:pet_clair:4207:6868229,6861514:pet_clair:4207:6868281,6861516:pet_clair:4207:6868248,6861517:pet_clair:4207:6868376,6861519:pet_clair:4207:6862580,6861520:pet_clair:4207:6862650,6861524:pet_clair:4207:6862208,6861635:pet_clair:4207:6862361,6864848:pet_clair:4207:6867917,6876683:pet_clair:4207:6877191,6876685:pet_clair:4207:6877011,6876686:pet_clair:4207:6877046,6886258:pet_clair:4207:6886875,6887695:pet_clair:4207:6888290,6889190:pet_clair:4207:6891353,6895749:pet_clair:4207:6896153,6898175:pet_clair:4207:6898498,6904057:pet_clair:4207:6904509,6906036:pet_clair:4207:6907044,6917033:pet_clair:4207:6917257,6921897:pet_clair:4207:6922719,6'}, {'unwanted_material': 'pet_clair', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.00038788845724592604, 'nb_photo': 52, 'list_port_cont': '6613103,6648162,6655833,6657176,6658378,6660889,6666083,6666651,6666947,6668915,6669550,6669705,6669777,6671157,6700228,6704711,6706879,6709548,6712610,6720621,6722887,6723439,6744920,6748621,6748942,6761453,6774102,6776478,6837016,6841130,6846026,6853934', 'assoc_port': '6612572:6613103,6630822:6666083,6631385:6668915,6631618:6669777,6632901:6671157,6635653:6660889,6647202:6648162,6655088:6655833,6656505:6657176,6656506:6658378,6665067:6666947,6665718:6666651,6668654:6669705,6668657:6669550,6699726:6700228,6700555:6704711,6706262:6706879,6707499:6709548,6708643:6712610,6720202:6720621,6722573:6722887,6723094:6723439,6744086:6744920,6747584:6748621,6748313:6748942,6761126:6761453,6773386:6774102,6776077:6776478,6836050:6837016,6840554:6841130,6845438:6846026,6852534:6853934', 'assoc_mat': '6612572:pet_clair:4209:6613103,6630822:pet_clair:4209:6666083,6631385:pet_clair:4209:6668915,6631618:pet_clair:4209:6669777,6632901:pet_clair:4209:6671157,6635653:pet_clair:4209:6660889,6647202:pet_clair:4209:6648162,6655088:pet_clair:4209:6655833,6656505:pet_clair:4209:6657176,6656506:pet_clair:4209:6658378,6665067:pet_clair:4209:6666947,6665718:pet_clair:4209:6666651,6668654:pet_clair:4209:6669705,6668657:pet_clair:4209:6669550,6699726:pet_clair:4209:6700228,6700555:pet_clair:4209:6704711,6706262:pet_clair:4209:6706879,6707499:pet_clair:4209:6709548,6708643:pet_clair:4209:6712610,6720202:pet_clair:4209:6720621,6722573:pet_clair:4209:6722887,6723094:pet_clair:4209:6723439,6744086:pet_clair:4209:6744920,6747584:pet_clair:4209:6748621,6748313:pet_clair:4209:6748942,6761126:pet_clair:4209:6761453,6773386:pet_clair:4209:6774102,6776077:pet_clair:4209:6776478,6836050:pet_clair:4209:6837016,6840554:pet_clair:4209:6841130,6845438:pet_clair:4209:6846026,6852534:pet_clair:4209:6853934'}, {'unwanted_material': 'pet_clair', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.00042088720046359597, 'nb_photo': 1, 'list_port_cont': '6862605', 'assoc_port': '6861515:6862605', 'assoc_mat': '6861515:pet_clair:4209:6862605'}, {'unwanted_material': 'pet_fonce', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0008578698895049885, 'nb_photo': 28, 'list_port_cont': '6860798,6861839,6862207,6868256,6868671,6869549,6877674,6877708,6877816,6877879,6878008,6886869,6888301,6889467,6898496,6904520,6915175,6922716', 'assoc_port': '6832753:6877879,6834821:6878008,6838973:6877674,6840554:6877708,6842390:6877816,6846972:6869549,6851003:6868671,6855642:6860798,6859123:6861839,6861516:6868256,6861524:6862207,6886258:6886869,6887695:6888301,6887698:6889467,6898175:6898496,6904057:6904520,6914259:6915175,6921897:6922716', 'assoc_mat': '6832753:pet_fonce:4207:6877879,6834821:pet_fonce:4207:6878008,6838973:pet_fonce:4207:6877674,6840554:pet_fonce:4207:6877708,6842390:pet_fonce:4207:6877816,6846972:pet_fonce:4207:6869549,6851003:pet_fonce:4207:6868671,6855642:pet_fonce:4207:6860798,6859123:pet_fonce:4207:6861839,6861516:pet_fonce:4207:6868256,6861524:pet_fonce:4207:6862207,6886258:pet_fonce:4207:6886869,6887695:pet_fonce:4207:6888301,6887698:pet_fonce:4207:6889467,6898175:pet_fonce:4207:6898496,6904057:pet_fonce:4207:6904520,6914259:pet_fonce:4207:6915175,6921897:pet_fonce:4207:6922716'}, {'unwanted_material': 'pet_fonce', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.002001176734086916, 'nb_photo': 165, 'list_port_cont': '6613106,6614596,6626457,6628830,6629210,6635370,6641607,6642548,6644426,6646347,6647448,6648893,6654377,6659140,6659225,6660883,6661079,6664546,6664700,6665227,6666079,6666644,6666756,6666950,6668662,6669252,6669485,6669785,6671575,6674270,6678461,6683425,6683978,6702227,6704714,6706892,6708972,6709423,6709577,6711293,6712608,6718881,6719276,6719647,6720634,6722892,6723506,6744910,6748597,6762003,6774109,6776983,6841135,6842934,6847565,6859391', 'assoc_port': '6612572:6613106,6614336:6614596,6625827:6626457,6628455:6628830,6628736:6629210,6630577:6664546,6630578:6664700,6630581:6665227,6630822:6666079,6631384:6668662,6631618:6669785,6633545:6635370,6635651:6661079,6635653:6660883,6639123:6644426,6639695:6642548,6640928:6641607,6645791:6646347,6646682:6647448,6648217:6648893,6652800:6654377,6657819:6659140,6659025:6659225,6665067:6666950,6665718:6666644,6665719:6666756,6668655:6669252,6668656:6669485,6670886:6671575,6673850:6674270,6678032:6678461,6682633:6683425,6683289:6683978,6700555:6704714,6701501:6702227,6706262:6706892,6706760:6709423,6707163:6708972,6707499:6709577,6708643:6712608,6708644:6711293,6718520:6719276,6718521:6718881,6719441:6719647,6720202:6720634,6722573:6722892,6723192:6723506,6744086:6744910,6747584:6748597,6759349:6762003,6773386:6774109,6775574:6776983,6840554:6841135,6842390:6842934,6846972:6847565,6859123:6859391', 'assoc_mat': '6612572:pet_fonce:4209:6613106,6614336:pet_fonce:4209:6614596,6625827:pet_fonce:4209:6626457,6628455:pet_fonce:4209:6628830,6628736:pet_fonce:4209:6629210,6630577:pet_fonce:4209:6664546,6630578:pet_fonce:4209:6664700,6630581:pet_fonce:4209:6665227,6630822:pet_fonce:4209:6666079,6631384:pet_fonce:4209:6668662,6631618:pet_fonce:4209:6669785,6633545:pet_fonce:4209:6635370,6635651:pet_fonce:4209:6661079,6635653:pet_fonce:4209:6660883,6639123:pet_fonce:4209:6644426,6639695:pet_fonce:4209:6642548,6640928:pet_fonce:4209:6641607,6645791:pet_fonce:4209:6646347,6646682:pet_fonce:4209:6647448,6648217:pet_fonce:4209:6648893,6652800:pet_fonce:4209:6654377,6657819:pet_fonce:4209:6659140,6659025:pet_fonce:4209:6659225,6665067:pet_fonce:4209:6666950,6665718:pet_fonce:4209:6666644,6665719:pet_fonce:4209:6666756,6668655:pet_fonce:4209:6669252,6668656:pet_fonce:4209:6669485,6670886:pet_fonce:4209:6671575,6673850:pet_fonce:4209:6674270,6678032:pet_fonce:4209:6678461,6682633:pet_fonce:4209:6683425,6683289:pet_fonce:4209:6683978,6'}, {'unwanted_material': 'pet_fonce', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.0009882492230258019, 'nb_photo': 2, 'list_port_cont': '6894805', 'assoc_port': '6894658:6894805', 'assoc_mat': '6894658:pet_fonce:4209:6894805'}, {'unwanted_material': 'pet_fonce', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.9832720072705387, 'nb_photo': 21, 'list_port_cont': '6647700,6861697', 'assoc_port': '6647200:6647700,6861521:6861697', 'assoc_mat': '6647200:pet_fonce:4200:6647700,6861521:pet_fonce:4200:6861697'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0006428090611349251, 'nb_photo': 9, 'list_port_cont': '6866496,6868369,6869096,6869546,6878036,6904487,6905142,6905991,6922715', 'assoc_port': '6833565:6878036,6846972:6869546,6852533:6869096,6861517:6868369,6864855:6866496,6903710:6905991,6903712:6904487,6905102:6905142,6921897:6922715', 'assoc_mat': '6833565:pet_opaque:4207:6878036,6846972:pet_opaque:4207:6869546,6852533:pet_opaque:4207:6869096,6861517:pet_opaque:4207:6868369,6864855:pet_opaque:4207:6866496,6903710:pet_opaque:4207:6905991,6903712:pet_opaque:4207:6904487,6905102:pet_opaque:4207:6905142,6921897:pet_opaque:4207:6922715'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0005744915736568625, 'nb_photo': 214, 'list_port_cont': '6613105,6614118,6616031,6627858,6628408,6628839,6629216,6629282,6635308,6635367,6639706,6640660,6641595,6644422,6647441,6655823,6657172,6658373,6659149,6660897,6661077,6664543,6664634,6664804,6664940,6665229,6666078,6666656,6666757,6666952,6667994,6668664,6668914,6669254,6669547,6669695,6669784,6669814,6670110,6671566,6671848,6672292,6674152,6676774,6678456,6681180,6683428,6683979,6700223,6704719,6706884,6709575,6711305,6712607,6718352,6718715,6719280,6719890,6720396,6722151,6722589,6722891,6723435,6744915,6748617,6748952,6749035,6760254,6761440,6774110,6776978,6813183,6834135,6834365,6834702,6835809,6839582,6841141,6842938,6853225,6853523,6859402', 'assoc_port': '6612572:6613105,6613636:6614118,6614891:6616031,6627447:6627858,6627801:6628408,6627802:6629282,6628455:6628839,6628736:6629216,6630577:6664543,6630579:6664634,6630580:6664940,6630581:6665229,6630822:6666078,6631154:6667994,6631384:6668664,6631385:6668914,6631618:6669784,6631619:6669814,6631901:6670110,6633543:6635308,6633544:6671848,6633545:6635367,6633546:6672292,6635651:6661077,6635653:6660897,6639123:6644422,6639124:6639706,6640066:6640660,6640928:6641595,6646682:6647441,6655088:6655823,6656505:6657172,6656506:6658373,6657819:6659149,6663042:6664804,6665067:6666952,6665718:6666656,6665719:6666757,6668654:6669695,6668655:6669254,6668657:6669547,6670886:6671566,6671756:6676774,6673385:6674152,6678032:6678456,6680947:6681180,6682633:6683428,6683289:6683979,6699726:6700223,6700555:6704719,6706262:6706884,6707499:6709575,6708643:6712607,6708644:6711305,6715973:6718352,6716500:6718715,6717201:6719890,6718520:6719280,6719951:6720396,6721826:6722589,6721827:6722151,6722573:6722891,6723094:6723435,6744086:6744915,', 'assoc_mat': '6612572:pet_opaque:4209:6613105,6613636:pet_opaque:4209:6614118,6614891:pet_opaque:4209:6616031,6627447:pet_opaque:4209:6627858,6627801:pet_opaque:4209:6628408,6627802:pet_opaque:4209:6629282,6628455:pet_opaque:4209:6628839,6628736:pet_opaque:4209:6629216,6630577:pet_opaque:4209:6664543,6630579:pet_opaque:4209:6664634,6630580:pet_opaque:4209:6664940,6630581:pet_opaque:4209:6665229,6630822:pet_opaque:4209:6666078,6631154:pet_opaque:4209:6667994,6631384:pet_opaque:4209:6668664,6631385:pet_opaque:4209:6668914,6631618:pet_opaque:4209:6669784,6631619:pet_opaque:4209:6669814,6631901:pet_opaque:4209:6670110,6633543:pet_opaque:4209:6635308,6633544:pet_opaque:4209:6671848,6633545:pet_opaque:4209:6635367,6633546:pet_opaque:4209:6672292,6635651:pet_opaque:4209:6661077,6635653:pet_opaque:4209:6660897,6639123:pet_opaque:4209:6644422,6639124:pet_opaque:4209:6639706,6640066:pet_opaque:4209:6640660,6640928:pet_opaque:4209:6641595,6646682:pet_opaque:4209:6647441,6655088:pet_opaque:4209:6655823,6656505:pet_opaque:4209:6657172,'}) select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(448643,449568,454450,454465,454459,454472,456239,458795,461498,454567,457375,457649,459844,460382,461634,463066,472528,465865,469297,468129,475039,472453,474892,474538,475316,476524,479832,481665,479903)); after get_hostname_from_raspi hasthag : emr hasthag that could be used but not yet : _______carton,background hasthag : jrm hasthag that could be used but not yet : _______papier,background hasthag : aluminium hasthag that could be used but not yet : _______metal,background hasthag : pet_fonce hasthag that could be used but not yet : _______pet_fonce,background hasthag : gm hasthag that could be used but not yet : _______papier,background after impurety_average_per_hashtag VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier filepath : /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? {'emr': {'barquette_opaque': ['barquette_opaque', '0.01%', 44], 'carton': ['carton', '0.78%', 1462], 'ela': ['ela', '0.01%', 3], 'etiquette': ['etiquette', '0.08%', 1049], 'film_plastique': ['film_plastique', '0.01%', 9], 'kraft': ['kraft', '0.11%', 1], 'metal': ['metal', '2.6%', 787], 'papier': ['papier', '96.86%', 148871], 'pehd': ['pehd', '0.0%', 2], 'pet_clair': ['pet_clair', '0.04%', 52], 'pet_fonce': ['pet_fonce', '0.2%', 165], 'pet_opaque': ['pet_opaque', '0.06%', 214]}, 'aluminium': {'carton': ['carton', '1.02%', 16], 'metal': ['metal', '98.97%', 54], 'papier': ['papier', '0.13%', 9]}, 'gm': {'carton': ['carton', '0.29%', 10], 'etiquette': ['etiquette', '0.08%', 16], 'metal': ['metal', '3.43%', 14], 'papier': ['papier', '99.03%', 2955], 'pet_clair': ['pet_clair', '0.04%', 1], 'pet_fonce': ['pet_fonce', '0.1%', 2]}, 'pet_fonce': {'carton': ['carton', '0.59%', 4], 'etiquette': ['etiquette', '0.11%', 5], 'papier': ['papier', '1.02%', 31], 'pet_fonce': ['pet_fonce', '98.33%', 21]}} filepath : /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf hash: 9b473a9b7e6f69a2b2147971a7bf4221 for path: /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf voici le hostname : marlene hash: 9b473a9b7e6f69a2b2147971a7bf4221 for path: /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf ############################### TEST one_day ################################ TODO and TOTEST Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_one_day nb_day : (3, 30) nb deleted : 3 VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier Inconsistency in dashboard_places dashboard_places from input : romainville_petite_presse dashboard_name_from_port found from datou STS from crontab from raspi : Romainville_Presse_1 We force the correct camera_place_name ! TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO TODO : Insert select and so on ***** analysis of device with port 20001 for dashboard romainville_petite_presse ***** **** analysis of day 2022/09/01/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-01 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis of day 2022/09/02/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-02 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis of day 2022/09/03/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-03 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis for all days **** Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 17148, not duplicated 17595, two criteria ok 17148, nb forced upload 0 end of day status of photos as found in sqllite Info from dashboard total number of uploaded images : 0 number of useful images : 17148 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) cvs resume : date,nb_photos,% time,nb ok,uploaded,to upload, % uploaded, nb useful, % completed photos, last_update, remark 2022/09/01,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, 2022/09/02,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, 2022/09/03,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, coverage for this period for every 10 second 2022/09/01/ 0.0 2022/09/02/ 0.0 2022/09/03/ 0.0 mean value for this period : 0.0 coverage for this period for every 20 second 2022/09/01/ 0.0 2022/09/02/ 0.0 2022/09/03/ 0.0 mean value for this period : 0.0 ############################### TEST get_data ################################ TODO and TOTEST VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier nb_day : (0, 31) VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select id from MTRUser.mtr_portfolios where id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(select last_run_id from MTRPhoto.dashboard_entry_day where dashboard_place_id in (select id from MTRPhoto.dashboard_places where name = 'Romainville_Presse_1' and date like '%2022-08%') and created_at like '%2022-08%'))); nb_day : (0, 31) after unwanted_material_data nb_day : (0, 31) after coverage_data after number_of_batch date_start : 2022-08-01 : dt_date_just_month_year : 2022-08-01 00:00:00 : VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! after pl.get_datou_sts_from_crontab : verbose : False no sts found, try to find from database SELECT dri.id FROM MTRPhoto.dashboard_run_ids dri, MTRPhoto.dashboard_entry_day ded, MTRPhoto.dashboard_places dp WHERE dp.name= "Romainville_Presse_1" AND ded.dashboard_place_id=dp.id AND dri.dashboard_entry_day=ded.id AND dri.id=ded.last_run_id AND ded.date >= "2022-08-01" AND ded.date <= "2022-08-31" apple3 {'gm': {'mat': 'gm', 'pht': 4209, 'datou_carac_id': 3994, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'emr': {'mat': 'emr', 'pht': 4207, 'datou_carac_id': 3993, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'carton,background'}, 'jrm': {'mat': 'jrm', 'pht': 3726, 'datou_carac_id': 3459, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'ela': {'mat': 'ela', 'pht': 4203, 'datou_carac_id': 3991, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'ela,background'}, 'pet_clair': {'mat': 'pet_clair', 'pht': 3327, 'datou_carac_id': 3804, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_clair,bouchon,etiquette,barquette_avec_film,background'}, 'film_pedb': {'mat': 'film_pedb', 'pht': 4461, 'datou_carac_id': 4322, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'film_plastique'}, 'pehd_pp': {'mat': 'pehd_pp', 'pht': 4211, 'datou_carac_id': 3995, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pehd,background'}, 'pet_fonce': {'mat': 'pet_fonce', 'pht': 4200, 'datou_carac_id': 4153, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_fonce,background'}, 'aluminium': {'mat': 'aluminium', 'pht': 4205, 'datou_carac_id': 3992, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'metal,background'}, 'refus': {'mat': 'refus', 'pht': 3594, 'datou_carac_id': 3318, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'refus,background'}} SELECT h.hashtag as unwanted_material, substr(dr.hashtag,8) as main_material, ptp.type as pht_type, sum(pcr.value*dr.nombre_balle)/sum(dr.nombre_balle) as ratio, count(distinct mpp.mtr_photo_id) as nb_photo, group_concat(distinct ptp.mtr_portfolio_id_2) as list_port_cont, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_port, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", h.hashtag, ":", cast(ptp.type as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_mat FROM MTRPhoto.dashboard_results dr, MTRPhoto.mtr_port_to_port_ids ptp, MTRUser.mtr_portfolio_photos mpp, MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h WHERE dr.dashboard_run_id IN (449271,454376,454388,454411,454399,454424,451519,452689,468274,455185,456478,457640,459828,461074,462392,463490,464885,465870,469411,470781,897152,474755,474338,475306,477478,478927,479886) AND dr.mtr_portfolio_id=ptp.mtr_portfolio_id_1 AND dr.qualite >= 0 AND mpp.mtr_portfolio_id=ptp.mtr_portfolio_id_2 AND pcr.portfolio_id=ptp.mtr_portfolio_id_1 AND h.hashtag_id = pcr.hashtag_id AND ptp.type = pcr.hashtag_type AND mpp.hide_status = 0 AND ptp.hashtag_id=h.hashtag_id AND ptp.type IN (4209,4207,3726,4203,3327,4461,4211,4200,4205,3594) group by h.hashtag, dr.hashtag, ptp.type; VR TODO TO BETTER PARSE ! ({'unwanted_material': 'autre', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.025985557831013643, 'nb_photo': 16651, 'list_port_cont': '6601211,6601249,6601319,6601376,6601558,6601691,6601783,6601837,6602312,6603611,6603837,6603984,6604939,6606283,6606361,6607127,6607396,6607889,6608318,6608592,6608684,6608960,6609911,6610060,6610278,6610907,6611078,6613725,6614148,6615899,6616064,6616199,6616975,6617461,6617846,6618177,6618947,6620505,6620992,6621119,6621146,6621174,6621388,6621447,6621537,6622105,6622162,6622311,6622378,6622426,6622514,6622647,6622758,6626372,6626974,6626997,6627200,6627837,6628024,6628557,6628624,6629354,6629396,6635806,6635864,6635897,6635947,6635974,6636029,6636109,6636139,6636295,6636316,6636423,6636599,6636674,6636727,6636791,6637048,6637689,6637708,6637756,6637854,6637954,6637974,6638095,6638421,6638597,6638696,6638833,6638947,6638968,6639187,6639299,6639353,6639652,6639761,6639844,6639904,6639965,6640123,6640725,6640745,6640776,6640959,6641209,6641306,6641333,6641484,6641521,6641655,6641820,6641954,6642026,6642079,6642285,6643237,6643372,6643479,6643603,6643631,6643664,6643695,6643718,6643895,6644078,6644458,6644663,', 'assoc_port': '6600535:6601249,6600537:6601691,6600543:6601211,6600545:6601376,6600547:6601558,6600550:6601783,6600553:6601319,6601140:6602312,6601199:6601837,6602727:6603837,6602729:6603984,6602732:6603611,6604400:6604939,6604702:6606361,6605500:6606283,6605502:6607127,6606682:6608318,6606685:6607889,6606687:6607396,6607836:6608592,6607838:6608684,6608144:6608960,6609197:6610060,6609198:6609911,6609963:6610278,6610497:6611078,6610499:6610907,6612953:6613725,6613333:6614148,6614966:6616199,6614968:6616064,6615360:6615899,6616171:6616975,6616960:6618177,6616966:6617461,6616967:6617846,6618310:6618947,6620036:6620505,6620039:6621388,6620042:6620992,6620404:6621146,6620441:6621447,6620445:6621174,6620446:6621537,6620449:6621119,6621639:6622647,6621642:6622758,6621645:6622514,6621650:6622378,6621652:6622426,6621655:6622162,6621656:6622105,6621661:6622311,6625901:6626372,6626270:6626974,6626272:6626997,6626275:6627200,6627097:6627837,6627099:6628024,6627946:6628557,6627948:6628624,6628988:6629354,6628991:6629396,6630847:6635947,', 'assoc_mat': '6600535:autre:3327:6601249,6600537:autre:3327:6601691,6600543:autre:3327:6601211,6600545:autre:3327:6601376,6600547:autre:3327:6601558,6600550:autre:3327:6601783,6600553:autre:3327:6601319,6601140:autre:3327:6602312,6601199:autre:3327:6601837,6602727:autre:3327:6603837,6602729:autre:3327:6603984,6602732:autre:3327:6603611,6604400:autre:3327:6604939,6604702:autre:3327:6606361,6605500:autre:3327:6606283,6605502:autre:3327:6607127,6606682:autre:3327:6608318,6606685:autre:3327:6607889,6606687:autre:3327:6607396,6607836:autre:3327:6608592,6607838:autre:3327:6608684,6608144:autre:3327:6608960,6609197:autre:3327:6610060,6609198:autre:3327:6609911,6609963:autre:3327:6610278,6610497:autre:3327:6611078,6610499:autre:3327:6610907,6612953:autre:3327:6613725,6613333:autre:3327:6614148,6614966:autre:3327:6616199,6614968:autre:3327:6616064,6615360:autre:3327:6615899,6616171:autre:3327:6616975,6616960:autre:3327:6618177,6616966:autre:3327:6617461,6616967:autre:3327:6617846,6618310:autre:3327:6618947,6620036:autre:3327:662050'}, {'unwanted_material': 'barquette_avec_film', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.0050634087913797535, 'nb_photo': 1051, 'list_port_cont': '6601216,6601255,6601316,6601366,6601556,6601694,6601795,6601849,6602314,6603610,6603845,6603981,6604938,6606272,6606380,6607136,6607388,6607885,6608324,6608593,6608675,6608967,6609910,6610048,6610273,6611082,6613721,6614147,6615898,6616193,6616972,6617468,6618176,6618950,6620509,6620988,6621144,6621167,6621391,6621449,6622102,6622163,6622316,6622379,6622436,6622519,6622752,6626364,6626973,6627004,6627197,6627840,6628023,6628564,6628623,6629352,6629394,6635811,6635862,6635901,6635945,6635972,6636036,6636112,6636138,6636318,6636424,6636724,6637044,6637685,6637716,6637764,6637852,6637945,6637984,6638094,6638596,6638700,6638835,6638944,6638974,6639298,6639361,6639645,6639764,6639852,6639896,6639968,6640121,6640722,6640783,6640963,6641211,6641294,6641332,6641486,6641520,6641652,6641944,6642022,6642080,6642290,6643373,6643478,6643608,6643683,6643716,6643902,6644079,6644456,6644652,6645162,6645304,6645766,6647408,6648376,6648805,6649738,6651383,6651686,6654487,6654608,6655045,6655877,6656213,6656668,6659085,6660221,', 'assoc_port': '6600535:6601255,6600537:6601694,6600543:6601216,6600545:6601366,6600547:6601556,6600550:6601795,6600553:6601316,6601140:6602314,6601199:6601849,6602727:6603845,6602729:6603981,6602732:6603610,6604400:6604938,6604702:6606380,6605500:6606272,6605502:6607136,6606682:6608324,6606685:6607885,6606687:6607388,6607836:6608593,6607838:6608675,6608144:6608967,6609197:6610048,6609198:6609910,6609963:6610273,6610497:6611082,6612953:6613721,6613333:6614147,6614966:6616193,6615360:6615898,6616171:6616972,6616960:6618176,6616966:6617468,6618310:6618950,6620036:6620509,6620039:6621391,6620042:6620988,6620404:6621144,6620441:6621449,6620445:6621167,6621642:6622752,6621645:6622519,6621650:6622379,6621652:6622436,6621655:6622163,6621656:6622102,6621661:6622316,6625901:6626364,6626270:6626973,6626272:6627004,6626275:6627197,6627097:6627840,6627099:6628023,6627946:6628564,6627948:6628623,6628988:6629352,6628991:6629394,6630847:6635945,6630849:6636036,6630851:6635972,6630854:6637685,6630934:6637764,6630936:6637716,6630938:6638094,', 'assoc_mat': '6600535:barquette_avec_film:3327:6601255,6600537:barquette_avec_film:3327:6601694,6600543:barquette_avec_film:3327:6601216,6600545:barquette_avec_film:3327:6601366,6600547:barquette_avec_film:3327:6601556,6600550:barquette_avec_film:3327:6601795,6600553:barquette_avec_film:3327:6601316,6601140:barquette_avec_film:3327:6602314,6601199:barquette_avec_film:3327:6601849,6602727:barquette_avec_film:3327:6603845,6602729:barquette_avec_film:3327:6603981,6602732:barquette_avec_film:3327:6603610,6604400:barquette_avec_film:3327:6604938,6604702:barquette_avec_film:3327:6606380,6605500:barquette_avec_film:3327:6606272,6605502:barquette_avec_film:3327:6607136,6606682:barquette_avec_film:3327:6608324,6606685:barquette_avec_film:3327:6607885,6606687:barquette_avec_film:3327:6607388,6607836:barquette_avec_film:3327:6608593,6607838:barquette_avec_film:3327:6608675,6608144:barquette_avec_film:3327:6608967,6609197:barquette_avec_film:3327:6610048,6609198:barquette_avec_film:3327:6609910,6609963:barquette_avec_film:3327:6610273'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0006065490165070008, 'nb_photo': 9, 'list_port_cont': '6603426,6627334,6634749,6723417,6780640,6834099,6852168', 'assoc_port': '6602731:6603426,6626721:6627334,6630937:6634749,6671808:6834099,6722813:6723417,6780026:6780640,6851650:6852168', 'assoc_mat': '6602731:barquette_opaque:4203:6603426,6626721:barquette_opaque:4203:6627334,6630937:barquette_opaque:4203:6634749,6671808:barquette_opaque:4203:6834099,6722813:barquette_opaque:4203:6723417,6780026:barquette_opaque:4203:6780640,6851650:barquette_opaque:4203:6852168'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0005293780694215035, 'nb_photo': 1, 'list_port_cont': '6692906', 'assoc_port': '6691778:6692906', 'assoc_mat': '6691778:barquette_opaque:4209:6692906'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.008662761320534096, 'nb_photo': 659, 'list_port_cont': '6600860,6600903,6601023,6601433,6602273,6602593,6603403,6606082,6607689,6608069,6608289,6609462,6610653,6614532,6615427,6615792,6616525,6618779,6618922,6620328,6621053,6621664,6621960,6622002,6622135,6622199,6624057,6626607,6627268,6628481,6629183,6629252,6634382,6635139,6638289,6638441,6640989,6642058,6642706,6650155,6650276,6650959,6658495,6664041,6668175,6669610,6672035,6674014,6674549,6674630,6674772,6675081,6675105,6675139,6675482,6676524,6677132,6677217,6678042,6678216,6678577,6678660,6678688,6678769,6678960,6678988,6681252,6687138,6692292,6693194,6693681,6693891,6694045,6694217,6694420,6698421,6698805,6699377,6699427,6700669,6707231,6711232,6711409,6711495,6712142,6713437,6713607,6713753,6714397,6716562,6717498,6717555,6718025,6719023,6719736,6720660,6720723,6721143,6721925,6722677,6723045,6723102,6724740,6724912,6724973,6725830,6729866,6730263,6730718,6731855,6732324,6732440,6735375,6738136,6740197,6740602,6741090,6741939,6743256,6743597,6743876,6745278,6747827,6751581,6751678,6751683,6752336,6754140,', 'assoc_port': '10844008:10846065,10844009:10845784,10844011:10845552,10844012:10845981,10844014:10845176,10844016:10845161,10844017:10845129,10844018:10844946,10844019:10845048,10844020:10844679,12107427:12108055,12107429:12107821,6600536:6601433,6600544:6600860,6600546:6601023,6600551:6600903,6601138:6602593,6601649:6602273,6602733:6603403,6604399:6608069,6605499:6610653,6605688:6606082,6606683:6607689,6607839:6608289,6609199:6609462,6614199:6614532,6614969:6615427,6615362:6615792,6616173:6616525,6618307:6618779,6618311:6618922,6620040:6620328,6620422:6621664,6620444:6621053,6621638:6624057,6621647:6622199,6621651:6622135,6621654:6621960,6621659:6622002,6626268:6626607,6627098:6627268,6627945:6628481,6628987:6629183,6628989:6629252,6630852:6674014,6630932:6674630,6630935:6674772,6630939:6675105,6631065:6675081,6631069:6675482,6631193:6635139,6631477:6676524,6631489:6677132,6631492:6677217,6631967:6634382,6632585:6678216,6632660:6678577,6632663:6678660,6632714:6678769,6632787:6678688,6632877:6678960,6632881:6678988,6635669:', 'assoc_mat': '10844008:barquette_opaque:4211:10846065,10844009:barquette_opaque:4211:10845784,10844011:barquette_opaque:4211:10845552,10844012:barquette_opaque:4211:10845981,10844014:barquette_opaque:4211:10845176,10844016:barquette_opaque:4211:10845161,10844017:barquette_opaque:4211:10845129,10844018:barquette_opaque:4211:10844946,10844019:barquette_opaque:4211:10845048,10844020:barquette_opaque:4211:10844679,12107427:barquette_opaque:4211:12108055,12107429:barquette_opaque:4211:12107821,6600536:barquette_opaque:4211:6601433,6600544:barquette_opaque:4211:6600860,6600546:barquette_opaque:4211:6601023,6600551:barquette_opaque:4211:6600903,6601138:barquette_opaque:4211:6602593,6601649:barquette_opaque:4211:6602273,6602733:barquette_opaque:4211:6603403,6604399:barquette_opaque:4211:6608069,6605499:barquette_opaque:4211:6610653,6605688:barquette_opaque:4211:6606082,6606683:barquette_opaque:4211:6607689,6607839:barquette_opaque:4211:6608289,6609199:barquette_opaque:4211:6609462,6614199:barquette_opaque:4211:6614532,6614969:barq'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.0009915902324084817, 'nb_photo': 5, 'list_port_cont': '6649286,6687459,6784020,6813915,6920756', 'assoc_port': '6631070:6649286,6686295:6687459,6782249:6784020,6813389:6813915,6920100:6920756', 'assoc_mat': '6631070:barquette_opaque:4200:6649286,6686295:barquette_opaque:4200:6687459,6782249:barquette_opaque:4200:6784020,6813389:barquette_opaque:4200:6813915,6920100:barquette_opaque:4200:6920756'}, {'unwanted_material': 'bouchon', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.002148587132747826, 'nb_photo': 2481, 'list_port_cont': '6601204,6601258,6601320,6601369,6601559,6601701,6601789,6601838,6602303,6603616,6603840,6603991,6604940,6606271,6606371,6607138,6607392,6607893,6608314,6608584,6608679,6608959,6609909,6610052,6610283,6610913,6611085,6613733,6614158,6615892,6616060,6616205,6616979,6617470,6617845,6618175,6618953,6620508,6620996,6621149,6621177,6621393,6621437,6621531,6622101,6622159,6622323,6622381,6622429,6622524,6622659,6622748,6626363,6626980,6626996,6627191,6627830,6628020,6628559,6628625,6629350,6629387,6635807,6635871,6635902,6635950,6635976,6636031,6636142,6636288,6636337,6636425,6636596,6636683,6636733,6636798,6637042,6637681,6637704,6637763,6637859,6637953,6637977,6638087,6638418,6638605,6638699,6638834,6638943,6638976,6639182,6639296,6639351,6639643,6639763,6639846,6639907,6639971,6640125,6640715,6640780,6640955,6641210,6641300,6641327,6641485,6641509,6641661,6641818,6641952,6642084,6642292,6643375,6643473,6643600,6643630,6643663,6643691,6643711,6643907,6644068,6644455,6644662,6645166,6645298,6645757,6647406,6648003,', 'assoc_port': '6600535:6601258,6600537:6601701,6600543:6601204,6600545:6601369,6600547:6601559,6600550:6601789,6600553:6601320,6601140:6602303,6601199:6601838,6602727:6603840,6602729:6603991,6602732:6603616,6604400:6604940,6604702:6606371,6605500:6606271,6605502:6607138,6606682:6608314,6606685:6607893,6606687:6607392,6607836:6608584,6607838:6608679,6608144:6608959,6609197:6610052,6609198:6609909,6609963:6610283,6610497:6611085,6610499:6610913,6612953:6613733,6613333:6614158,6614966:6616205,6614968:6616060,6615360:6615892,6616171:6616979,6616960:6618175,6616966:6617470,6616967:6617845,6618310:6618953,6620036:6620508,6620039:6621393,6620042:6620996,6620404:6621149,6620441:6621437,6620445:6621177,6620446:6621531,6621639:6622659,6621642:6622748,6621645:6622524,6621650:6622381,6621652:6622429,6621655:6622159,6621656:6622101,6621661:6622323,6625901:6626363,6626270:6626980,6626272:6626996,6626275:6627191,6627097:6627830,6627099:6628020,6627946:6628559,6627948:6628625,6628988:6629350,6628991:6629387,6630847:6635950,6630849:6636031,', 'assoc_mat': '6600535:bouchon:3327:6601258,6600537:bouchon:3327:6601701,6600543:bouchon:3327:6601204,6600545:bouchon:3327:6601369,6600547:bouchon:3327:6601559,6600550:bouchon:3327:6601789,6600553:bouchon:3327:6601320,6601140:bouchon:3327:6602303,6601199:bouchon:3327:6601838,6602727:bouchon:3327:6603840,6602729:bouchon:3327:6603991,6602732:bouchon:3327:6603616,6604400:bouchon:3327:6604940,6604702:bouchon:3327:6606371,6605500:bouchon:3327:6606271,6605502:bouchon:3327:6607138,6606682:bouchon:3327:6608314,6606685:bouchon:3327:6607893,6606687:bouchon:3327:6607392,6607836:bouchon:3327:6608584,6607838:bouchon:3327:6608679,6608144:bouchon:3327:6608959,6609197:bouchon:3327:6610052,6609198:bouchon:3327:6609909,6609963:bouchon:3327:6610283,6610497:bouchon:3327:6611085,6610499:bouchon:3327:6610913,6612953:bouchon:3327:6613733,6613333:bouchon:3327:6614158,6614966:bouchon:3327:6616205,6614968:bouchon:3327:6616060,6615360:bouchon:3327:6615892,6616171:bouchon:3327:6616979,6616960:bouchon:3327:6618175,6616966:bouchon:3327:6617470,6616967:b'}, {'unwanted_material': 'carton', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.015781843756573148, 'nb_photo': 1998, 'list_port_cont': '6603428,6608804,6618325,6624159,6627335,6634751,6634798,6660090,6698369,6704158,6712719,6720528,6723218,6723410,6735159,6735455,6741137,6754060,6761153,6768731,6775020,6780639,6799269,6800310,6801317,6806363,6808106,6808745,6810123,6812813,6822554,6826369,6834087,6838770,6846433,6852159,6863267,6865659,6869925,6876020,6924800,6930493,6940832,10844173', 'assoc_port': '10844025:10844173,6602731:6603428,6608146:6608804,6617603:6618325,6620405:6624159,6626721:6627335,6630937:6634751,6631483:6634798,6632879:6660090,6638160:6704158,6653521:6723218,6664117:6735455,6671808:6834087,6682068:6768731,6685476:6799269,6685991:6800310,6686304:6801317,6690887:6735159,6696473:6698369,6712332:6712719,6718634:6720528,6722813:6723410,6740022:6741137,6745199:6810123,6753416:6754060,6760133:6761153,6765566:6808745,6774410:6775020,6780026:6780639,6786466:6808106,6793236:6806363,6811950:6812813,6820599:6822554,6825858:6826369,6837012:6838770,6846007:6846433,6851650:6852159,6863104:6863267,6865559:6865659,6869544:6869925,6875674:6876020,6894306:6930493,6900378:6940832,6923650:6924800', 'assoc_mat': '10844025:carton:4203:10844173,6602731:carton:4203:6603428,6608146:carton:4203:6608804,6617603:carton:4203:6618325,6620405:carton:4203:6624159,6626721:carton:4203:6627335,6630937:carton:4203:6634751,6631483:carton:4203:6634798,6632879:carton:4203:6660090,6638160:carton:4203:6704158,6653521:carton:4203:6723218,6664117:carton:4203:6735455,6671808:carton:4203:6834087,6682068:carton:4203:6768731,6685476:carton:4203:6799269,6685991:carton:4203:6800310,6686304:carton:4203:6801317,6690887:carton:4203:6735159,6696473:carton:4203:6698369,6712332:carton:4203:6712719,6718634:carton:4203:6720528,6722813:carton:4203:6723410,6740022:carton:4203:6741137,6745199:carton:4203:6810123,6753416:carton:4203:6754060,6760133:carton:4203:6761153,6765566:carton:4203:6808745,6774410:carton:4203:6775020,6780026:carton:4203:6780639,6786466:carton:4203:6808106,6793236:carton:4203:6806363,6811950:carton:4203:6812813,6820599:carton:4203:6822554,6825858:carton:4203:6826369,6837012:carton:4203:6838770,6846007:carton:4203:6846433,6851650:carton'}, {'unwanted_material': 'carton', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.9743551532309155, 'nb_photo': 4, 'list_port_cont': '12107738,12108208', 'assoc_port': '12107419:12108208,12107426:12107738', 'assoc_mat': '12107419:carton:4207:12108208,12107426:carton:4207:12107738'}, {'unwanted_material': 'carton', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.004418352534261281, 'nb_photo': 161, 'list_port_cont': '6600769,6601467,6602156,6616727,6617535,6617707,6618976,6620415,6621476,6621927,6622071,6625405,6627053,6637137,6638503,6641082,6644340,6647280,6648123,6651932,6655484,6657576,6664017,6666562,6667513,6668138,6668212,6669002,6669287,6669404,6670447,6670556,6670902,6672181,6673400,6674363,6678083,6682353,6690824,6692905,6694179,6695453,6695907,6697455,6699015,6711039,6712083,6712246,6716814,6721878,6723683,6740491,6742328,6751444,6754763,6812438,6824357', 'assoc_port': '6600533:6601467,6600540:6602156,6600552:6600769,6616168:6616727,6616961:6617707,6616963:6617535,6618302:6618976,6620037:6620415,6620443:6621476,6621640:6622071,6621646:6621927,6625237:6625405,6626722:6627053,6631064:6666562,6631190:6668138,6631196:6668212,6631480:6669002,6631482:6669287,6631491:6669404,6632580:6670447,6632661:6670556,6632883:6670902,6633864:6637137,6638162:6638503,6638777:6644340,6640593:6641082,6646444:6647280,6647214:6648123,6650509:6651932,6654838:6655484,6656897:6657576,6663620:6664017,6664426:6667513,6671806:6672181,6672700:6673400,6673893:6674363,6677866:6678083,6681682:6682353,6682898:6690824,6685513:6697455,6688873:6699015,6691778:6692905,6692502:6694179,6694731:6695907,6694740:6695453,6709375:6711039,6710867:6712246,6711682:6712083,6716079:6716814,6721779:6721878,6723504:6723683,6739465:6742328,6740030:6740491,6741911:6754763,6750987:6751444,6811912:6812438,6823971:6824357', 'assoc_mat': '6600533:carton:4209:6601467,6600540:carton:4209:6602156,6600552:carton:4209:6600769,6616168:carton:4209:6616727,6616961:carton:4209:6617707,6616963:carton:4209:6617535,6618302:carton:4209:6618976,6620037:carton:4209:6620415,6620443:carton:4209:6621476,6621640:carton:4209:6622071,6621646:carton:4209:6621927,6625237:carton:4209:6625405,6626722:carton:4209:6627053,6631064:carton:4209:6666562,6631190:carton:4209:6668138,6631196:carton:4209:6668212,6631480:carton:4209:6669002,6631482:carton:4209:6669287,6631491:carton:4209:6669404,6632580:carton:4209:6670447,6632661:carton:4209:6670556,6632883:carton:4209:6670902,6633864:carton:4209:6637137,6638162:carton:4209:6638503,6638777:carton:4209:6644340,6640593:carton:4209:6641082,6646444:carton:4209:6647280,6647214:carton:4209:6648123,6650509:carton:4209:6651932,6654838:carton:4209:6655484,6656897:carton:4209:6657576,6663620:carton:4209:6664017,6664426:carton:4209:6667513,6671806:carton:4209:6672181,6672700:carton:4209:6673400,6673893:carton:4209:6674363,6677866:carton:4'}, {'unwanted_material': 'carton', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.0034360485265016666, 'nb_photo': 74, 'list_port_cont': '6846228,6863059,6869289,6886849,6890963,6894674,6908912,6914945,6920514,6922344,6960443,12107789,12108019,12108273,12108337,12108377,12108542,12108682,12151219', 'assoc_port': '12107418:12151219,12107420:12108682,12107421:12108542,12107422:12108377,12107424:12108273,12107425:12108337,12107428:12108019,12107430:12107789,6845636:6846228,6862271:6863059,6868823:6869289,6886556:6886849,6890813:6890963,6894304:6894674,6902262:6908912,6914405:6914945,6919130:6920514,6919131:6960443,6920918:6922344', 'assoc_mat': '12107418:carton:4209:12151219,12107420:carton:4209:12108682,12107421:carton:4209:12108542,12107422:carton:4209:12108377,12107424:carton:4209:12108273,12107425:carton:4209:12108337,12107428:carton:4209:12108019,12107430:carton:4209:12107789,6845636:carton:4209:6846228,6862271:carton:4209:6863059,6868823:carton:4209:6869289,6886556:carton:4209:6886849,6890813:carton:4209:6890963,6894304:carton:4209:6894674,6902262:carton:4209:6908912,6914405:carton:4209:6914945,6919130:carton:4209:6920514,6919131:carton:4209:6960443,6920918:carton:4209:6922344'}, {'unwanted_material': 'carton', 'main_material': 'pcnc', 'pht_type': 4207, 'ratio': 0.7882882989748035, 'nb_photo': 99, 'list_port_cont': '6617257,6617678,6633558,6659532', 'assoc_port': '6616962:6617257,6616964:6617678,6631478:6633558,6652939:6659532', 'assoc_mat': '6616962:carton:4207:6617257,6616964:carton:4207:6617678,6631478:carton:4207:6633558,6652939:carton:4207:6659532'}, {'unwanted_material': 'carton', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.005541745404523602, 'nb_photo': 10072, 'list_port_cont': '6600862,6600896,6601025,6601432,6601509,6602274,6602602,6603017,6603405,6605885,6606079,6607679,6608062,6608285,6609455,6610403,6610610,6610646,6611147,6613688,6614029,6614524,6615392,6615433,6615557,6615807,6616523,6617433,6618778,6618919,6620209,6620333,6620915,6621055,6621679,6621959,6621994,6622138,6622194,6622460,6624060,6625559,6626610,6626645,6627278,6628471,6629181,6629246,6634380,6634648,6634846,6635132,6635839,6638277,6638440,6639151,6640998,6642060,6642703,6648971,6650047,6650147,6650271,6650967,6658510,6664044,6666448,6668089,6668177,6669622,6672030,6674022,6674547,6674632,6674763,6675077,6675104,6675135,6675473,6676026,6676537,6676625,6677127,6677221,6677812,6678043,6678220,6678576,6678652,6678765,6678931,6678963,6678994,6679223,6681249,6687137,6691499,6691652,6692287,6693204,6693679,6693897,6694010,6694038,6694216,6694416,6695511,6697512,6698426,6698766,6698791,6699371,6699423,6700624,6700657,6705442,6706626,6706767,6707236,6707810,6709732,6711227,6711402,6711481,6712143,6712484,6712643,6712742,', 'assoc_port': '10844008:10846058,10844009:10845786,10844010:10846035,10844011:10845555,10844012:10845974,10844013:10845417,10844014:10845188,10844015:10845076,10844016:10845159,10844017:10845134,10844018:10844944,10844019:10845036,10844020:10844680,10844021:10844446,12107423:12108311,12107427:12108078,12107429:12107832,6600536:6601432,6600541:6601509,6600544:6600862,6600546:6601025,6600551:6600896,6601138:6602602,6601649:6602274,6602728:6603017,6602733:6603405,6604399:6608062,6605499:6610646,6605503:6605885,6605688:6606079,6606683:6607679,6607835:6610403,6607839:6608285,6609199:6609455,6610498:6610610,6610992:6611147,6613334:6613688,6613876:6614029,6614199:6614524,6614967:6615392,6614969:6615433,6615340:6615557,6615362:6615807,6616173:6616523,6616982:6617433,6618307:6618778,6618311:6618919,6620038:6620209,6620040:6620333,6620422:6621679,6620444:6621055,6620447:6620915,6621638:6624060,6621643:6622460,6621647:6622194,6621651:6622138,6621654:6621959,6621659:6621994,6625236:6625559,6626268:6626610,6626274:6626645,6627098:662727', 'assoc_mat': '10844008:carton:4211:10846058,10844009:carton:4211:10845786,10844010:carton:4211:10846035,10844011:carton:4211:10845555,10844012:carton:4211:10845974,10844013:carton:4211:10845417,10844014:carton:4211:10845188,10844015:carton:4211:10845076,10844016:carton:4211:10845159,10844017:carton:4211:10845134,10844018:carton:4211:10844944,10844019:carton:4211:10845036,10844020:carton:4211:10844680,10844021:carton:4211:10844446,12107423:carton:4211:12108311,12107427:carton:4211:12108078,12107429:carton:4211:12107832,6600536:carton:4211:6601432,6600541:carton:4211:6601509,6600544:carton:4211:6600862,6600546:carton:4211:6601025,6600551:carton:4211:6600896,6601138:carton:4211:6602602,6601649:carton:4211:6602274,6602728:carton:4211:6603017,6602733:carton:4211:6603405,6604399:carton:4211:6608062,6605499:carton:4211:6610646,6605503:carton:4211:6605885,6605688:carton:4211:6606079,6606683:carton:4211:6607679,6607835:carton:4211:6610403,6607839:carton:4211:6608285,6609199:carton:4211:6609455,6610498:carton:4211:6610610,6610992:ca'}, {'unwanted_material': 'carton', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.021265796174868765, 'nb_photo': 11055, 'list_port_cont': '6601215,6601254,6601311,6601365,6601560,6601690,6601782,6601848,6602311,6603606,6603844,6603989,6604932,6606278,6606384,6607129,6607398,6607888,6608321,6608587,6608682,6608971,6609916,6610055,6610284,6610912,6611077,6613728,6614151,6615888,6616062,6616202,6616976,6617459,6617851,6618180,6618944,6620516,6620989,6621115,6621137,6621173,6621387,6621436,6621535,6622109,6622160,6622315,6622373,6622427,6622513,6622655,6622753,6626373,6626971,6627002,6627199,6627839,6628013,6628560,6628619,6629361,6629384,6635813,6635874,6635895,6635941,6635971,6636033,6636118,6636148,6636296,6636333,6636418,6636592,6636681,6636726,6636802,6637043,6637688,6637712,6637762,6637860,6637949,6637973,6638091,6638425,6638595,6638701,6638837,6638945,6638981,6639192,6639297,6639349,6639651,6639755,6639845,6639906,6639972,6640117,6640719,6640754,6640781,6640958,6641217,6641305,6641322,6641489,6641514,6641657,6641813,6641946,6642029,6642085,6642288,6643238,6643376,6643471,6643599,6643635,6643658,6643688,6643713,6643897,6644067,6644460,6644654,', 'assoc_port': '6600535:6601254,6600537:6601690,6600543:6601215,6600545:6601365,6600547:6601560,6600550:6601782,6600553:6601311,6601140:6602311,6601199:6601848,6602727:6603844,6602729:6603989,6602732:6603606,6604400:6604932,6604702:6606384,6605500:6606278,6605502:6607129,6606682:6608321,6606685:6607888,6606687:6607398,6607836:6608587,6607838:6608682,6608144:6608971,6609197:6610055,6609198:6609916,6609963:6610284,6610497:6611077,6610499:6610912,6612953:6613728,6613333:6614151,6614966:6616202,6614968:6616062,6615360:6615888,6616171:6616976,6616960:6618180,6616966:6617459,6616967:6617851,6618310:6618944,6620036:6620516,6620039:6621387,6620042:6620989,6620404:6621137,6620441:6621436,6620445:6621173,6620446:6621535,6620449:6621115,6621639:6622655,6621642:6622753,6621645:6622513,6621650:6622373,6621652:6622427,6621655:6622160,6621656:6622109,6621661:6622315,6625901:6626373,6626270:6626971,6626272:6627002,6626275:6627199,6627097:6627839,6627099:6628013,6627946:6628560,6627948:6628619,6628988:6629361,6628991:6629384,6630847:6635941,', 'assoc_mat': '6600535:carton:3327:6601254,6600537:carton:3327:6601690,6600543:carton:3327:6601215,6600545:carton:3327:6601365,6600547:carton:3327:6601560,6600550:carton:3327:6601782,6600553:carton:3327:6601311,6601140:carton:3327:6602311,6601199:carton:3327:6601848,6602727:carton:3327:6603844,6602729:carton:3327:6603989,6602732:carton:3327:6603606,6604400:carton:3327:6604932,6604702:carton:3327:6606384,6605500:carton:3327:6606278,6605502:carton:3327:6607129,6606682:carton:3327:6608321,6606685:carton:3327:6607888,6606687:carton:3327:6607398,6607836:carton:3327:6608587,6607838:carton:3327:6608682,6608144:carton:3327:6608971,6609197:carton:3327:6610055,6609198:carton:3327:6609916,6609963:carton:3327:6610284,6610497:carton:3327:6611077,6610499:carton:3327:6610912,6612953:carton:3327:6613728,6613333:carton:3327:6614151,6614966:carton:3327:6616202,6614968:carton:3327:6616062,6615360:carton:3327:6615888,6616171:carton:3327:6616976,6616960:carton:3327:6618180,6616966:carton:3327:6617459,6616967:carton:3327:6617851,6618310:carton:3'}, {'unwanted_material': 'carton', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.0030077102589759186, 'nb_photo': 188, 'list_port_cont': '6600928,6602053,6613067,6615617,6620856,6626789,6634257,6637078,6637444,6638372,6639234,6641575,6646017,6647334,6649471,6649914,6653437,6654689,6655701,6658478,6662382,6670056,6672616,6674513,6683667,6684475,6687238,6687457,6696453,6696810,6698452,6711567,6712284,6712773,6714498,6717605,6719822,6720209,6720249,6720369,6721251,6722524,6740808,6745523,6745785,6750251,6754193,6754563,6765279,6766693,6780395,6780519,6784030,6786852,6788641,6791341,6793689,6795536,6817651,6821378,6824090,6835185,6836549,6840202,6849237,6852379,6854531,6859034,6862722,6865777,6866323,6867980,6869264,6874412,6875751,6875979,6882733,6886815,6888107,6889496,6902501,6904840,6914439,6920752,6921621,6924262', 'assoc_port': '6600538:6600928,6601136:6602053,6612632:6613067,6615365:6615617,6620439:6620856,6626271:6626789,6630848:6646017,6631186:6634257,6631194:6649914,6631961:6655701,6632583:6662382,6632966:6670056,6635673:6637444,6635681:6637078,6637843:6638372,6638774:6639234,6641286:6641575,6646447:6647334,6648940:6649471,6650951:6653437,6654041:6654689,6657889:6658478,6671809:6672616,6673895:6674513,6681680:6683667,6683618:6684475,6685440:6720369,6685511:6720209,6686027:6719822,6686295:6687457,6686312:6687238,6688871:6714498,6690894:6698452,6693641:6696453,6696476:6696810,6710860:6711567,6711680:6712284,6712324:6712773,6716688:6717605,6718843:6720249,6720803:6721251,6722354:6722524,6740018:6740808,6740709:6745523,6744268:6745785,6748062:6750251,6753406:6754563,6753419:6754193,6764848:6765279,6765563:6766693,6779977:6780519,6780017:6780395,6782249:6784030,6786463:6786852,6787690:6788641,6790918:6791341,6793238:6793689,6795258:6795536,6817300:6817651,6820776:6821378,6823753:6824090,6833510:6835185,6835942:6836549,6839793:6840202,', 'assoc_mat': '6600538:carton:4200:6600928,6601136:carton:4200:6602053,6612632:carton:4200:6613067,6615365:carton:4200:6615617,6620439:carton:4200:6620856,6626271:carton:4200:6626789,6630848:carton:4200:6646017,6631186:carton:4200:6634257,6631194:carton:4200:6649914,6631961:carton:4200:6655701,6632583:carton:4200:6662382,6632966:carton:4200:6670056,6635673:carton:4200:6637444,6635681:carton:4200:6637078,6637843:carton:4200:6638372,6638774:carton:4200:6639234,6641286:carton:4200:6641575,6646447:carton:4200:6647334,6648940:carton:4200:6649471,6650951:carton:4200:6653437,6654041:carton:4200:6654689,6657889:carton:4200:6658478,6671809:carton:4200:6672616,6673895:carton:4200:6674513,6681680:carton:4200:6683667,6683618:carton:4200:6684475,6685440:carton:4200:6720369,6685511:carton:4200:6720209,6686027:carton:4200:6719822,6686295:carton:4200:6687457,6686312:carton:4200:6687238,6688871:carton:4200:6714498,6690894:carton:4200:6698452,6693641:carton:4200:6696453,6696476:carton:4200:6696810,6710860:carton:4200:6711567,6711680:carton:4'}, {'unwanted_material': 'ela', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.9213990406858268, 'nb_photo': 851, 'list_port_cont': '6603436,6608816,6618318,6624164,6627339,6634755,6634797,6660094,6660517,6698372,6704166,6712720,6720539,6723233,6723408,6735149,6735451,6741141,6754061,6761154,6768736,6775025,6780642,6799277,6800318,6801314,6806353,6808104,6808746,6810130,6812814,6822548,6826377,6834092,6838771,6846432,6852158,6863258,6865673,6869916,6876021,6930479,6940847,10844172', 'assoc_port': '10844025:10844172,6602731:6603436,6608146:6608816,6617603:6618318,6620405:6624164,6626721:6627339,6630937:6634755,6631483:6634797,6631962:6660517,6632879:6660094,6638160:6704166,6653521:6723233,6664117:6735451,6671808:6834092,6682068:6768736,6685476:6799277,6685991:6800318,6686304:6801314,6690887:6735149,6696473:6698372,6712332:6712720,6718634:6720539,6722813:6723408,6740022:6741141,6745199:6810130,6753416:6754061,6760133:6761154,6765566:6808746,6774410:6775025,6780026:6780642,6786466:6808104,6793236:6806353,6811950:6812814,6820599:6822548,6825858:6826377,6837012:6838771,6846007:6846432,6851650:6852158,6863104:6863258,6865559:6865673,6869544:6869916,6875674:6876021,6894306:6930479,6900378:6940847', 'assoc_mat': '10844025:ela:4203:10844172,6602731:ela:4203:6603436,6608146:ela:4203:6608816,6617603:ela:4203:6618318,6620405:ela:4203:6624164,6626721:ela:4203:6627339,6630937:ela:4203:6634755,6631483:ela:4203:6634797,6631962:ela:4203:6660517,6632879:ela:4203:6660094,6638160:ela:4203:6704166,6653521:ela:4203:6723233,6664117:ela:4203:6735451,6671808:ela:4203:6834092,6682068:ela:4203:6768736,6685476:ela:4203:6799277,6685991:ela:4203:6800318,6686304:ela:4203:6801314,6690887:ela:4203:6735149,6696473:ela:4203:6698372,6712332:ela:4203:6712720,6718634:ela:4203:6720539,6722813:ela:4203:6723408,6740022:ela:4203:6741141,6745199:ela:4203:6810130,6753416:ela:4203:6754061,6760133:ela:4203:6761154,6765566:ela:4203:6808746,6774410:ela:4203:6775025,6780026:ela:4203:6780642,6786466:ela:4203:6808104,6793236:ela:4203:6806353,6811950:ela:4203:6812814,6820599:ela:4203:6822548,6825858:ela:4203:6826377,6837012:ela:4203:6838771,6846007:ela:4203:6846432,6851650:ela:4203:6852158,6863104:ela:4203:6863258,6865559:ela:4203:6865673,6869544:ela:4203:68699'}, {'unwanted_material': 'ela', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0007116836898320703, 'nb_photo': 13, 'list_port_cont': '6622066,6638510,6668140,6674367,6682359,6692900,6699027,6751454,6812448', 'assoc_port': '6621640:6622066,6631190:6668140,6638162:6638510,6673893:6674367,6681682:6682359,6688873:6699027,6691778:6692900,6750987:6751454,6811912:6812448', 'assoc_mat': '6621640:ela:4209:6622066,6631190:ela:4209:6668140,6638162:ela:4209:6638510,6673893:ela:4209:6674367,6681682:ela:4209:6682359,6688873:ela:4209:6699027,6691778:ela:4209:6692900,6750987:ela:4209:6751454,6811912:ela:4209:6812448'}, {'unwanted_material': 'ela', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.00032398461757896294, 'nb_photo': 4, 'list_port_cont': '6846232,6863050,6869299,6922346', 'assoc_port': '6845636:6846232,6862271:6863050,6868823:6869299,6920918:6922346', 'assoc_mat': '6845636:ela:4209:6846232,6862271:ela:4209:6863050,6868823:ela:4209:6869299,6920918:ela:4209:6922346'}, {'unwanted_material': 'ela', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.00015698953627416628, 'nb_photo': 147, 'list_port_cont': '6606078,6610614,6610650,6611153,6618777,6621995,6624051,6626615,6629238,6634390,6638443,6650283,6668181,6674761,6676619,6687148,6691488,6692283,6693901,6697509,6698420,6712139,6712495,6712638,6713763,6714406,6719444,6721923,6723047,6725696,6725822,6730328,6732327,6732432,6735373,6735646,6743245,6748843,6752335,6753734,6754131,6754280,6755113,6756969,6757867,6757971,6759593,6762164,6768398,6778929,6780252,6780740,6780892,6784776,6785335,6790082,6794260,6795576,6797595,6799113,6799411,6799545,6799693,6800458,6800536,6800674,6800830,6808649,6808671,6808798,6809166,6810011,6810258,6812213,6835747,6840031,6842616,6854223,6855219,6855451,6869220,6889386,6889562,6917712,6920224,6922983,6961422,10844945,10845415,10846062,12107826', 'assoc_port': '10844008:10846062,10844013:10845415,10844018:10844945,12107429:12107826,6605499:6610650,6605688:6606078,6610498:6610614,6610992:6611153,6618307:6618777,6621638:6624051,6621659:6621995,6626268:6626615,6628989:6629238,6630935:6674761,6631484:6676619,6631967:6634390,6635671:6693901,6638161:6638443,6649833:6650283,6653520:6723047,6653523:6725696,6654837:6725822,6663625:6735646,6666361:6668181,6671801:6755113,6683616:6790082,6683620:6691488,6685431:6799113,6685484:6854223,6685489:6799411,6685506:6799545,6685519:6799693,6685533:6697509,6686006:6855219,6686021:6855451,6686031:6800458,6686094:6800536,6686289:6800674,6686298:6800830,6686321:6687148,6688870:6732432,6688875:6732327,6690891:6735373,6691780:6730328,6691869:6692283,6696477:6698420,6710862:6713763,6711681:6712139,6711684:6714406,6712328:6712638,6712336:6712495,6718636:6719444,6721635:6721923,6738742:6810258,6740001:6752335,6740031:6743245,6743340:6757867,6748058:6810011,6748061:6748843,6748064:6756969,6753412:6754131,6753415:6754280,6753418:6753734,6757493:', 'assoc_mat': '10844008:ela:4211:10846062,10844013:ela:4211:10845415,10844018:ela:4211:10844945,12107429:ela:4211:12107826,6605499:ela:4211:6610650,6605688:ela:4211:6606078,6610498:ela:4211:6610614,6610992:ela:4211:6611153,6618307:ela:4211:6618777,6621638:ela:4211:6624051,6621659:ela:4211:6621995,6626268:ela:4211:6626615,6628989:ela:4211:6629238,6630935:ela:4211:6674761,6631484:ela:4211:6676619,6631967:ela:4211:6634390,6635671:ela:4211:6693901,6638161:ela:4211:6638443,6649833:ela:4211:6650283,6653520:ela:4211:6723047,6653523:ela:4211:6725696,6654837:ela:4211:6725822,6663625:ela:4211:6735646,6666361:ela:4211:6668181,6671801:ela:4211:6755113,6683616:ela:4211:6790082,6683620:ela:4211:6691488,6685431:ela:4211:6799113,6685484:ela:4211:6854223,6685489:ela:4211:6799411,6685506:ela:4211:6799545,6685519:ela:4211:6799693,6685533:ela:4211:6697509,6686006:ela:4211:6855219,6686021:ela:4211:6855451,6686031:ela:4211:6800458,6686094:ela:4211:6800536,6686289:ela:4211:6800674,6686298:ela:4211:6800830,6686321:ela:4211:6687148,6688870:ela:4211'}, {'unwanted_material': 'ela', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.0010348878985054983, 'nb_photo': 35, 'list_port_cont': '6604882,6609168,6620173,6628355,6637450,6641567,6654693,6665190,6684476,6719824,6754201,6754550,6780397,6797660,6813919,6835187,6842436,6852373,6859023,6862721,6866325,6867974,6875989,6888099,6895293,6920760', 'assoc_port': '6604397:6604882,6608145:6609168,6620043:6620173,6627944:6628355,6635673:6637450,6641286:6641567,6654041:6654693,6664429:6665190,6683618:6684476,6686027:6719824,6753406:6754550,6753419:6754201,6780017:6780397,6797266:6797660,6813389:6813919,6833510:6835187,6842061:6842436,6852123:6852373,6858797:6859023,6862270:6862721,6865552:6866325,6867749:6867974,6875675:6875989,6887861:6888099,6894305:6895293,6920100:6920760', 'assoc_mat': '6604397:ela:4200:6604882,6608145:ela:4200:6609168,6620043:ela:4200:6620173,6627944:ela:4200:6628355,6635673:ela:4200:6637450,6641286:ela:4200:6641567,6654041:ela:4200:6654693,6664429:ela:4200:6665190,6683618:ela:4200:6684476,6686027:ela:4200:6719824,6753406:ela:4200:6754550,6753419:ela:4200:6754201,6780017:ela:4200:6780397,6797266:ela:4200:6797660,6813389:ela:4200:6813919,6833510:ela:4200:6835187,6842061:ela:4200:6842436,6852123:ela:4200:6852373,6858797:ela:4200:6859023,6862270:ela:4200:6862721,6865552:ela:4200:6866325,6867749:ela:4200:6867974,6875675:ela:4200:6875989,6887861:ela:4200:6888099,6894305:ela:4200:6895293,6920100:ela:4200:6920760'}, {'unwanted_material': 'etiquette', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.00021900090159665767, 'nb_photo': 78, 'list_port_cont': '6603427,6608805,6624167,6634748,6634790,6660096,6698374,6704172,6712723,6720540,6723232,6723415,6735146,6741134,6754075,6761156,6775024,6780647,6799267,6800315,6801318,6806348,6808094,6808744,6810125,6822562,6826365,6834101,6838767,6852166,6863265,6865664,6869922,6876013,6930484,10844168', 'assoc_port': '10844025:10844168,6602731:6603427,6608146:6608805,6620405:6624167,6630937:6634748,6631483:6634790,6632879:6660096,6638160:6704172,6653521:6723232,6671808:6834101,6685476:6799267,6685991:6800315,6686304:6801318,6690887:6735146,6696473:6698374,6712332:6712723,6718634:6720540,6722813:6723415,6740022:6741134,6745199:6810125,6753416:6754075,6760133:6761156,6765566:6808744,6774410:6775024,6780026:6780647,6786466:6808094,6793236:6806348,6820599:6822562,6825858:6826365,6837012:6838767,6851650:6852166,6863104:6863265,6865559:6865664,6869544:6869922,6875674:6876013,6894306:6930484', 'assoc_mat': '10844025:etiquette:4203:10844168,6602731:etiquette:4203:6603427,6608146:etiquette:4203:6608805,6620405:etiquette:4203:6624167,6630937:etiquette:4203:6634748,6631483:etiquette:4203:6634790,6632879:etiquette:4203:6660096,6638160:etiquette:4203:6704172,6653521:etiquette:4203:6723232,6671808:etiquette:4203:6834101,6685476:etiquette:4203:6799267,6685991:etiquette:4203:6800315,6686304:etiquette:4203:6801318,6690887:etiquette:4203:6735146,6696473:etiquette:4203:6698374,6712332:etiquette:4203:6712723,6718634:etiquette:4203:6720540,6722813:etiquette:4203:6723415,6740022:etiquette:4203:6741134,6745199:etiquette:4203:6810125,6753416:etiquette:4203:6754075,6760133:etiquette:4203:6761156,6765566:etiquette:4203:6808744,6774410:etiquette:4203:6775024,6780026:etiquette:4203:6780647,6786466:etiquette:4203:6808094,6793236:etiquette:4203:6806348,6820599:etiquette:4203:6822562,6825858:etiquette:4203:6826365,6837012:etiquette:4203:6838767,6851650:etiquette:4203:6852166,6863104:etiquette:4203:6863265,6865559:etiquette:4203:6865664'}, {'unwanted_material': 'etiquette', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.00030922441233135133, 'nb_photo': 45, 'list_port_cont': '6600778,6617522,6618983,6620411,6621477,6621941,6625401,6641076,6647266,6648129,6651936,6667511,6669411,6670890,6672171,6673397,6674364,6678090,6682348,6690825,6695447,6695912,6697467,6699022,6711044,6712233,6723686,6740487,6742318,6751446,6754766', 'assoc_port': '6600552:6600778,6616963:6617522,6618302:6618983,6620037:6620411,6620443:6621477,6621646:6621941,6625237:6625401,6631491:6669411,6632883:6670890,6640593:6641076,6646444:6647266,6647214:6648129,6650509:6651936,6664426:6667511,6671806:6672171,6672700:6673397,6673893:6674364,6677866:6678090,6681682:6682348,6682898:6690825,6685513:6697467,6688873:6699022,6694731:6695912,6694740:6695447,6709375:6711044,6710867:6712233,6723504:6723686,6739465:6742318,6740030:6740487,6741911:6754766,6750987:6751446', 'assoc_mat': '6600552:etiquette:4209:6600778,6616963:etiquette:4209:6617522,6618302:etiquette:4209:6618983,6620037:etiquette:4209:6620411,6620443:etiquette:4209:6621477,6621646:etiquette:4209:6621941,6625237:etiquette:4209:6625401,6631491:etiquette:4209:6669411,6632883:etiquette:4209:6670890,6640593:etiquette:4209:6641076,6646444:etiquette:4209:6647266,6647214:etiquette:4209:6648129,6650509:etiquette:4209:6651936,6664426:etiquette:4209:6667511,6671806:etiquette:4209:6672171,6672700:etiquette:4209:6673397,6673893:etiquette:4209:6674364,6677866:etiquette:4209:6678090,6681682:etiquette:4209:6682348,6682898:etiquette:4209:6690825,6685513:etiquette:4209:6697467,6688873:etiquette:4209:6699022,6694731:etiquette:4209:6695912,6694740:etiquette:4209:6695447,6709375:etiquette:4209:6711044,6710867:etiquette:4209:6712233,6723504:etiquette:4209:6723686,6739465:etiquette:4209:6742318,6740030:etiquette:4209:6740487,6741911:etiquette:4209:6754766,6750987:etiquette:4209:6751446'}, {'unwanted_material': 'etiquette', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.00022776236187163268, 'nb_photo': 13, 'list_port_cont': '6869297,6890964,6908904,6960438,12108034,12108669', 'assoc_port': '12107420:12108669,12107428:12108034,6868823:6869297,6890813:6890964,6902262:6908904,6919131:6960438', 'assoc_mat': '12107420:etiquette:4209:12108669,12107428:etiquette:4209:12108034,6868823:etiquette:4209:6869297,6890813:etiquette:4209:6890964,6902262:etiquette:4209:6908904,6919131:etiquette:4209:6960438'}, {'unwanted_material': 'etiquette', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 9.761859113588492e-05, 'nb_photo': 731, 'list_port_cont': '6600902,6602270,6608065,6608287,6609459,6610411,6610617,6611154,6614525,6615396,6615423,6615803,6617425,6620339,6620925,6621056,6621665,6621973,6621996,6622127,6622201,6622459,6624053,6625563,6628477,6634393,6634662,6634854,6635143,6635844,6639158,6640993,6650044,6650156,6650281,6658498,6664049,6666450,6669614,6672026,6674393,6674548,6674635,6674760,6675108,6675142,6675485,6676526,6676614,6677128,6677225,6677804,6678044,6678223,6678571,6678768,6678969,6679218,6681248,6687147,6691498,6691665,6692288,6694047,6694207,6694414,6695513,6697508,6698432,6698802,6699435,6700656,6705444,6706636,6706768,6707239,6707826,6709730,6711224,6711404,6711483,6712141,6712483,6712632,6712743,6713443,6713604,6713764,6714402,6716469,6716553,6717496,6717550,6718035,6718534,6719031,6719449,6719733,6720659,6721132,6721831,6722611,6722685,6723046,6723113,6723251,6723786,6724921,6724969,6725105,6725687,6729864,6730275,6730327,6730721,6731860,6732320,6732438,6735374,6735654,6738119,6740200,6740600,6741082,6741932,6743253,6743387,6743591,', 'assoc_port': '10844009:10845795,10844011:10845547,10844012:10845968,10844014:10845181,10844016:10845171,10844017:10845140,10844018:10844947,10844019:10845034,10844020:10844676,10844021:10844452,12107427:12108051,12107429:12107825,6600551:6600902,6601649:6602270,6604399:6608065,6607835:6610411,6607839:6608287,6609199:6609459,6610498:6610617,6610992:6611154,6614199:6614525,6614967:6615396,6614969:6615423,6615362:6615803,6616982:6617425,6620040:6620339,6620422:6621665,6620444:6621056,6620447:6620925,6621638:6624053,6621643:6622459,6621647:6622201,6621651:6622127,6621654:6621973,6621659:6621996,6625236:6625563,6627945:6628477,6630850:6634854,6630856:6634662,6630932:6674635,6630935:6674760,6630939:6675108,6631069:6675485,6631193:6635143,6631477:6676526,6631484:6676614,6631489:6677128,6631492:6677225,6631960:6677804,6631967:6634393,6632585:6678223,6632660:6678571,6632714:6678768,6632877:6678969,6632963:6679218,6633862:6635844,6635676:6694047,6635682:6694207,6637840:6700656,6638773:6705444,6638776:6639158,6640587:6706636,6640590:', 'assoc_mat': '10844009:etiquette:4211:10845795,10844011:etiquette:4211:10845547,10844012:etiquette:4211:10845968,10844014:etiquette:4211:10845181,10844016:etiquette:4211:10845171,10844017:etiquette:4211:10845140,10844018:etiquette:4211:10844947,10844019:etiquette:4211:10845034,10844020:etiquette:4211:10844676,10844021:etiquette:4211:10844452,12107427:etiquette:4211:12108051,12107429:etiquette:4211:12107825,6600551:etiquette:4211:6600902,6601649:etiquette:4211:6602270,6604399:etiquette:4211:6608065,6607835:etiquette:4211:6610411,6607839:etiquette:4211:6608287,6609199:etiquette:4211:6609459,6610498:etiquette:4211:6610617,6610992:etiquette:4211:6611154,6614199:etiquette:4211:6614525,6614967:etiquette:4211:6615396,6614969:etiquette:4211:6615423,6615362:etiquette:4211:6615803,6616982:etiquette:4211:6617425,6620040:etiquette:4211:6620339,6620422:etiquette:4211:6621665,6620444:etiquette:4211:6621056,6620447:etiquette:4211:6620925,6621638:etiquette:4211:6624053,6621643:etiquette:4211:6622459,6621647:etiquette:4211:6622201,6621651:'}, {'unwanted_material': 'etiquette', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.004348080716206635, 'nb_photo': 2177, 'list_port_cont': '6601212,6601260,6601313,6601368,6601566,6601697,6601794,6601839,6602310,6603617,6603847,6603990,6604929,6606281,6606358,6607128,6607393,6607886,6608319,6608595,6609912,6610053,6610275,6610911,6611081,6613732,6614155,6615891,6616068,6616203,6616981,6617464,6617847,6618173,6618943,6620503,6620995,6621113,6621139,6621170,6621386,6621441,6621536,6622158,6622312,6622433,6622517,6622652,6622759,6626361,6626978,6626999,6627195,6627832,6628014,6628554,6628614,6629363,6629389,6635808,6635870,6635898,6635951,6635977,6636117,6636143,6636289,6636331,6636428,6636605,6636678,6636737,6636797,6637045,6637679,6637710,6637767,6637948,6637983,6638086,6638422,6638600,6638706,6638840,6638940,6638970,6639190,6639289,6639354,6639650,6639754,6639847,6639897,6639975,6640120,6640716,6640750,6640777,6640954,6641212,6641304,6641334,6641488,6641517,6641651,6641811,6641945,6642027,6642087,6642291,6643242,6643378,6643477,6643637,6643657,6643690,6643720,6643900,6644076,6644462,6644664,6645167,6645306,6645763,6648004,6648352,6648373,6649645,', 'assoc_port': '6600535:6601260,6600537:6601697,6600543:6601212,6600545:6601368,6600547:6601566,6600550:6601794,6600553:6601313,6601140:6602310,6601199:6601839,6602727:6603847,6602729:6603990,6602732:6603617,6604400:6604929,6604702:6606358,6605500:6606281,6605502:6607128,6606682:6608319,6606685:6607886,6606687:6607393,6607836:6608595,6609197:6610053,6609198:6609912,6609963:6610275,6610497:6611081,6610499:6610911,6612953:6613732,6613333:6614155,6614966:6616203,6614968:6616068,6615360:6615891,6616171:6616981,6616960:6618173,6616966:6617464,6616967:6617847,6618310:6618943,6620036:6620503,6620039:6621386,6620042:6620995,6620404:6621139,6620441:6621441,6620445:6621170,6620446:6621536,6620449:6621113,6621639:6622652,6621642:6622759,6621645:6622517,6621652:6622433,6621655:6622158,6621661:6622312,6625901:6626361,6626270:6626978,6626272:6626999,6626275:6627195,6627097:6627832,6627099:6628014,6627946:6628554,6627948:6628614,6628988:6629363,6628991:6629389,6630847:6635951,6630851:6635977,6630853:6636678,6630854:6637679,6630934:6637767,', 'assoc_mat': '6600535:etiquette:3327:6601260,6600537:etiquette:3327:6601697,6600543:etiquette:3327:6601212,6600545:etiquette:3327:6601368,6600547:etiquette:3327:6601566,6600550:etiquette:3327:6601794,6600553:etiquette:3327:6601313,6601140:etiquette:3327:6602310,6601199:etiquette:3327:6601839,6602727:etiquette:3327:6603847,6602729:etiquette:3327:6603990,6602732:etiquette:3327:6603617,6604400:etiquette:3327:6604929,6604702:etiquette:3327:6606358,6605500:etiquette:3327:6606281,6605502:etiquette:3327:6607128,6606682:etiquette:3327:6608319,6606685:etiquette:3327:6607886,6606687:etiquette:3327:6607393,6607836:etiquette:3327:6608595,6609197:etiquette:3327:6610053,6609198:etiquette:3327:6609912,6609963:etiquette:3327:6610275,6610497:etiquette:3327:6611081,6610499:etiquette:3327:6610911,6612953:etiquette:3327:6613732,6613333:etiquette:3327:6614155,6614966:etiquette:3327:6616203,6614968:etiquette:3327:6616068,6615360:etiquette:3327:6615891,6616171:etiquette:3327:6616981,6616960:etiquette:3327:6618173,6616966:etiquette:3327:6617464,6'}, {'unwanted_material': 'etiquette', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.00042222109924499836, 'nb_photo': 28, 'list_port_cont': '6613076,6621900,6635395,6637437,6638359,6641572,6654692,6665194,6669586,6670051,6686808,6687472,6714497,6717610,6719817,6745800,6765266,6766701,6780408,6780724,6797654,6836540,6840198,6865778,6874405,6886806,6902498,6904836', 'assoc_port': '6612632:6613076,6621653:6621900,6632786:6635395,6632966:6670051,6635673:6637437,6637843:6638359,6641286:6641572,6654041:6654692,6664429:6665194,6668950:6669586,6686027:6719817,6686295:6687472,6686325:6686808,6688871:6714497,6716688:6717610,6744268:6745800,6764848:6765266,6765563:6766701,6779960:6780724,6780017:6780408,6797266:6797654,6835942:6836540,6839793:6840198,6865562:6865778,6873774:6874405,6886550:6886806,6899435:6902498,6904395:6904836', 'assoc_mat': '6612632:etiquette:4200:6613076,6621653:etiquette:4200:6621900,6632786:etiquette:4200:6635395,6632966:etiquette:4200:6670051,6635673:etiquette:4200:6637437,6637843:etiquette:4200:6638359,6641286:etiquette:4200:6641572,6654041:etiquette:4200:6654692,6664429:etiquette:4200:6665194,6668950:etiquette:4200:6669586,6686027:etiquette:4200:6719817,6686295:etiquette:4200:6687472,6686325:etiquette:4200:6686808,6688871:etiquette:4200:6714497,6716688:etiquette:4200:6717610,6744268:etiquette:4200:6745800,6764848:etiquette:4200:6765266,6765563:etiquette:4200:6766701,6779960:etiquette:4200:6780724,6780017:etiquette:4200:6780408,6797266:etiquette:4200:6797654,6835942:etiquette:4200:6836540,6839793:etiquette:4200:6840198,6865562:etiquette:4200:6865778,6873774:etiquette:4200:6874405,6886550:etiquette:4200:6886806,6899435:etiquette:4200:6902498,6904395:etiquette:4200:6904836'}, {'unwanted_material': 'film_plastique', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0010375570916267033, 'nb_photo': 51, 'list_port_cont': '6603435,6618321,6624160,6627326,6698377,6704173,6723230,6735453,6741145,6754070,6761155,6768723,6775026,6780641,6799270,6800313,6801310,6808096,6810127,6822556,6826374,6834088,6838769,6846429,6852169,6869926,6876022,6930487,6940833', 'assoc_port': '6602731:6603435,6617603:6618321,6620405:6624160,6626721:6627326,6638160:6704173,6653521:6723230,6664117:6735453,6671808:6834088,6682068:6768723,6685476:6799270,6685991:6800313,6686304:6801310,6696473:6698377,6740022:6741145,6745199:6810127,6753416:6754070,6760133:6761155,6774410:6775026,6780026:6780641,6786466:6808096,6820599:6822556,6825858:6826374,6837012:6838769,6846007:6846429,6851650:6852169,6869544:6869926,6875674:6876022,6894306:6930487,6900378:6940833', 'assoc_mat': '6602731:film_plastique:4203:6603435,6617603:film_plastique:4203:6618321,6620405:film_plastique:4203:6624160,6626721:film_plastique:4203:6627326,6638160:film_plastique:4203:6704173,6653521:film_plastique:4203:6723230,6664117:film_plastique:4203:6735453,6671808:film_plastique:4203:6834088,6682068:film_plastique:4203:6768723,6685476:film_plastique:4203:6799270,6685991:film_plastique:4203:6800313,6686304:film_plastique:4203:6801310,6696473:film_plastique:4203:6698377,6740022:film_plastique:4203:6741145,6745199:film_plastique:4203:6810127,6753416:film_plastique:4203:6754070,6760133:film_plastique:4203:6761155,6774410:film_plastique:4203:6775026,6780026:film_plastique:4203:6780641,6786466:film_plastique:4203:6808096,6820599:film_plastique:4203:6822556,6825858:film_plastique:4203:6826374,6837012:film_plastique:4203:6838769,6846007:film_plastique:4203:6846429,6851650:film_plastique:4203:6852169,6869544:film_plastique:4203:6869926,6875674:film_plastique:4203:6876022,6894306:film_plastique:4203:6930487,6900378:film_pla'}, {'unwanted_material': 'film_plastique', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0024536770792147593, 'nb_photo': 69, 'list_port_cont': '6600779,6602159,6617705,6618987,6620421,6620776,6621929,6622078,6627041,6638509,6641074,6648120,6655495,6664009,6667518,6668136,6668210,6670551,6670887,6672176,6673393,6674366,6678097,6690827,6692895,6694182,6695921,6697464,6699019,6712090,6712235,6716808,6721888,6723150,6723675,6724773,6740489,6742332,6751452,6824345', 'assoc_port': '6600540:6602159,6600552:6600779,6616961:6617705,6618302:6618987,6620037:6620421,6620448:6620776,6621640:6622078,6621646:6621929,6626722:6627041,6631190:6668136,6631196:6668210,6632661:6670551,6632883:6670887,6638162:6638509,6640593:6641074,6647214:6648120,6654838:6655495,6663620:6664009,6664426:6667518,6671806:6672176,6672700:6673393,6673893:6674366,6677866:6678097,6682898:6690827,6685513:6697464,6688873:6699019,6691778:6692895,6692502:6694182,6694731:6695921,6710867:6712235,6711682:6712090,6716079:6716808,6721779:6721888,6722817:6723150,6723504:6723675,6724593:6724773,6739465:6742332,6740030:6740489,6750987:6751452,6823971:6824345', 'assoc_mat': '6600540:film_plastique:4209:6602159,6600552:film_plastique:4209:6600779,6616961:film_plastique:4209:6617705,6618302:film_plastique:4209:6618987,6620037:film_plastique:4209:6620421,6620448:film_plastique:4209:6620776,6621640:film_plastique:4209:6622078,6621646:film_plastique:4209:6621929,6626722:film_plastique:4209:6627041,6631190:film_plastique:4209:6668136,6631196:film_plastique:4209:6668210,6632661:film_plastique:4209:6670551,6632883:film_plastique:4209:6670887,6638162:film_plastique:4209:6638509,6640593:film_plastique:4209:6641074,6647214:film_plastique:4209:6648120,6654838:film_plastique:4209:6655495,6663620:film_plastique:4209:6664009,6664426:film_plastique:4209:6667518,6671806:film_plastique:4209:6672176,6672700:film_plastique:4209:6673393,6673893:film_plastique:4209:6674366,6677866:film_plastique:4209:6678097,6682898:film_plastique:4209:6690827,6685513:film_plastique:4209:6697464,6688873:film_plastique:4209:6699019,6691778:film_plastique:4209:6692895,6692502:film_plastique:4209:6694182,6694731:film_pla'}, {'unwanted_material': 'film_plastique', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.004405144558639858, 'nb_photo': 50, 'list_port_cont': '6846222,6863051,6869293,6869651,6886840,6890965,6908908,6914944,6917082,6920507,6922345,6960431,12107792,12108029,12108283,12108676', 'assoc_port': '12107420:12108676,12107424:12108283,12107428:12108029,12107430:12107792,6845636:6846222,6862271:6863051,6868823:6869293,6869543:6869651,6886556:6886840,6890813:6890965,6902262:6908908,6914405:6914944,6916368:6917082,6919130:6920507,6919131:6960431,6920918:6922345', 'assoc_mat': '12107420:film_plastique:4209:12108676,12107424:film_plastique:4209:12108283,12107428:film_plastique:4209:12108029,12107430:film_plastique:4209:12107792,6845636:film_plastique:4209:6846222,6862271:film_plastique:4209:6863051,6868823:film_plastique:4209:6869293,6869543:film_plastique:4209:6869651,6886556:film_plastique:4209:6886840,6890813:film_plastique:4209:6890965,6902262:film_plastique:4209:6908908,6914405:film_plastique:4209:6914944,6916368:film_plastique:4209:6917082,6919130:film_plastique:4209:6920507,6919131:film_plastique:4209:6960431,6920918:film_plastique:4209:6922345'}, {'unwanted_material': 'film_plastique', 'main_material': 'pcnc', 'pht_type': 4207, 'ratio': 0.006753454742247015, 'nb_photo': 34, 'list_port_cont': '6617254,6617671,6659522', 'assoc_port': '6616962:6617254,6616964:6617671,6652939:6659522', 'assoc_mat': '6616962:film_plastique:4207:6617254,6616964:film_plastique:4207:6617671,6652939:film_plastique:4207:6659522'}, {'unwanted_material': 'film_plastique', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.002259746608148063, 'nb_photo': 5499, 'list_port_cont': '6600849,6600890,6601017,6601442,6602275,6602592,6603008,6603406,6606076,6607678,6608058,6608295,6609456,6610404,6610609,6610652,6611148,6614536,6615399,6615435,6615793,6616511,6617424,6618766,6618925,6620326,6620921,6621065,6621674,6621974,6622008,6622128,6622208,6622454,6624061,6625573,6626621,6626647,6627282,6628485,6629175,6629247,6634383,6634658,6634850,6635144,6635829,6638283,6638444,6639153,6641001,6642062,6642716,6650045,6650153,6650270,6650961,6658508,6664038,6666449,6668094,6668173,6669613,6672032,6674008,6674546,6674641,6674770,6675073,6675109,6675137,6675474,6676525,6676617,6677121,6677223,6677802,6678045,6678212,6678572,6678654,6678774,6678959,6678990,6679232,6681260,6687145,6691518,6691651,6692290,6693202,6693680,6693906,6694032,6694214,6694412,6695522,6697517,6698427,6698760,6698790,6699374,6699429,6700667,6705446,6706630,6706777,6707225,6707821,6709736,6711239,6711396,6711489,6712153,6712493,6712628,6712756,6713442,6713595,6713761,6714400,6716476,6716567,6717500,6717544,6718036,6718525,6719027,', 'assoc_port': '10844008:10846072,10844009:10845794,10844010:10846040,10844011:10845549,10844012:10845979,10844013:10845414,10844014:10845182,10844015:10845079,10844016:10845170,10844017:10845132,10844018:10844951,10844019:10845054,10844020:10844687,10844021:10844442,12107423:12108313,12107427:12108076,12107429:12107822,6600536:6601442,6600544:6600849,6600546:6601017,6600551:6600890,6601138:6602592,6601649:6602275,6602728:6603008,6602733:6603406,6604399:6608058,6605499:6610652,6605688:6606076,6606683:6607678,6607835:6610404,6607839:6608295,6609199:6609456,6610498:6610609,6610992:6611148,6614199:6614536,6614967:6615399,6614969:6615435,6615362:6615793,6616173:6616511,6616982:6617424,6618307:6618766,6618311:6618925,6620040:6620326,6620422:6621674,6620444:6621065,6620447:6620921,6621638:6624061,6621643:6622454,6621647:6622208,6621651:6622128,6621654:6621974,6621659:6622008,6625236:6625573,6626268:6626621,6626274:6626647,6627098:6627282,6627945:6628485,6628987:6629175,6628989:6629247,6630850:6634850,6630852:6674008,6630856:663465', 'assoc_mat': '10844008:film_plastique:4211:10846072,10844009:film_plastique:4211:10845794,10844010:film_plastique:4211:10846040,10844011:film_plastique:4211:10845549,10844012:film_plastique:4211:10845979,10844013:film_plastique:4211:10845414,10844014:film_plastique:4211:10845182,10844015:film_plastique:4211:10845079,10844016:film_plastique:4211:10845170,10844017:film_plastique:4211:10845132,10844018:film_plastique:4211:10844951,10844019:film_plastique:4211:10845054,10844020:film_plastique:4211:10844687,10844021:film_plastique:4211:10844442,12107423:film_plastique:4211:12108313,12107427:film_plastique:4211:12108076,12107429:film_plastique:4211:12107822,6600536:film_plastique:4211:6601442,6600544:film_plastique:4211:6600849,6600546:film_plastique:4211:6601017,6600551:film_plastique:4211:6600890,6601138:film_plastique:4211:6602592,6601649:film_plastique:4211:6602275,6602728:film_plastique:4211:6603008,6602733:film_plastique:4211:6603406,6604399:film_plastique:4211:6608058,6605499:film_plastique:4211:6610652,6605688:film_plast'}, {'unwanted_material': 'film_plastique', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.009562760165995879, 'nb_photo': 651, 'list_port_cont': '6600935,6602065,6604873,6606113,6609166,6613072,6615619,6618496,6620183,6620863,6621894,6626781,6628361,6634253,6635399,6637071,6637439,6638362,6639237,6641573,6646019,6647326,6649276,6649481,6649910,6653428,6654681,6655699,6658476,6662389,6665188,6669588,6670057,6672611,6674515,6683658,6684468,6686795,6687232,6687468,6696454,6696813,6698466,6711565,6712273,6712779,6714502,6717597,6719828,6720160,6720216,6720243,6720362,6721257,6722534,6724152,6739287,6740805,6745514,6745792,6750253,6754188,6754560,6759424,6765277,6766706,6767355,6780405,6780527,6780713,6784028,6786844,6788649,6791328,6793700,6795545,6797658,6813918,6817663,6821381,6824093,6835171,6836542,6840210,6842440,6849225,6851785,6852368,6854521,6859028,6862726,6865775,6866336,6867967,6869257,6874408,6875757,6882725,6886810,6888104,6889508,6895295,6900331,6902499,6904843,6914441,6920747,6921624,6924250', 'assoc_port': '6600538:6600935,6601136:6602065,6604397:6604873,6605689:6606113,6608145:6609166,6612632:6613072,6615365:6615619,6618309:6618496,6620043:6620183,6620439:6620863,6621653:6621894,6626271:6626781,6627944:6628361,6630848:6646019,6631070:6649276,6631186:6634253,6631194:6649910,6631961:6655699,6632583:6662389,6632786:6635399,6632966:6670057,6635673:6637439,6635681:6637071,6637843:6638362,6638774:6639237,6641286:6641573,6646447:6647326,6648940:6649481,6650951:6653428,6654041:6654681,6657889:6658476,6664429:6665188,6668950:6669588,6671809:6672611,6673895:6674515,6681680:6683658,6683618:6684468,6685440:6720362,6685511:6720216,6686027:6719828,6686041:6720160,6686295:6687468,6686312:6687232,6686325:6686795,6688871:6714502,6690894:6698466,6693641:6696454,6696476:6696813,6710860:6711565,6711680:6712273,6712324:6712779,6716688:6717597,6718843:6720243,6720803:6721257,6722354:6722534,6723892:6724152,6738748:6739287,6740018:6740805,6740709:6745514,6744268:6745792,6748062:6750253,6753406:6754560,6753419:6754188,6759057:6759424,', 'assoc_mat': '6600538:film_plastique:4200:6600935,6601136:film_plastique:4200:6602065,6604397:film_plastique:4200:6604873,6605689:film_plastique:4200:6606113,6608145:film_plastique:4200:6609166,6612632:film_plastique:4200:6613072,6615365:film_plastique:4200:6615619,6618309:film_plastique:4200:6618496,6620043:film_plastique:4200:6620183,6620439:film_plastique:4200:6620863,6621653:film_plastique:4200:6621894,6626271:film_plastique:4200:6626781,6627944:film_plastique:4200:6628361,6630848:film_plastique:4200:6646019,6631070:film_plastique:4200:6649276,6631186:film_plastique:4200:6634253,6631194:film_plastique:4200:6649910,6631961:film_plastique:4200:6655699,6632583:film_plastique:4200:6662389,6632786:film_plastique:4200:6635399,6632966:film_plastique:4200:6670057,6635673:film_plastique:4200:6637439,6635681:film_plastique:4200:6637071,6637843:film_plastique:4200:6638362,6638774:film_plastique:4200:6639237,6641286:film_plastique:4200:6641573,6646447:film_plastique:4200:6647326,6648940:film_plastique:4200:6649481,6650951:film_pla'}, {'unwanted_material': 'kraft', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0013476688860348137, 'nb_photo': 25, 'list_port_cont': '6603437,6608810,6624174,6698367,6704169,6720534,6723222,6723404,6735448,6754066,6768737,6775016,6799274,6806349,6808099,6808748,6822563,6826367,6930490', 'assoc_port': '6602731:6603437,6608146:6608810,6620405:6624174,6638160:6704169,6653521:6723222,6664117:6735448,6682068:6768737,6685476:6799274,6696473:6698367,6718634:6720534,6722813:6723404,6753416:6754066,6765566:6808748,6774410:6775016,6786466:6808099,6793236:6806349,6820599:6822563,6825858:6826367,6894306:6930490', 'assoc_mat': '6602731:kraft:4203:6603437,6608146:kraft:4203:6608810,6620405:kraft:4203:6624174,6638160:kraft:4203:6704169,6653521:kraft:4203:6723222,6664117:kraft:4203:6735448,6682068:kraft:4203:6768737,6685476:kraft:4203:6799274,6696473:kraft:4203:6698367,6718634:kraft:4203:6720534,6722813:kraft:4203:6723404,6753416:kraft:4203:6754066,6765566:kraft:4203:6808748,6774410:kraft:4203:6775016,6786466:kraft:4203:6808099,6793236:kraft:4203:6806349,6820599:kraft:4203:6822563,6825858:kraft:4203:6826367,6894306:kraft:4203:6930490'}, {'unwanted_material': 'kraft', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.0002605400427680809, 'nb_photo': 31, 'list_port_cont': '6602597,6603402,6608291,6621971,6629173,6635141,6674012,6675078,6718038,6721839,6731861,6756973,6763094,6767267,6782609,6783130,6786055,6787764,6799513,6800227,6800537,6806177,6808705,6863473,6869206,6917724', 'assoc_port': '6601138:6602597,6602733:6603402,6607839:6608291,6621654:6621971,6628987:6629173,6630852:6674012,6631065:6675078,6631193:6635141,6640595:6806177,6651292:6721839,6657891:6731861,6685502:6799513,6685989:6800227,6686094:6800537,6717679:6718038,6748064:6756973,6762546:6763094,6765565:6808705,6766922:6767267,6779975:6786055,6782244:6783130,6782282:6782609,6787218:6787764,6862775:6863473,6868825:6869206,6916370:6917724', 'assoc_mat': '6601138:kraft:4211:6602597,6602733:kraft:4211:6603402,6607839:kraft:4211:6608291,6621654:kraft:4211:6621971,6628987:kraft:4211:6629173,6630852:kraft:4211:6674012,6631065:kraft:4211:6675078,6631193:kraft:4211:6635141,6640595:kraft:4211:6806177,6651292:kraft:4211:6721839,6657891:kraft:4211:6731861,6685502:kraft:4211:6799513,6685989:kraft:4211:6800227,6686094:kraft:4211:6800537,6717679:kraft:4211:6718038,6748064:kraft:4211:6756973,6762546:kraft:4211:6763094,6765565:kraft:4211:6808705,6766922:kraft:4211:6767267,6779975:kraft:4211:6786055,6782244:kraft:4211:6783130,6782282:kraft:4211:6782609,6787218:kraft:4211:6787764,6862775:kraft:4211:6863473,6868825:kraft:4211:6869206,6916370:kraft:4211:6917724'}, {'unwanted_material': 'kraft', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.002080903131306298, 'nb_photo': 8, 'list_port_cont': '6654687,6665187,6711559,6739285,6786851,6875988,6895294', 'assoc_port': '6654041:6654687,6664429:6665187,6710860:6711559,6738748:6739285,6786463:6786851,6875675:6875988,6894305:6895294', 'assoc_mat': '6654041:kraft:4200:6654687,6664429:kraft:4200:6665187,6710860:kraft:4200:6711559,6738748:kraft:4200:6739285,6786463:kraft:4200:6786851,6875675:kraft:4200:6875988,6894305:kraft:4200:6895294'}, {'unwanted_material': 'metal', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0025689075464105384, 'nb_photo': 116, 'list_port_cont': '6603439,6608802,6618323,6624168,6627336,6634744,6634804,6698366,6704170,6712714,6720531,6723221,6723414,6735153,6741143,6754064,6768738,6775029,6780637,6799279,6800304,6801309,6806355,6808095,6808740,6810126,6812819,6822550,6826362,6834102,6838768,6846434,6852156,6863255,6865671,6869930,6876016,6930480,6940846,10844182', 'assoc_port': '10844025:10844182,6602731:6603439,6608146:6608802,6617603:6618323,6620405:6624168,6626721:6627336,6630937:6634744,6631483:6634804,6638160:6704170,6653521:6723221,6671808:6834102,6682068:6768738,6685476:6799279,6685991:6800304,6686304:6801309,6690887:6735153,6696473:6698366,6712332:6712714,6718634:6720531,6722813:6723414,6740022:6741143,6745199:6810126,6753416:6754064,6765566:6808740,6774410:6775029,6780026:6780637,6786466:6808095,6793236:6806355,6811950:6812819,6820599:6822550,6825858:6826362,6837012:6838768,6846007:6846434,6851650:6852156,6863104:6863255,6865559:6865671,6869544:6869930,6875674:6876016,6894306:6930480,6900378:6940846', 'assoc_mat': '10844025:metal:4203:10844182,6602731:metal:4203:6603439,6608146:metal:4203:6608802,6617603:metal:4203:6618323,6620405:metal:4203:6624168,6626721:metal:4203:6627336,6630937:metal:4203:6634744,6631483:metal:4203:6634804,6638160:metal:4203:6704170,6653521:metal:4203:6723221,6671808:metal:4203:6834102,6682068:metal:4203:6768738,6685476:metal:4203:6799279,6685991:metal:4203:6800304,6686304:metal:4203:6801309,6690887:metal:4203:6735153,6696473:metal:4203:6698366,6712332:metal:4203:6712714,6718634:metal:4203:6720531,6722813:metal:4203:6723414,6740022:metal:4203:6741143,6745199:metal:4203:6810126,6753416:metal:4203:6754064,6765566:metal:4203:6808740,6774410:metal:4203:6775029,6780026:metal:4203:6780637,6786466:metal:4203:6808095,6793236:metal:4203:6806355,6811950:metal:4203:6812819,6820599:metal:4203:6822550,6825858:metal:4203:6826362,6837012:metal:4203:6838768,6846007:metal:4203:6846434,6851650:metal:4203:6852156,6863104:metal:4203:6863255,6865559:metal:4203:6865671,6869544:metal:4203:6869930,6875674:metal:4203:6876'}, {'unwanted_material': 'metal', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.01620320652826406, 'nb_photo': 95, 'list_port_cont': '6600765,6601464,6602163,6616735,6618978,6620417,6620780,6621471,6622065,6625394,6627054,6638506,6641080,6644333,6647279,6648126,6651921,6655492,6664016,6666561,6668141,6668996,6669285,6669399,6670444,6670900,6672174,6673388,6678096,6690826,6692907,6695450,6695916,6697457,6711036,6712095,6712243,6716818,6721890,6723141,6723677,6740494,6751439,6754759,6791206,6812443', 'assoc_port': '6600533:6601464,6600540:6602163,6600552:6600765,6616168:6616735,6618302:6618978,6620037:6620417,6620443:6621471,6620448:6620780,6621640:6622065,6625237:6625394,6626722:6627054,6631064:6666561,6631190:6668141,6631480:6668996,6631482:6669285,6631491:6669399,6632580:6670444,6632883:6670900,6638162:6638506,6638777:6644333,6640593:6641080,6646444:6647279,6647214:6648126,6650509:6651921,6654838:6655492,6663620:6664016,6671806:6672174,6672700:6673388,6677866:6678096,6682898:6690826,6685513:6697457,6691778:6692907,6694731:6695916,6694740:6695450,6709375:6711036,6710867:6712243,6711682:6712095,6716079:6716818,6721779:6721890,6722817:6723141,6723504:6723677,6740030:6740494,6741911:6754759,6750987:6751439,6790915:6791206,6811912:6812443', 'assoc_mat': '6600533:metal:4209:6601464,6600540:metal:4209:6602163,6600552:metal:4209:6600765,6616168:metal:4209:6616735,6618302:metal:4209:6618978,6620037:metal:4209:6620417,6620443:metal:4209:6621471,6620448:metal:4209:6620780,6621640:metal:4209:6622065,6625237:metal:4209:6625394,6626722:metal:4209:6627054,6631064:metal:4209:6666561,6631190:metal:4209:6668141,6631480:metal:4209:6668996,6631482:metal:4209:6669285,6631491:metal:4209:6669399,6632580:metal:4209:6670444,6632883:metal:4209:6670900,6638162:metal:4209:6638506,6638777:metal:4209:6644333,6640593:metal:4209:6641080,6646444:metal:4209:6647279,6647214:metal:4209:6648126,6650509:metal:4209:6651921,6654838:metal:4209:6655492,6663620:metal:4209:6664016,6671806:metal:4209:6672174,6672700:metal:4209:6673388,6677866:metal:4209:6678096,6682898:metal:4209:6690826,6685513:metal:4209:6697457,6691778:metal:4209:6692907,6694731:metal:4209:6695916,6694740:metal:4209:6695450,6709375:metal:4209:6711036,6710867:metal:4209:6712243,6711682:metal:4209:6712095,6716079:metal:4209:671681'}, {'unwanted_material': 'metal', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.03813640273004401, 'nb_photo': 50, 'list_port_cont': '6846234,6869292,6869658,6886838,6890968,6894664,6908915,6914949,6920506,6922343,6960437,12108025,12108375,12108683', 'assoc_port': '12107420:12108683,12107422:12108375,12107428:12108025,6845636:6846234,6868823:6869292,6869543:6869658,6886556:6886838,6890813:6890968,6894304:6894664,6902262:6908915,6914405:6914949,6919130:6920506,6919131:6960437,6920918:6922343', 'assoc_mat': '12107420:metal:4209:12108683,12107422:metal:4209:12108375,12107428:metal:4209:12108025,6845636:metal:4209:6846234,6868823:metal:4209:6869292,6869543:metal:4209:6869658,6886556:metal:4209:6886838,6890813:metal:4209:6890968,6894304:metal:4209:6894664,6902262:metal:4209:6908915,6914405:metal:4209:6914949,6919130:metal:4209:6920506,6919131:metal:4209:6960437,6920918:metal:4209:6922343'}, {'unwanted_material': 'metal', 'main_material': 'pcnc', 'pht_type': 4207, 'ratio': 0.28424781729752074, 'nb_photo': 15, 'list_port_cont': '6617261,6617679,6659518', 'assoc_port': '6616962:6617261,6616964:6617679,6652939:6659518', 'assoc_mat': '6616962:metal:4207:6617261,6616964:metal:4207:6617679,6652939:metal:4207:6659518'}, {'unwanted_material': 'metal', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.014105013759782188, 'nb_photo': 4099, 'list_port_cont': '6600856,6600899,6601011,6601438,6601603,6602271,6602596,6603016,6603404,6606080,6607692,6608057,6608280,6609447,6610406,6610603,6610660,6611150,6613941,6614530,6615402,6615431,6615798,6616522,6617438,6618772,6618911,6620212,6620337,6620916,6621058,6621671,6621967,6622006,6622131,6622198,6622469,6624050,6625565,6626622,6626649,6627272,6628473,6629174,6629239,6634384,6634649,6634849,6635130,6635842,6638288,6638454,6639155,6641000,6642050,6642701,6649970,6650051,6650148,6650282,6650968,6653714,6658501,6664036,6666452,6668086,6668172,6669618,6672033,6674018,6674395,6674559,6674640,6674768,6675067,6675113,6675145,6675475,6676538,6676615,6677120,6677227,6677799,6678035,6678225,6678304,6678567,6678655,6678773,6678968,6679000,6679222,6681257,6687134,6691497,6691663,6692286,6693199,6693683,6693905,6694046,6694218,6694411,6695512,6697510,6698424,6698770,6698800,6699365,6699425,6700612,6700659,6705447,6706623,6706766,6707227,6707825,6709724,6711225,6711407,6711492,6712148,6712489,6712634,6712755,6713434,6713596,6713751,', 'assoc_port': '10844008:10846070,10844009:10845782,10844010:10846037,10844011:10845544,10844012:10845982,10844013:10845413,10844014:10845190,10844015:10845088,10844016:10845174,10844017:10845137,10844018:10844954,10844019:10845038,10844020:10844683,10844021:10844455,12107423:12108305,12107427:12108080,12107429:12107834,6600536:6601438,6600544:6600856,6600546:6601011,6600551:6600899,6601138:6602596,6601142:6601603,6601649:6602271,6602728:6603016,6602733:6603404,6604399:6608057,6605499:6610660,6605688:6606080,6606683:6607692,6607835:6610406,6607839:6608280,6609199:6609447,6610498:6610603,6610992:6611150,6613877:6613941,6614199:6614530,6614967:6615402,6614969:6615431,6615362:6615798,6616173:6616522,6616982:6617438,6618307:6618772,6618311:6618911,6620038:6620212,6620040:6620337,6620422:6621671,6620444:6621058,6620447:6620916,6621638:6624050,6621643:6622469,6621647:6622198,6621651:6622131,6621654:6621967,6621659:6622006,6625236:6625565,6626268:6626622,6626274:6626649,6627098:6627272,6627945:6628473,6628987:6629174,6628989:662923', 'assoc_mat': '10844008:metal:4211:10846070,10844009:metal:4211:10845782,10844010:metal:4211:10846037,10844011:metal:4211:10845544,10844012:metal:4211:10845982,10844013:metal:4211:10845413,10844014:metal:4211:10845190,10844015:metal:4211:10845088,10844016:metal:4211:10845174,10844017:metal:4211:10845137,10844018:metal:4211:10844954,10844019:metal:4211:10845038,10844020:metal:4211:10844683,10844021:metal:4211:10844455,12107423:metal:4211:12108305,12107427:metal:4211:12108080,12107429:metal:4211:12107834,6600536:metal:4211:6601438,6600544:metal:4211:6600856,6600546:metal:4211:6601011,6600551:metal:4211:6600899,6601138:metal:4211:6602596,6601142:metal:4211:6601603,6601649:metal:4211:6602271,6602728:metal:4211:6603016,6602733:metal:4211:6603404,6604399:metal:4211:6608057,6605499:metal:4211:6610660,6605688:metal:4211:6606080,6606683:metal:4211:6607692,6607835:metal:4211:6610406,6607839:metal:4211:6608280,6609199:metal:4211:6609447,6610498:metal:4211:6610603,6610992:metal:4211:6611150,6613877:metal:4211:6613941,6614199:metal:4211'}, {'unwanted_material': 'metal', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.0018611932504713049, 'nb_photo': 168, 'list_port_cont': '6602309,6606381,6607132,6607892,6608676,6609913,6610058,6613724,6618178,6621382,6621446,6622317,6622515,6622750,6627836,6629357,6635868,6636122,6636323,6636736,6637761,6638093,6638417,6638705,6638836,6638971,6639356,6639756,6639841,6640772,6640965,6642021,6642089,6643659,6643689,6644080,6648806,6652946,6664581,6668616,6674494,6675599,6676588,6681849,6685021,6686471,6687366,6687492,6688258,6688984,6689512,6689535,6690288,6690633,6692197,6692683,6693648,6696369,6697176,6697873,6698735,6698882,6699062,6699467,6700152,6712810,6712846,6713279,6718380,6719374,6721461,6722064,6723542,6724041,6725336,6740417,6745471,6746032,6750024,6751761,6754361,6754498,6754531,6756337,6760376,6761240,6765850,6767303,6767722,6781605,6782080,6783162,6784119,6788824,6789101,6789299,6790666,6792153,6792401,6797280,6797781,6798013,6817846,6821598,6833582,6834267,6840067,6843272,6851938,6852133,6852491,6854288,6865690,6865866,6869175,6875951,6876047,6876102,6876518,6887363,6887386,6888987,6890298,6891134,6895349,6904238,6904578,6915036', 'assoc_port': '6601140:6602309,6604702:6606381,6605502:6607132,6606685:6607892,6607838:6608676,6609197:6610058,6609198:6609913,6612953:6613724,6616960:6618178,6620039:6621382,6620441:6621446,6621642:6622750,6621645:6622515,6621661:6622317,6627097:6627836,6628988:6629357,6630934:6637761,6630938:6638093,6631068:6638836,6631071:6638971,6631076:6639356,6631490:6640772,6631493:6640965,6631964:6642089,6632713:6648806,6632788:6643659,6632880:6636323,6632882:6644080,6633863:6635868,6635683:6636736,6635684:6636122,6637839:6638705,6637844:6638417,6638772:6639841,6638775:6639756,6640596:6643689,6641258:6642021,6650952:6652946,6663623:6664581,6667559:6668616,6673888:6675599,6673890:6674494,6674743:6676588,6681004:6681849,6683621:6685021,6685438:6699467,6685448:6698882,6685487:6698735,6685490:6699062,6685495:6700152,6685528:6687366,6685534:6686471,6686008:6697873,6686056:6697176,6686063:6689512,6686284:6689535,6686287:6690633,6686297:6688984,6686305:6688258,6686329:6687492,6688867:6690288,6690873:6692197,6691781:6692683,6692503:6693648,', 'assoc_mat': '6601140:metal:3327:6602309,6604702:metal:3327:6606381,6605502:metal:3327:6607132,6606685:metal:3327:6607892,6607838:metal:3327:6608676,6609197:metal:3327:6610058,6609198:metal:3327:6609913,6612953:metal:3327:6613724,6616960:metal:3327:6618178,6620039:metal:3327:6621382,6620441:metal:3327:6621446,6621642:metal:3327:6622750,6621645:metal:3327:6622515,6621661:metal:3327:6622317,6627097:metal:3327:6627836,6628988:metal:3327:6629357,6630934:metal:3327:6637761,6630938:metal:3327:6638093,6631068:metal:3327:6638836,6631071:metal:3327:6638971,6631076:metal:3327:6639356,6631490:metal:3327:6640772,6631493:metal:3327:6640965,6631964:metal:3327:6642089,6632713:metal:3327:6648806,6632788:metal:3327:6643659,6632880:metal:3327:6636323,6632882:metal:3327:6644080,6633863:metal:3327:6635868,6635683:metal:3327:6636736,6635684:metal:3327:6636122,6637839:metal:3327:6638705,6637844:metal:3327:6638417,6638772:metal:3327:6639841,6638775:metal:3327:6639756,6640596:metal:3327:6643689,6641258:metal:3327:6642021,6650952:metal:3327:665294'}, {'unwanted_material': 'metal', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.008187433477581552, 'nb_photo': 161, 'list_port_cont': '6600924,6604874,6606109,6609169,6613066,6615621,6618495,6620180,6620864,6621895,6628356,6635390,6637077,6637440,6638365,6639245,6646018,6649283,6649472,6649917,6653439,6654695,6655708,6662393,6672612,6674525,6683669,6684477,6686796,6687223,6687470,6696448,6696812,6698463,6711552,6712278,6714493,6719818,6720151,6720203,6720239,6720363,6721248,6722529,6724153,6740811,6745799,6750239,6754562,6759423,6765268,6767356,6780402,6780532,6784026,6786842,6788648,6791326,6793698,6797669,6813916,6824104,6835192,6836545,6840197,6842435,6849222,6851783,6852365,6854532,6859036,6862724,6866334,6867975,6869267,6874413,6875761,6875987,6882735,6895285,6900330,6902505,6904844,6914446,6920751,6921633', 'assoc_port': '6600538:6600924,6604397:6604874,6605689:6606109,6608145:6609169,6612632:6613066,6615365:6615621,6618309:6618495,6620043:6620180,6620439:6620864,6621653:6621895,6627944:6628356,6630848:6646018,6631070:6649283,6631194:6649917,6631961:6655708,6632583:6662393,6632786:6635390,6635673:6637440,6635681:6637077,6637843:6638365,6638774:6639245,6648940:6649472,6650951:6653439,6654041:6654695,6671809:6672612,6673895:6674525,6681680:6683669,6683618:6684477,6685440:6720363,6685511:6720203,6686027:6719818,6686041:6720151,6686295:6687470,6686312:6687223,6686325:6686796,6688871:6714493,6690894:6698463,6693641:6696448,6696476:6696812,6710860:6711552,6711680:6712278,6718843:6720239,6720803:6721248,6722354:6722529,6723892:6724153,6740018:6740811,6744268:6745799,6748062:6750239,6753406:6754562,6759057:6759423,6764848:6765268,6766924:6767356,6779977:6780532,6780017:6780402,6782249:6784026,6786463:6786842,6787690:6788648,6790918:6791326,6793238:6793698,6797266:6797669,6813389:6813916,6823753:6824104,6833510:6835192,6835942:6836545,', 'assoc_mat': '6600538:metal:4200:6600924,6604397:metal:4200:6604874,6605689:metal:4200:6606109,6608145:metal:4200:6609169,6612632:metal:4200:6613066,6615365:metal:4200:6615621,6618309:metal:4200:6618495,6620043:metal:4200:6620180,6620439:metal:4200:6620864,6621653:metal:4200:6621895,6627944:metal:4200:6628356,6630848:metal:4200:6646018,6631070:metal:4200:6649283,6631194:metal:4200:6649917,6631961:metal:4200:6655708,6632583:metal:4200:6662393,6632786:metal:4200:6635390,6635673:metal:4200:6637440,6635681:metal:4200:6637077,6637843:metal:4200:6638365,6638774:metal:4200:6639245,6648940:metal:4200:6649472,6650951:metal:4200:6653439,6654041:metal:4200:6654695,6671809:metal:4200:6672612,6673895:metal:4200:6674525,6681680:metal:4200:6683669,6683618:metal:4200:6684477,6685440:metal:4200:6720363,6685511:metal:4200:6720203,6686027:metal:4200:6719818,6686041:metal:4200:6720151,6686295:metal:4200:6687470,6686312:metal:4200:6687223,6686325:metal:4200:6686796,6688871:metal:4200:6714493,6690894:metal:4200:6698463,6693641:metal:4200:669644'}, {'unwanted_material': 'papier', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.061231730810446236, 'nb_photo': 10116, 'list_port_cont': '6603432,6608812,6618330,6624173,6627328,6634747,6634795,6660087,6698364,6704163,6712713,6720535,6723229,6723411,6735156,6735458,6741135,6754073,6761149,6768728,6775022,6780633,6799278,6800307,6801306,6806356,6808105,6808742,6810131,6812817,6822555,6826373,6834089,6838762,6846431,6852157,6863266,6865666,6869918,6876010,6930481,6940835,10844177', 'assoc_port': '10844025:10844177,6602731:6603432,6608146:6608812,6617603:6618330,6620405:6624173,6626721:6627328,6630937:6634747,6631483:6634795,6632879:6660087,6638160:6704163,6653521:6723229,6664117:6735458,6671808:6834089,6682068:6768728,6685476:6799278,6685991:6800307,6686304:6801306,6690887:6735156,6696473:6698364,6712332:6712713,6718634:6720535,6722813:6723411,6740022:6741135,6745199:6810131,6753416:6754073,6760133:6761149,6765566:6808742,6774410:6775022,6780026:6780633,6786466:6808105,6793236:6806356,6811950:6812817,6820599:6822555,6825858:6826373,6837012:6838762,6846007:6846431,6851650:6852157,6863104:6863266,6865559:6865666,6869544:6869918,6875674:6876010,6894306:6930481,6900378:6940835', 'assoc_mat': '10844025:papier:4203:10844177,6602731:papier:4203:6603432,6608146:papier:4203:6608812,6617603:papier:4203:6618330,6620405:papier:4203:6624173,6626721:papier:4203:6627328,6630937:papier:4203:6634747,6631483:papier:4203:6634795,6632879:papier:4203:6660087,6638160:papier:4203:6704163,6653521:papier:4203:6723229,6664117:papier:4203:6735458,6671808:papier:4203:6834089,6682068:papier:4203:6768728,6685476:papier:4203:6799278,6685991:papier:4203:6800307,6686304:papier:4203:6801306,6690887:papier:4203:6735156,6696473:papier:4203:6698364,6712332:papier:4203:6712713,6718634:papier:4203:6720535,6722813:papier:4203:6723411,6740022:papier:4203:6741135,6745199:papier:4203:6810131,6753416:papier:4203:6754073,6760133:papier:4203:6761149,6765566:papier:4203:6808742,6774410:papier:4203:6775022,6780026:papier:4203:6780633,6786466:papier:4203:6808105,6793236:papier:4203:6806356,6811950:papier:4203:6812817,6820599:papier:4203:6822555,6825858:papier:4203:6826373,6837012:papier:4203:6838762,6846007:papier:4203:6846431,6851650:papier'}, {'unwanted_material': 'papier', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.026271390056728197, 'nb_photo': 8, 'list_port_cont': '12107737,12108213', 'assoc_port': '12107419:12108213,12107426:12107737', 'assoc_mat': '12107419:papier:4207:12108213,12107426:papier:4207:12107737'}, {'unwanted_material': 'papier', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.9818228416534489, 'nb_photo': 22895, 'list_port_cont': '6600771,6601474,6602161,6616739,6617534,6617710,6618973,6620407,6620774,6621480,6621932,6622068,6625404,6627042,6637128,6638496,6641078,6644338,6647274,6648128,6651923,6655481,6657574,6664005,6666560,6667526,6668133,6668203,6668997,6669282,6669400,6670442,6670550,6670895,6672175,6673389,6674361,6678087,6682355,6690830,6692892,6694186,6695449,6695911,6697461,6699021,6711049,6712084,6712241,6716813,6721889,6723151,6723680,6724781,6740492,6742323,6751441,6754771,6791212,6812437,6824349', 'assoc_port': '6600533:6601474,6600540:6602161,6600552:6600771,6616168:6616739,6616961:6617710,6616963:6617534,6618302:6618973,6620037:6620407,6620443:6621480,6620448:6620774,6621640:6622068,6621646:6621932,6625237:6625404,6626722:6627042,6631064:6666560,6631190:6668133,6631196:6668203,6631480:6668997,6631482:6669282,6631491:6669400,6632580:6670442,6632661:6670550,6632883:6670895,6633864:6637128,6638162:6638496,6638777:6644338,6640593:6641078,6646444:6647274,6647214:6648128,6650509:6651923,6654838:6655481,6656897:6657574,6663620:6664005,6664426:6667526,6671806:6672175,6672700:6673389,6673893:6674361,6677866:6678087,6681682:6682355,6682898:6690830,6685513:6697461,6688873:6699021,6691778:6692892,6692502:6694186,6694731:6695911,6694740:6695449,6709375:6711049,6710867:6712241,6711682:6712084,6716079:6716813,6721779:6721889,6722817:6723151,6723504:6723680,6724593:6724781,6739465:6742323,6740030:6740492,6741911:6754771,6750987:6751441,6790915:6791212,6811912:6812437,6823971:6824349', 'assoc_mat': '6600533:papier:4209:6601474,6600540:papier:4209:6602161,6600552:papier:4209:6600771,6616168:papier:4209:6616739,6616961:papier:4209:6617710,6616963:papier:4209:6617534,6618302:papier:4209:6618973,6620037:papier:4209:6620407,6620443:papier:4209:6621480,6620448:papier:4209:6620774,6621640:papier:4209:6622068,6621646:papier:4209:6621932,6625237:papier:4209:6625404,6626722:papier:4209:6627042,6631064:papier:4209:6666560,6631190:papier:4209:6668133,6631196:papier:4209:6668203,6631480:papier:4209:6668997,6631482:papier:4209:6669282,6631491:papier:4209:6669400,6632580:papier:4209:6670442,6632661:papier:4209:6670550,6632883:papier:4209:6670895,6633864:papier:4209:6637128,6638162:papier:4209:6638496,6638777:papier:4209:6644338,6640593:papier:4209:6641078,6646444:papier:4209:6647274,6647214:papier:4209:6648128,6650509:papier:4209:6651923,6654838:papier:4209:6655481,6656897:papier:4209:6657574,6663620:papier:4209:6664005,6664426:papier:4209:6667526,6671806:papier:4209:6672175,6672700:papier:4209:6673389,6673893:papier:4'}, {'unwanted_material': 'papier', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.9778679543860948, 'nb_photo': 9654, 'list_port_cont': '6846225,6863052,6869290,6869652,6886839,6890954,6894669,6908902,6914938,6917074,6920517,6922339,6960439,12107799,12108033,12108279,12108335,12108368,12108547,12108681,12151216', 'assoc_port': '12107418:12151216,12107420:12108681,12107421:12108547,12107422:12108368,12107424:12108279,12107425:12108335,12107428:12108033,12107430:12107799,6845636:6846225,6862271:6863052,6868823:6869290,6869543:6869652,6886556:6886839,6890813:6890954,6894304:6894669,6902262:6908902,6914405:6914938,6916368:6917074,6919130:6920517,6919131:6960439,6920918:6922339', 'assoc_mat': '12107418:papier:4209:12151216,12107420:papier:4209:12108681,12107421:papier:4209:12108547,12107422:papier:4209:12108368,12107424:papier:4209:12108279,12107425:papier:4209:12108335,12107428:papier:4209:12108033,12107430:papier:4209:12107799,6845636:papier:4209:6846225,6862271:papier:4209:6863052,6868823:papier:4209:6869290,6869543:papier:4209:6869652,6886556:papier:4209:6886839,6890813:papier:4209:6890954,6894304:papier:4209:6894669,6902262:papier:4209:6908902,6914405:papier:4209:6914938,6916368:papier:4209:6917074,6919130:papier:4209:6920517,6919131:papier:4209:6960439,6920918:papier:4209:6922339'}, {'unwanted_material': 'papier', 'main_material': 'pcnc', 'pht_type': 4207, 'ratio': 0.01913239060931627, 'nb_photo': 173, 'list_port_cont': '6617263,6617672,6633555,6659520', 'assoc_port': '6616962:6617263,6616964:6617672,6631478:6633555,6652939:6659520', 'assoc_mat': '6616962:papier:4207:6617263,6616964:papier:4207:6617672,6631478:papier:4207:6633555,6652939:papier:4207:6659520'}, {'unwanted_material': 'papier', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.02200696110253512, 'nb_photo': 44767, 'list_port_cont': '6600859,6600889,6601024,6601441,6601504,6602279,6602446,6602594,6603004,6603395,6605883,6606067,6607684,6608067,6608284,6609460,6610408,6610604,6610647,6611155,6613695,6614035,6614526,6615395,6615430,6615802,6616513,6617436,6618598,6618775,6618915,6620220,6620329,6620926,6621066,6621670,6621709,6621963,6621993,6622136,6622195,6622456,6624056,6625570,6626617,6626659,6627281,6628480,6629180,6629253,6634378,6634445,6634659,6634723,6634856,6635137,6635833,6638284,6638445,6639159,6640995,6642056,6642709,6648980,6650056,6650154,6650269,6650966,6653712,6658497,6664045,6666459,6668084,6668174,6669620,6672034,6674013,6674557,6674636,6674758,6674902,6675068,6675112,6675138,6675487,6676025,6676535,6676626,6677122,6677215,6677813,6678048,6678214,6678566,6678657,6678767,6678938,6678965,6679003,6679162,6679227,6681256,6687142,6691490,6691666,6692285,6693203,6693684,6693895,6694008,6694043,6694209,6694415,6695514,6697518,6698423,6698761,6698796,6699375,6699424,6700614,6700670,6704525,6705435,6706448,6706631,6706771,6707237,', 'assoc_port': '10844008:10846064,10844009:10845789,10844010:10846038,10844011:10845545,10844012:10845980,10844013:10845418,10844014:10845180,10844015:10845083,10844016:10845173,10844017:10845135,10844018:10844948,10844019:10845046,10844020:10844685,10844021:10844451,12107423:12108307,12107427:12108063,12107429:12107830,6600536:6601441,6600541:6601504,6600544:6600859,6600546:6601024,6600551:6600889,6601134:6602446,6601138:6602594,6601649:6602279,6602728:6603004,6602733:6603395,6604399:6608067,6605499:6610647,6605503:6605883,6605688:6606067,6606683:6607684,6607835:6610408,6607839:6608284,6609199:6609460,6610498:6610604,6610992:6611155,6613334:6613695,6613876:6614035,6614199:6614526,6614967:6615395,6614969:6615430,6615362:6615802,6616173:6616513,6616982:6617436,6618305:6618598,6618307:6618775,6618311:6618915,6620038:6620220,6620040:6620329,6620422:6621670,6620440:6621709,6620444:6621066,6620447:6620926,6621638:6624056,6621643:6622456,6621647:6622195,6621651:6622136,6621654:6621963,6621659:6621993,6625236:6625570,6626268:662661', 'assoc_mat': '10844008:papier:4211:10846064,10844009:papier:4211:10845789,10844010:papier:4211:10846038,10844011:papier:4211:10845545,10844012:papier:4211:10845980,10844013:papier:4211:10845418,10844014:papier:4211:10845180,10844015:papier:4211:10845083,10844016:papier:4211:10845173,10844017:papier:4211:10845135,10844018:papier:4211:10844948,10844019:papier:4211:10845046,10844020:papier:4211:10844685,10844021:papier:4211:10844451,12107423:papier:4211:12108307,12107427:papier:4211:12108063,12107429:papier:4211:12107830,6600536:papier:4211:6601441,6600541:papier:4211:6601504,6600544:papier:4211:6600859,6600546:papier:4211:6601024,6600551:papier:4211:6600889,6601134:papier:4211:6602446,6601138:papier:4211:6602594,6601649:papier:4211:6602279,6602728:papier:4211:6603004,6602733:papier:4211:6603395,6604399:papier:4211:6608067,6605499:papier:4211:6610647,6605503:papier:4211:6605883,6605688:papier:4211:6606067,6606683:papier:4211:6607684,6607835:papier:4211:6610408,6607839:papier:4211:6608284,6609199:papier:4211:6609460,6610498:pa'}, {'unwanted_material': 'papier', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.009355240101605987, 'nb_photo': 6285, 'list_port_cont': '6601213,6601256,6601317,6601373,6601553,6601698,6601785,6601845,6602306,6603618,6603848,6603983,6604941,6606282,6606355,6607130,6607397,6607894,6608325,6608588,6608678,6608962,6609907,6610050,6610277,6610916,6611080,6613729,6614150,6615896,6616066,6616196,6616978,6617467,6617840,6618171,6618951,6620513,6620986,6621120,6621143,6621171,6621389,6621444,6621532,6622103,6622164,6622319,6622375,6622434,6622526,6622660,6622747,6626370,6626972,6627003,6627196,6627833,6628017,6628555,6628622,6629355,6629386,6635801,6635866,6635899,6635944,6635981,6636042,6636111,6636140,6636285,6636315,6636430,6636597,6636684,6636728,6636793,6637051,6637686,6637707,6637760,6637847,6637947,6637985,6638092,6638415,6638592,6638709,6638831,6638953,6638977,6639183,6639295,6639358,6639648,6639762,6639839,6639905,6639970,6640128,6640718,6640743,6640775,6640957,6641216,6641307,6641324,6641487,6641516,6641659,6641814,6641948,6642020,6642091,6642283,6643241,6643369,6643482,6643610,6643638,6643667,6643692,6643717,6643898,6644077,6644463,6644658,', 'assoc_port': '6600535:6601256,6600537:6601698,6600543:6601213,6600545:6601373,6600547:6601553,6600550:6601785,6600553:6601317,6601140:6602306,6601199:6601845,6602727:6603848,6602729:6603983,6602732:6603618,6604400:6604941,6604702:6606355,6605500:6606282,6605502:6607130,6606682:6608325,6606685:6607894,6606687:6607397,6607836:6608588,6607838:6608678,6608144:6608962,6609197:6610050,6609198:6609907,6609963:6610277,6610497:6611080,6610499:6610916,6612953:6613729,6613333:6614150,6614966:6616196,6614968:6616066,6615360:6615896,6616171:6616978,6616960:6618171,6616966:6617467,6616967:6617840,6618310:6618951,6620036:6620513,6620039:6621389,6620042:6620986,6620404:6621143,6620441:6621444,6620445:6621171,6620446:6621532,6620449:6621120,6621639:6622660,6621642:6622747,6621645:6622526,6621650:6622375,6621652:6622434,6621655:6622164,6621656:6622103,6621661:6622319,6625901:6626370,6626270:6626972,6626272:6627003,6626275:6627196,6627097:6627833,6627099:6628017,6627946:6628555,6627948:6628622,6628988:6629355,6628991:6629386,6630847:6635944,', 'assoc_mat': '6600535:papier:3327:6601256,6600537:papier:3327:6601698,6600543:papier:3327:6601213,6600545:papier:3327:6601373,6600547:papier:3327:6601553,6600550:papier:3327:6601785,6600553:papier:3327:6601317,6601140:papier:3327:6602306,6601199:papier:3327:6601845,6602727:papier:3327:6603848,6602729:papier:3327:6603983,6602732:papier:3327:6603618,6604400:papier:3327:6604941,6604702:papier:3327:6606355,6605500:papier:3327:6606282,6605502:papier:3327:6607130,6606682:papier:3327:6608325,6606685:papier:3327:6607894,6606687:papier:3327:6607397,6607836:papier:3327:6608588,6607838:papier:3327:6608678,6608144:papier:3327:6608962,6609197:papier:3327:6610050,6609198:papier:3327:6609907,6609963:papier:3327:6610277,6610497:papier:3327:6611080,6610499:papier:3327:6610916,6612953:papier:3327:6613729,6613333:papier:3327:6614150,6614966:papier:3327:6616196,6614968:papier:3327:6616066,6615360:papier:3327:6615896,6616171:papier:3327:6616978,6616960:papier:3327:6618171,6616966:papier:3327:6617467,6616967:papier:3327:6617840,6618310:papier:3'}, {'unwanted_material': 'papier', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.017125275841086585, 'nb_photo': 2971, 'list_port_cont': '6600926,6602063,6604875,6606104,6609174,6613071,6615626,6618500,6620184,6620854,6621908,6626783,6628360,6634243,6635396,6637073,6637446,6638360,6639247,6641571,6646008,6647325,6649284,6649479,6649902,6653435,6654683,6655706,6658468,6662396,6665198,6669579,6670065,6672619,6674516,6683662,6684471,6686799,6687236,6687458,6696447,6696824,6698461,6711553,6712279,6712781,6714496,6717599,6719829,6720164,6720215,6720241,6720368,6721253,6722530,6724154,6739279,6740796,6745518,6745791,6750249,6754202,6754551,6759426,6765269,6766705,6767366,6780400,6780521,6780722,6784029,6786845,6788639,6791340,6793690,6795542,6797664,6813924,6817658,6821374,6824105,6835188,6836541,6840208,6842437,6849226,6851792,6852378,6854525,6859024,6862734,6865779,6866332,6867973,6869258,6874411,6875759,6875982,6882729,6886818,6888094,6889509,6895296,6902512,6904831,6914440,6920757,6921627,6924261', 'assoc_port': '6600538:6600926,6601136:6602063,6604397:6604875,6605689:6606104,6608145:6609174,6612632:6613071,6615365:6615626,6618309:6618500,6620043:6620184,6620439:6620854,6621653:6621908,6626271:6626783,6627944:6628360,6630848:6646008,6631070:6649284,6631186:6634243,6631194:6649902,6631961:6655706,6632583:6662396,6632786:6635396,6632966:6670065,6635673:6637446,6635681:6637073,6637843:6638360,6638774:6639247,6641286:6641571,6646447:6647325,6648940:6649479,6650951:6653435,6654041:6654683,6657889:6658468,6664429:6665198,6668950:6669579,6671809:6672619,6673895:6674516,6681680:6683662,6683618:6684471,6685440:6720368,6685511:6720215,6686027:6719829,6686041:6720164,6686295:6687458,6686312:6687236,6686325:6686799,6688871:6714496,6690894:6698461,6693641:6696447,6696476:6696824,6710860:6711553,6711680:6712279,6712324:6712781,6716688:6717599,6718843:6720241,6720803:6721253,6722354:6722530,6723892:6724154,6738748:6739279,6740018:6740796,6740709:6745518,6744268:6745791,6748062:6750249,6753406:6754551,6753419:6754202,6759057:6759426,', 'assoc_mat': '6600538:papier:4200:6600926,6601136:papier:4200:6602063,6604397:papier:4200:6604875,6605689:papier:4200:6606104,6608145:papier:4200:6609174,6612632:papier:4200:6613071,6615365:papier:4200:6615626,6618309:papier:4200:6618500,6620043:papier:4200:6620184,6620439:papier:4200:6620854,6621653:papier:4200:6621908,6626271:papier:4200:6626783,6627944:papier:4200:6628360,6630848:papier:4200:6646008,6631070:papier:4200:6649284,6631186:papier:4200:6634243,6631194:papier:4200:6649902,6631961:papier:4200:6655706,6632583:papier:4200:6662396,6632786:papier:4200:6635396,6632966:papier:4200:6670065,6635673:papier:4200:6637446,6635681:papier:4200:6637073,6637843:papier:4200:6638360,6638774:papier:4200:6639247,6641286:papier:4200:6641571,6646447:papier:4200:6647325,6648940:papier:4200:6649479,6650951:papier:4200:6653435,6654041:papier:4200:6654683,6657889:papier:4200:6658468,6664429:papier:4200:6665198,6668950:papier:4200:6669579,6671809:papier:4200:6672619,6673895:papier:4200:6674516,6681680:papier:4200:6683662,6683618:papier:4'}, {'unwanted_material': 'pehd', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0013604158722732354, 'nb_photo': 67, 'list_port_cont': '6603441,6608813,6618332,6624169,6627324,6634752,6634796,6698371,6704167,6712710,6720530,6723225,6723402,6735152,6735445,6754074,6761147,6775031,6799271,6801313,6806359,6808097,6808736,6826372,6834093,6838765,6852167,6865667,6876014,6930485,6940839,10844181', 'assoc_port': '10844025:10844181,6602731:6603441,6608146:6608813,6617603:6618332,6620405:6624169,6626721:6627324,6630937:6634752,6631483:6634796,6638160:6704167,6653521:6723225,6664117:6735445,6671808:6834093,6685476:6799271,6686304:6801313,6690887:6735152,6696473:6698371,6712332:6712710,6718634:6720530,6722813:6723402,6753416:6754074,6760133:6761147,6765566:6808736,6774410:6775031,6786466:6808097,6793236:6806359,6825858:6826372,6837012:6838765,6851650:6852167,6865559:6865667,6875674:6876014,6894306:6930485,6900378:6940839', 'assoc_mat': '10844025:pehd:4203:10844181,6602731:pehd:4203:6603441,6608146:pehd:4203:6608813,6617603:pehd:4203:6618332,6620405:pehd:4203:6624169,6626721:pehd:4203:6627324,6630937:pehd:4203:6634752,6631483:pehd:4203:6634796,6638160:pehd:4203:6704167,6653521:pehd:4203:6723225,6664117:pehd:4203:6735445,6671808:pehd:4203:6834093,6685476:pehd:4203:6799271,6686304:pehd:4203:6801313,6690887:pehd:4203:6735152,6696473:pehd:4203:6698371,6712332:pehd:4203:6712710,6718634:pehd:4203:6720530,6722813:pehd:4203:6723402,6753416:pehd:4203:6754074,6760133:pehd:4203:6761147,6765566:pehd:4203:6808736,6774410:pehd:4203:6775031,6786466:pehd:4203:6808097,6793236:pehd:4203:6806359,6825858:pehd:4203:6826372,6837012:pehd:4203:6838765,6851650:pehd:4203:6852167,6865559:pehd:4203:6865667,6875674:pehd:4203:6876014,6894306:pehd:4203:6930485,6900378:pehd:4203:6940839'}, {'unwanted_material': 'pehd', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0004041124511179276, 'nb_photo': 6, 'list_port_cont': '6618980,6620778,6655493,6669280,6711051,6751445', 'assoc_port': '6618302:6618980,6620448:6620778,6631482:6669280,6654838:6655493,6709375:6711051,6750987:6751445', 'assoc_mat': '6618302:pehd:4209:6618980,6620448:pehd:4209:6620778,6631482:pehd:4209:6669280,6654838:pehd:4209:6655493,6709375:pehd:4209:6711051,6750987:pehd:4209:6751445'}, {'unwanted_material': 'pehd', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.0005712244972110796, 'nb_photo': 6, 'list_port_cont': '6869288,6869648,6922350,12108027,12108673', 'assoc_port': '12107420:12108673,12107428:12108027,6868823:6869288,6869543:6869648,6920918:6922350', 'assoc_mat': '12107420:pehd:4209:12108673,12107428:pehd:4209:12108027,6868823:pehd:4209:6869288,6869543:pehd:4209:6869648,6920918:pehd:4209:6922350'}, {'unwanted_material': 'pehd', 'main_material': 'pcnc', 'pht_type': 4207, 'ratio': 0.07610095728061055, 'nb_photo': 1, 'list_port_cont': '6659525', 'assoc_port': '6652939:6659525', 'assoc_mat': '6652939:pehd:4207:6659525'}, {'unwanted_material': 'pehd', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.9648891206027819, 'nb_photo': 8269, 'list_port_cont': '6600597,6600702,6600852,6600891,6601022,6601052,6601428,6601500,6601604,6602281,6602444,6602601,6603006,6603409,6605890,6606075,6607690,6608072,6608288,6609448,6610185,6610407,6610605,6610648,6611149,6613701,6613906,6613939,6614041,6614531,6615390,6615428,6615558,6615799,6616519,6617435,6618606,6618771,6618913,6620221,6620341,6620928,6621057,6621666,6621699,6621790,6621962,6622001,6622037,6622126,6622206,6622461,6624054,6625571,6626612,6626650,6627283,6628486,6629182,6629248,6634392,6634439,6634652,6634721,6634852,6635131,6635834,6638285,6638453,6639157,6640992,6642047,6642713,6648978,6649961,6650055,6650160,6650275,6650956,6653710,6658507,6664051,6666446,6668093,6668166,6669619,6672027,6674019,6674406,6674558,6674637,6674764,6674895,6675069,6675098,6675131,6675486,6676018,6676531,6676620,6677123,6677226,6677805,6678033,6678221,6678316,6678581,6678659,6678689,6678763,6678928,6678955,6678997,6679159,6679231,6681253,6687144,6691496,6691661,6692281,6693207,6693677,6693893,6694000,6694040,6694208,6694413,6695519,', 'assoc_port': '10844008:10846067,10844009:10845792,10844010:10846032,10844011:10845548,10844012:10845975,10844013:10845405,10844014:10845186,10844015:10845082,10844016:10845169,10844017:10845131,10844018:10844955,10844019:10845040,10844020:10844684,10844021:10844450,12107423:12108306,12107427:12108068,12107429:12107833,6600532:6601052,6600536:6601428,6600541:6601500,6600544:6600852,6600546:6601022,6600548:6600702,6600551:6600891,6600554:6600597,6601134:6602444,6601138:6602601,6601142:6601604,6601649:6602281,6602728:6603006,6602733:6603409,6604399:6608072,6605499:6610648,6605503:6605890,6605688:6606075,6606683:6607690,6607835:6610407,6607837:6610185,6607839:6608288,6609199:6609448,6610498:6610605,6610992:6611149,6613334:6613701,6613876:6614041,6613877:6613939,6613878:6613906,6614199:6614531,6614967:6615390,6614969:6615428,6615340:6615558,6615362:6615799,6616173:6616519,6616982:6617435,6618305:6618606,6618307:6618771,6618311:6618913,6620038:6620221,6620040:6620341,6620422:6621666,6620440:6621699,6620444:6621057,6620447:662092', 'assoc_mat': '10844008:pehd:4211:10846067,10844009:pehd:4211:10845792,10844010:pehd:4211:10846032,10844011:pehd:4211:10845548,10844012:pehd:4211:10845975,10844013:pehd:4211:10845405,10844014:pehd:4211:10845186,10844015:pehd:4211:10845082,10844016:pehd:4211:10845169,10844017:pehd:4211:10845131,10844018:pehd:4211:10844955,10844019:pehd:4211:10845040,10844020:pehd:4211:10844684,10844021:pehd:4211:10844450,12107423:pehd:4211:12108306,12107427:pehd:4211:12108068,12107429:pehd:4211:12107833,6600532:pehd:4211:6601052,6600536:pehd:4211:6601428,6600541:pehd:4211:6601500,6600544:pehd:4211:6600852,6600546:pehd:4211:6601022,6600548:pehd:4211:6600702,6600551:pehd:4211:6600891,6600554:pehd:4211:6600597,6601134:pehd:4211:6602444,6601138:pehd:4211:6602601,6601142:pehd:4211:6601604,6601649:pehd:4211:6602281,6602728:pehd:4211:6603006,6602733:pehd:4211:6603409,6604399:pehd:4211:6608072,6605499:pehd:4211:6610648,6605503:pehd:4211:6605890,6605688:pehd:4211:6606075,6606683:pehd:4211:6607690,6607835:pehd:4211:6610407,6607837:pehd:4211:6610185,66'}, {'unwanted_material': 'pehd', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.0019293084660709505, 'nb_photo': 273, 'list_port_cont': '6601307,6601555,6601786,6602308,6603607,6603836,6604931,6606273,6606378,6607134,6607399,6607896,6608317,6608582,6609917,6610054,6610919,6611086,6613723,6616969,6621109,6621147,6621175,6621381,6621443,6622372,6622653,6622754,6626366,6626981,6628626,6629353,6629393,6635973,6636120,6636144,6636682,6637052,6637758,6638416,6638593,6638979,6639653,6639753,6639840,6639973,6640752,6640773,6640962,6641208,6641482,6641513,6641943,6642092,6643483,6643627,6643714,6643896,6645157,6645307,6645764,6648008,6648357,6648811,6649737,6652023,6652944,6654490,6655049,6656662,6657953,6663978,6670191,6671021,6672471,6673060,6674484,6675451,6675589,6676584,6681848,6682438,6684388,6686711,6687374,6687704,6688265,6689126,6689282,6689505,6690578,6690632,6692194,6692352,6692541,6692686,6693519,6694785,6696037,6697044,6697601,6697680,6698233,6698691,6698892,6698922,6699065,6710963,6711527,6712030,6712064,6712351,6712522,6712805,6712843,6713118,6713310,6717346,6720929,6721463,6723979,6724042,6725337,6725396,6727508,6740875,6741549,6742203,', 'assoc_port': '6600547:6601555,6600550:6601786,6600553:6601307,6601140:6602308,6602727:6603836,6602732:6603607,6604400:6604931,6604702:6606378,6605500:6606273,6605502:6607134,6606682:6608317,6606685:6607896,6606687:6607399,6607836:6608582,6609197:6610054,6609198:6609917,6610497:6611086,6610499:6610919,6612953:6613723,6616171:6616969,6620039:6621381,6620404:6621147,6620441:6621443,6620445:6621175,6620449:6621109,6621639:6622653,6621642:6622754,6621650:6622372,6625901:6626366,6626270:6626981,6627948:6628626,6628988:6629353,6628991:6629393,6630851:6635973,6630853:6636682,6630934:6637758,6631071:6638979,6631073:6639653,6631075:6639973,6631486:6641482,6631488:6640752,6631490:6640773,6631493:6640962,6631964:6642092,6631965:6641513,6632579:6645157,6632584:6645764,6632662:6643483,6632711:6643714,6632713:6648811,6632876:6643896,6632968:6645307,6633861:6637052,6635680:6636144,6635684:6636120,6637842:6638593,6637844:6638416,6638772:6639840,6638775:6639753,6640591:6641208,6641288:6643627,6641292:6641943,6646445:6648008,6647213:6648357,', 'assoc_mat': '6600547:pehd:3327:6601555,6600550:pehd:3327:6601786,6600553:pehd:3327:6601307,6601140:pehd:3327:6602308,6602727:pehd:3327:6603836,6602732:pehd:3327:6603607,6604400:pehd:3327:6604931,6604702:pehd:3327:6606378,6605500:pehd:3327:6606273,6605502:pehd:3327:6607134,6606682:pehd:3327:6608317,6606685:pehd:3327:6607896,6606687:pehd:3327:6607399,6607836:pehd:3327:6608582,6609197:pehd:3327:6610054,6609198:pehd:3327:6609917,6610497:pehd:3327:6611086,6610499:pehd:3327:6610919,6612953:pehd:3327:6613723,6616171:pehd:3327:6616969,6620039:pehd:3327:6621381,6620404:pehd:3327:6621147,6620441:pehd:3327:6621443,6620445:pehd:3327:6621175,6620449:pehd:3327:6621109,6621639:pehd:3327:6622653,6621642:pehd:3327:6622754,6621650:pehd:3327:6622372,6625901:pehd:3327:6626366,6626270:pehd:3327:6626981,6627948:pehd:3327:6628626,6628988:pehd:3327:6629353,6628991:pehd:3327:6629393,6630851:pehd:3327:6635973,6630853:pehd:3327:6636682,6630934:pehd:3327:6637758,6631071:pehd:3327:6638979,6631073:pehd:3327:6639653,6631075:pehd:3327:6639973,6631486:pe'}, {'unwanted_material': 'pehd', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.0051100227990344554, 'nb_photo': 146, 'list_port_cont': '6604877,6609170,6613068,6615614,6618503,6620187,6620866,6621902,6628367,6634250,6637080,6637448,6641565,6653431,6654691,6655698,6658465,6662390,6665200,6669580,6670050,6672621,6674514,6683659,6684464,6687228,6696451,6698464,6711561,6712283,6712785,6714507,6717609,6719826,6720245,6720364,6721249,6740804,6745511,6745786,6750247,6754189,6754553,6759422,6765273,6766697,6780403,6780526,6780710,6786856,6788637,6791338,6793691,6795538,6797665,6821369,6824092,6842448,6849223,6851780,6852369,6854529,6859027,6862725,6866337,6867970,6869263,6875756,6882722,6888109,6895287,6902508,6914447,6920759,6921629,6924255', 'assoc_port': '6604397:6604877,6608145:6609170,6612632:6613068,6615365:6615614,6618309:6618503,6620043:6620187,6620439:6620866,6621653:6621902,6627944:6628367,6631186:6634250,6631961:6655698,6632583:6662390,6632966:6670050,6635673:6637448,6635681:6637080,6641286:6641565,6650951:6653431,6654041:6654691,6657889:6658465,6664429:6665200,6668950:6669580,6671809:6672621,6673895:6674514,6681680:6683659,6683618:6684464,6685440:6720364,6686027:6719826,6686312:6687228,6688871:6714507,6690894:6698464,6693641:6696451,6710860:6711561,6711680:6712283,6712324:6712785,6716688:6717609,6718843:6720245,6720803:6721249,6740018:6740804,6740709:6745511,6744268:6745786,6748062:6750247,6753406:6754553,6753419:6754189,6759057:6759422,6764848:6765273,6765563:6766697,6779960:6780710,6779977:6780526,6780017:6780403,6786463:6786856,6787690:6788637,6790918:6791338,6793238:6793691,6795258:6795538,6797266:6797665,6820776:6821369,6823753:6824092,6842061:6842448,6848832:6849223,6850353:6851780,6852123:6852369,6854184:6854529,6858797:6859027,6862270:6862725,', 'assoc_mat': '6604397:pehd:4200:6604877,6608145:pehd:4200:6609170,6612632:pehd:4200:6613068,6615365:pehd:4200:6615614,6618309:pehd:4200:6618503,6620043:pehd:4200:6620187,6620439:pehd:4200:6620866,6621653:pehd:4200:6621902,6627944:pehd:4200:6628367,6631186:pehd:4200:6634250,6631961:pehd:4200:6655698,6632583:pehd:4200:6662390,6632966:pehd:4200:6670050,6635673:pehd:4200:6637448,6635681:pehd:4200:6637080,6641286:pehd:4200:6641565,6650951:pehd:4200:6653431,6654041:pehd:4200:6654691,6657889:pehd:4200:6658465,6664429:pehd:4200:6665200,6668950:pehd:4200:6669580,6671809:pehd:4200:6672621,6673895:pehd:4200:6674514,6681680:pehd:4200:6683659,6683618:pehd:4200:6684464,6685440:pehd:4200:6720364,6686027:pehd:4200:6719826,6686312:pehd:4200:6687228,6688871:pehd:4200:6714507,6690894:pehd:4200:6698464,6693641:pehd:4200:6696451,6710860:pehd:4200:6711561,6711680:pehd:4200:6712283,6712324:pehd:4200:6712785,6716688:pehd:4200:6717609,6718843:pehd:4200:6720245,6720803:pehd:4200:6721249,6740018:pehd:4200:6740804,6740709:pehd:4200:6745511,6744268:pe'}, {'unwanted_material': 'pet_clair', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0005167916399878171, 'nb_photo': 10, 'list_port_cont': '6608803,6712721,6754068,6800312,6806350,6808098,6876008,6940836', 'assoc_port': '6608146:6608803,6685991:6800312,6712332:6712721,6753416:6754068,6786466:6808098,6793236:6806350,6875674:6876008,6900378:6940836', 'assoc_mat': '6608146:pet_clair:4203:6608803,6685991:pet_clair:4203:6800312,6712332:pet_clair:4203:6712721,6753416:pet_clair:4203:6754068,6786466:pet_clair:4203:6808098,6793236:pet_clair:4203:6806350,6875674:pet_clair:4203:6876008,6900378:pet_clair:4203:6940836'}, {'unwanted_material': 'pet_clair', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.002615200546649331, 'nb_photo': 21, 'list_port_cont': '6600774,6602155,6638508,6669283,6673392,6694184,6695455,6712247,6723146,6740495,6742322,6751442,6754773', 'assoc_port': '6600540:6602155,6600552:6600774,6631482:6669283,6638162:6638508,6672700:6673392,6692502:6694184,6694740:6695455,6710867:6712247,6722817:6723146,6739465:6742322,6740030:6740495,6741911:6754773,6750987:6751442', 'assoc_mat': '6600540:pet_clair:4209:6602155,6600552:pet_clair:4209:6600774,6631482:pet_clair:4209:6669283,6638162:pet_clair:4209:6638508,6672700:pet_clair:4209:6673392,6692502:pet_clair:4209:6694184,6694740:pet_clair:4209:6695455,6710867:pet_clair:4209:6712247,6722817:pet_clair:4209:6723146,6739465:pet_clair:4209:6742322,6740030:pet_clair:4209:6740495,6741911:pet_clair:4209:6754773,6750987:pet_clair:4209:6751442'}, {'unwanted_material': 'pet_clair', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.0001938470394354349, 'nb_photo': 4, 'list_port_cont': '6869295,6869655,6914936,6960440', 'assoc_port': '6868823:6869295,6869543:6869655,6914405:6914936,6919131:6960440', 'assoc_mat': '6868823:pet_clair:4209:6869295,6869543:pet_clair:4209:6869655,6914405:pet_clair:4209:6914936,6919131:pet_clair:4209:6960440'}, {'unwanted_material': 'pet_clair', 'main_material': 'pcnc', 'pht_type': 4207, 'ratio': 0.0010334097253368944, 'nb_photo': 3, 'list_port_cont': '6617252,6617677,6633560', 'assoc_port': '6616962:6617252,6616964:6617677,6631478:6633560', 'assoc_mat': '6616962:pet_clair:4207:6617252,6616964:pet_clair:4207:6617677,6631478:pet_clair:4207:6633560'}, {'unwanted_material': 'pet_clair', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.0015524153897367723, 'nb_photo': 1500, 'list_port_cont': '6600858,6600897,6601013,6601431,6602282,6602595,6603012,6603407,6606068,6607680,6608064,6608283,6609454,6610409,6610606,6610649,6611145,6614534,6615393,6615436,6615797,6616526,6617426,6618921,6620330,6620914,6621054,6621668,6621965,6621997,6622134,6622196,6622457,6624048,6625562,6626620,6626654,6627270,6628482,6629176,6629250,6634388,6634656,6634855,6635135,6635836,6638280,6638446,6639162,6640996,6642051,6642710,6650049,6650151,6650272,6650962,6658506,6664046,6666445,6668082,6668178,6669608,6672025,6674016,6674544,6674638,6674762,6675076,6675106,6675132,6675480,6676532,6676622,6677125,6677222,6677808,6678040,6678226,6678568,6678651,6678771,6678961,6678993,6679220,6681247,6687136,6691495,6691657,6692280,6693193,6693673,6693892,6694033,6694210,6694409,6695508,6697514,6698425,6698773,6698798,6699368,6699436,6705438,6706628,6706772,6707230,6709725,6711231,6711405,6711487,6712154,6712633,6712745,6713436,6713606,6713756,6714401,6716473,6716566,6717505,6717554,6718023,6718524,6719029,6719453,6719723,6720668,6720734,', 'assoc_port': '10844008:10846061,10844009:10845791,10844010:10846028,10844011:10845556,10844012:10845977,10844014:10845189,10844015:10845087,10844017:10845127,10844018:10844952,10844019:10845053,10844020:10844681,10844021:10844449,12107423:12108303,12107427:12108066,12107429:12107823,6600536:6601431,6600544:6600858,6600546:6601013,6600551:6600897,6601138:6602595,6601649:6602282,6602728:6603012,6602733:6603407,6604399:6608064,6605499:6610649,6605688:6606068,6606683:6607680,6607835:6610409,6607839:6608283,6609199:6609454,6610498:6610606,6610992:6611145,6614199:6614534,6614967:6615393,6614969:6615436,6615362:6615797,6616173:6616526,6616982:6617426,6618311:6618921,6620040:6620330,6620422:6621668,6620444:6621054,6620447:6620914,6621638:6624048,6621643:6622457,6621647:6622196,6621651:6622134,6621654:6621965,6621659:6621997,6625236:6625562,6626268:6626620,6626274:6626654,6627098:6627270,6627945:6628482,6628987:6629176,6628989:6629250,6630850:6634855,6630852:6674016,6630856:6634656,6630932:6674638,6630935:6674762,6630939:6675106,66', 'assoc_mat': '10844008:pet_clair:4211:10846061,10844009:pet_clair:4211:10845791,10844010:pet_clair:4211:10846028,10844011:pet_clair:4211:10845556,10844012:pet_clair:4211:10845977,10844014:pet_clair:4211:10845189,10844015:pet_clair:4211:10845087,10844017:pet_clair:4211:10845127,10844018:pet_clair:4211:10844952,10844019:pet_clair:4211:10845053,10844020:pet_clair:4211:10844681,10844021:pet_clair:4211:10844449,12107423:pet_clair:4211:12108303,12107427:pet_clair:4211:12108066,12107429:pet_clair:4211:12107823,6600536:pet_clair:4211:6601431,6600544:pet_clair:4211:6600858,6600546:pet_clair:4211:6601013,6600551:pet_clair:4211:6600897,6601138:pet_clair:4211:6602595,6601649:pet_clair:4211:6602282,6602728:pet_clair:4211:6603012,6602733:pet_clair:4211:6603407,6604399:pet_clair:4211:6608064,6605499:pet_clair:4211:6610649,6605688:pet_clair:4211:6606068,6606683:pet_clair:4211:6607680,6607835:pet_clair:4211:6610409,6607839:pet_clair:4211:6608283,6609199:pet_clair:4211:6609454,6610498:pet_clair:4211:6610606,6610992:pet_clair:4211:6611145,66'}, {'unwanted_material': 'pet_clair', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.9599310617320319, 'nb_photo': 267351, 'list_port_cont': '6601210,6601259,6601315,6601372,6601561,6601699,6601793,6601843,6602315,6603612,6603846,6603988,6604935,6606277,6606379,6607135,6607387,6607887,6608320,6608594,6608685,6608963,6609915,6610051,6610272,6610908,6611079,6613731,6614159,6615900,6616061,6616197,6616980,6617460,6617848,6618172,6618946,6620507,6620990,6621111,6621145,6621178,6621380,6621448,6621534,6622108,6622166,6622320,6622376,6622431,6622522,6622656,6622746,6626362,6626970,6627008,6627198,6627829,6628015,6628553,6628621,6629351,6629395,6635805,6635872,6635894,6635946,6635979,6636038,6636114,6636146,6636284,6636327,6636427,6636595,6636676,6636732,6636800,6637047,6637357,6637676,6637709,6637759,6637850,6637946,6637981,6638089,6638412,6638604,6638708,6638827,6638952,6638975,6639189,6639290,6639357,6639644,6639751,6639842,6639900,6639967,6640118,6640724,6640748,6640771,6640953,6641215,6641298,6641330,6641481,6641511,6641662,6641812,6641955,6642028,6642082,6642295,6643245,6643371,6643476,6643601,6643634,6643665,6643684,6643715,6643904,6644075,6644466,', 'assoc_port': '6600535:6601259,6600537:6601699,6600543:6601210,6600545:6601372,6600547:6601561,6600550:6601793,6600553:6601315,6601140:6602315,6601199:6601843,6602727:6603846,6602729:6603988,6602732:6603612,6604400:6604935,6604702:6606379,6605500:6606277,6605502:6607135,6606682:6608320,6606685:6607887,6606687:6607387,6607836:6608594,6607838:6608685,6608144:6608963,6609197:6610051,6609198:6609915,6609963:6610272,6610497:6611079,6610499:6610908,6612953:6613731,6613333:6614159,6614966:6616197,6614968:6616061,6615360:6615900,6616171:6616980,6616960:6618172,6616966:6617460,6616967:6617848,6618310:6618946,6620036:6620507,6620039:6621380,6620042:6620990,6620404:6621145,6620441:6621448,6620445:6621178,6620446:6621534,6620449:6621111,6621639:6622656,6621642:6622746,6621645:6622522,6621650:6622376,6621652:6622431,6621655:6622166,6621656:6622108,6621661:6622320,6625901:6626362,6626270:6626970,6626272:6627008,6626275:6627198,6627097:6627829,6627099:6628015,6627946:6628553,6627948:6628621,6628988:6629351,6628991:6629395,6630847:6635946,', 'assoc_mat': '6600535:pet_clair:3327:6601259,6600537:pet_clair:3327:6601699,6600543:pet_clair:3327:6601210,6600545:pet_clair:3327:6601372,6600547:pet_clair:3327:6601561,6600550:pet_clair:3327:6601793,6600553:pet_clair:3327:6601315,6601140:pet_clair:3327:6602315,6601199:pet_clair:3327:6601843,6602727:pet_clair:3327:6603846,6602729:pet_clair:3327:6603988,6602732:pet_clair:3327:6603612,6604400:pet_clair:3327:6604935,6604702:pet_clair:3327:6606379,6605500:pet_clair:3327:6606277,6605502:pet_clair:3327:6607135,6606682:pet_clair:3327:6608320,6606685:pet_clair:3327:6607887,6606687:pet_clair:3327:6607387,6607836:pet_clair:3327:6608594,6607838:pet_clair:3327:6608685,6608144:pet_clair:3327:6608963,6609197:pet_clair:3327:6610051,6609198:pet_clair:3327:6609915,6609963:pet_clair:3327:6610272,6610497:pet_clair:3327:6611079,6610499:pet_clair:3327:6610908,6612953:pet_clair:3327:6613731,6613333:pet_clair:3327:6614159,6614966:pet_clair:3327:6616197,6614968:pet_clair:3327:6616061,6615360:pet_clair:3327:6615900,6616171:pet_clair:3327:6616980,6'}, {'unwanted_material': 'pet_clair', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.013850132418607056, 'nb_photo': 729, 'list_port_cont': '6600932,6602056,6604868,6606116,6609176,6613074,6615622,6618504,6620175,6620851,6621896,6626788,6628359,6634256,6635400,6637083,6637447,6638371,6639240,6641570,6646011,6647331,6649288,6649483,6649916,6653442,6654690,6655705,6658463,6662388,6665196,6669581,6670064,6672613,6674519,6683665,6684474,6686801,6687235,6687462,6696450,6696819,6698459,6711564,6712276,6712777,6714501,6717602,6719815,6720155,6720212,6720236,6720365,6721245,6722531,6724155,6739274,6740798,6745510,6745796,6750242,6754194,6754554,6759416,6765275,6766695,6767363,6780396,6780522,6780718,6784027,6786850,6788647,6791335,6793696,6795535,6797659,6813917,6817661,6821368,6824094,6835198,6836547,6840209,6842441,6849227,6851786,6852367,6854528,6859033,6862731,6865772,6866324,6867968,6869265,6874404,6875758,6875978,6882732,6886808,6888105,6889504,6895291,6900326,6902507,6904839,6914449,6920746,6921620,6924256', 'assoc_port': '6600538:6600932,6601136:6602056,6604397:6604868,6605689:6606116,6608145:6609176,6612632:6613074,6615365:6615622,6618309:6618504,6620043:6620175,6620439:6620851,6621653:6621896,6626271:6626788,6627944:6628359,6630848:6646011,6631070:6649288,6631186:6634256,6631194:6649916,6631961:6655705,6632583:6662388,6632786:6635400,6632966:6670064,6635673:6637447,6635681:6637083,6637843:6638371,6638774:6639240,6641286:6641570,6646447:6647331,6648940:6649483,6650951:6653442,6654041:6654690,6657889:6658463,6664429:6665196,6668950:6669581,6671809:6672613,6673895:6674519,6681680:6683665,6683618:6684474,6685440:6720365,6685511:6720212,6686027:6719815,6686041:6720155,6686295:6687462,6686312:6687235,6686325:6686801,6688871:6714501,6690894:6698459,6693641:6696450,6696476:6696819,6710860:6711564,6711680:6712276,6712324:6712777,6716688:6717602,6718843:6720236,6720803:6721245,6722354:6722531,6723892:6724155,6738748:6739274,6740018:6740798,6740709:6745510,6744268:6745796,6748062:6750242,6753406:6754554,6753419:6754194,6759057:6759416,', 'assoc_mat': '6600538:pet_clair:4200:6600932,6601136:pet_clair:4200:6602056,6604397:pet_clair:4200:6604868,6605689:pet_clair:4200:6606116,6608145:pet_clair:4200:6609176,6612632:pet_clair:4200:6613074,6615365:pet_clair:4200:6615622,6618309:pet_clair:4200:6618504,6620043:pet_clair:4200:6620175,6620439:pet_clair:4200:6620851,6621653:pet_clair:4200:6621896,6626271:pet_clair:4200:6626788,6627944:pet_clair:4200:6628359,6630848:pet_clair:4200:6646011,6631070:pet_clair:4200:6649288,6631186:pet_clair:4200:6634256,6631194:pet_clair:4200:6649916,6631961:pet_clair:4200:6655705,6632583:pet_clair:4200:6662388,6632786:pet_clair:4200:6635400,6632966:pet_clair:4200:6670064,6635673:pet_clair:4200:6637447,6635681:pet_clair:4200:6637083,6637843:pet_clair:4200:6638371,6638774:pet_clair:4200:6639240,6641286:pet_clair:4200:6641570,6646447:pet_clair:4200:6647331,6648940:pet_clair:4200:6649483,6650951:pet_clair:4200:6653442,6654041:pet_clair:4200:6654690,6657889:pet_clair:4200:6658463,6664429:pet_clair:4200:6665196,6668950:pet_clair:4200:6669581,6'}, {'unwanted_material': 'pet_fonce', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.00022092315643821876, 'nb_photo': 2, 'list_port_cont': '6712712,6806360', 'assoc_port': '6712332:6712712,6793236:6806360', 'assoc_mat': '6712332:pet_fonce:4203:6712712,6793236:pet_fonce:4203:6806360'}, {'unwanted_material': 'pet_fonce', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.0003846699616495868, 'nb_photo': 87, 'list_port_cont': '6600861,6603394,6607677,6615422,6622000,6622197,6624062,6627276,6628479,6629185,6634858,6635145,6672029,6674545,6675144,6675478,6677810,6678775,6691486,6694039,6698430,6699376,6699426,6707232,6719024,6720655,6721843,6723783,6724752,6724915,6725834,6730262,6732433,6741928,6743868,6751578,6751686,6754292,6756063,6768401,6769194,6775051,6780889,6782985,6783127,6789494,6789900,6792092,6794401,6795573,6800465,6800534,6800819,6801501,6808800,6808842,6835780,6855452,6869892,6885665,6917719,6920225,6922977,10844443', 'assoc_port': '10844021:10844443,6600544:6600861,6602733:6603394,6606683:6607677,6614969:6615422,6621638:6624062,6621647:6622197,6621659:6622000,6627098:6627276,6627945:6628479,6628987:6629185,6630850:6634858,6631069:6675478,6631193:6635145,6631960:6677810,6632714:6678775,6635676:6694039,6640592:6707232,6647212:6719024,6651292:6721843,6654038:6724915,6654837:6725834,6668952:6751578,6671805:6756063,6671810:6672029,6673889:6674545,6674723:6675144,6683614:6789900,6683620:6691486,6686021:6855452,6686031:6800465,6686094:6800534,6686298:6800819,6686318:6801501,6688870:6732433,6690893:6730262,6694730:6699426,6696471:6699376,6696477:6698430,6720298:6720655,6723498:6723783,6724591:6724752,6741314:6741928,6743337:6743868,6750985:6751686,6753415:6754292,6764851:6808842,6765559:6808800,6767894:6768401,6768944:6769194,6773721:6775051,6780010:6780889,6782241:6782985,6782244:6783127,6788947:6789494,6791774:6792092,6793940:6794401,6795260:6795573,6833511:6869892,6835299:6835780,6885269:6885665,6916370:6917719,6919132:6920225,6920914:692297', 'assoc_mat': '10844021:pet_fonce:4211:10844443,6600544:pet_fonce:4211:6600861,6602733:pet_fonce:4211:6603394,6606683:pet_fonce:4211:6607677,6614969:pet_fonce:4211:6615422,6621638:pet_fonce:4211:6624062,6621647:pet_fonce:4211:6622197,6621659:pet_fonce:4211:6622000,6627098:pet_fonce:4211:6627276,6627945:pet_fonce:4211:6628479,6628987:pet_fonce:4211:6629185,6630850:pet_fonce:4211:6634858,6631069:pet_fonce:4211:6675478,6631193:pet_fonce:4211:6635145,6631960:pet_fonce:4211:6677810,6632714:pet_fonce:4211:6678775,6635676:pet_fonce:4211:6694039,6640592:pet_fonce:4211:6707232,6647212:pet_fonce:4211:6719024,6651292:pet_fonce:4211:6721843,6654038:pet_fonce:4211:6724915,6654837:pet_fonce:4211:6725834,6668952:pet_fonce:4211:6751578,6671805:pet_fonce:4211:6756063,6671810:pet_fonce:4211:6672029,6673889:pet_fonce:4211:6674545,6674723:pet_fonce:4211:6675144,6683614:pet_fonce:4211:6789900,6683620:pet_fonce:4211:6691486,6686021:pet_fonce:4211:6855452,6686031:pet_fonce:4211:6800465,6686094:pet_fonce:4211:6800534,6686298:pet_fonce:4211:6800819'}, {'unwanted_material': 'pet_fonce', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.0027937857287839945, 'nb_photo': 263, 'list_port_cont': '6601312,6601378,6602302,6604937,6607386,6607897,6609918,6610056,6614156,6616198,6617469,6618949,6621166,6621524,6622171,6622318,6622432,6622523,6627007,6627194,6628022,6628558,6628620,6629360,6629385,6635890,6636150,6636335,6637041,6637956,6638951,6639300,6639360,6639649,6639843,6639963,6640116,6641206,6641660,6642031,6642088,6643666,6643686,6644074,6645311,6645758,6647413,6648013,6648348,6651389,6651678,6652033,6652947,6653022,6654605,6655887,6658048,6659096,6660216,6665644,6665949,6668610,6672473,6673067,6674828,6675455,6682558,6684379,6686720,6687693,6687751,6688499,6688987,6689537,6689587,6689850,6690097,6690214,6690635,6692361,6692530,6692691,6693861,6694779,6695712,6696111,6697041,6697340,6697715,6697768,6697941,6698112,6698730,6698987,6711529,6712340,6712870,6713125,6713218,6713281,6719368,6721992,6722018,6722072,6722229,6723374,6723733,6724035,6725340,6740139,6740418,6742670,6743702,6744133,6748415,6748972,6749009,6749528,6750026,6751763,6754264,6754530,6758447,6759452,6760150,6760421,6765840,6766308,', 'assoc_port': '6600545:6601378,6600553:6601312,6601140:6602302,6604400:6604937,6606685:6607897,6606687:6607386,6609197:6610056,6609198:6609918,6613333:6614156,6614966:6616198,6616966:6617469,6618310:6618949,6620445:6621166,6620446:6621524,6621645:6622523,6621652:6622432,6621655:6622171,6621661:6622318,6626272:6627007,6626275:6627194,6627099:6628022,6627946:6628558,6627948:6628620,6628988:6629360,6628991:6629385,6631066:6641660,6631073:6639649,6631075:6639963,6631076:6639360,6631185:6638951,6631476:6640116,6631964:6642088,6632584:6645758,6632785:6635890,6632788:6643666,6632880:6636335,6632882:6644074,6632968:6645311,6633861:6637041,6635680:6636150,6637237:6637956,6637846:6639300,6638772:6639843,6640591:6641206,6640596:6643686,6641258:6642031,6646445:6648013,6646448:6647413,6647213:6648348,6649832:6652033,6650508:6651389,6650511:6651678,6650952:6652947,6652019:6653022,6653519:6654605,6654835:6655887,6654836:6659096,6656899:6658048,6657892:6660216,6664425:6665644,6664427:6665949,6667559:6668610,6671807:6672473,6671811:6673067,', 'assoc_mat': '6600545:pet_fonce:3327:6601378,6600553:pet_fonce:3327:6601312,6601140:pet_fonce:3327:6602302,6604400:pet_fonce:3327:6604937,6606685:pet_fonce:3327:6607897,6606687:pet_fonce:3327:6607386,6609197:pet_fonce:3327:6610056,6609198:pet_fonce:3327:6609918,6613333:pet_fonce:3327:6614156,6614966:pet_fonce:3327:6616198,6616966:pet_fonce:3327:6617469,6618310:pet_fonce:3327:6618949,6620445:pet_fonce:3327:6621166,6620446:pet_fonce:3327:6621524,6621645:pet_fonce:3327:6622523,6621652:pet_fonce:3327:6622432,6621655:pet_fonce:3327:6622171,6621661:pet_fonce:3327:6622318,6626272:pet_fonce:3327:6627007,6626275:pet_fonce:3327:6627194,6627099:pet_fonce:3327:6628022,6627946:pet_fonce:3327:6628558,6627948:pet_fonce:3327:6628620,6628988:pet_fonce:3327:6629360,6628991:pet_fonce:3327:6629385,6631066:pet_fonce:3327:6641660,6631073:pet_fonce:3327:6639649,6631075:pet_fonce:3327:6639963,6631076:pet_fonce:3327:6639360,6631185:pet_fonce:3327:6638951,6631476:pet_fonce:3327:6640116,6631964:pet_fonce:3327:6642088,6632584:pet_fonce:3327:6645758,6'}, {'unwanted_material': 'pet_fonce', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.9433959349191054, 'nb_photo': 1865, 'list_port_cont': '6600921,6602052,6604879,6606107,6609173,6613064,6615625,6618497,6620185,6620862,6621905,6626786,6628358,6634244,6635391,6637070,6637445,6638364,6639248,6641566,6646009,6647335,6649279,6649476,6649915,6653441,6654686,6655703,6658466,6662391,6665191,6669589,6670061,6672609,6674517,6683670,6684465,6686804,6687227,6687466,6696439,6696818,6698462,6711554,6712271,6712775,6714495,6717596,6719814,6720150,6720211,6720250,6720367,6721247,6722527,6724149,6739286,6740801,6745519,6745787,6750252,6754190,6754558,6759425,6765276,6766699,6767360,6780401,6780523,6780723,6784025,6786849,6788646,6791336,6793695,6795539,6797667,6813921,6817652,6821383,6824097,6835196,6836551,6840207,6842434,6849224,6851782,6852366,6854523,6859029,6862727,6865773,6866335,6867976,6869266,6874407,6875753,6875975,6882723,6886803,6888103,6889495,6895281,6900334,6902509,6904835,6914448,6920755,6921623,6924260', 'assoc_port': '6600538:6600921,6601136:6602052,6604397:6604879,6605689:6606107,6608145:6609173,6612632:6613064,6615365:6615625,6618309:6618497,6620043:6620185,6620439:6620862,6621653:6621905,6626271:6626786,6627944:6628358,6630848:6646009,6631070:6649279,6631186:6634244,6631194:6649915,6631961:6655703,6632583:6662391,6632786:6635391,6632966:6670061,6635673:6637445,6635681:6637070,6637843:6638364,6638774:6639248,6641286:6641566,6646447:6647335,6648940:6649476,6650951:6653441,6654041:6654686,6657889:6658466,6664429:6665191,6668950:6669589,6671809:6672609,6673895:6674517,6681680:6683670,6683618:6684465,6685440:6720367,6685511:6720211,6686027:6719814,6686041:6720150,6686295:6687466,6686312:6687227,6686325:6686804,6688871:6714495,6690894:6698462,6693641:6696439,6696476:6696818,6710860:6711554,6711680:6712271,6712324:6712775,6716688:6717596,6718843:6720250,6720803:6721247,6722354:6722527,6723892:6724149,6738748:6739286,6740018:6740801,6740709:6745519,6744268:6745787,6748062:6750252,6753406:6754558,6753419:6754190,6759057:6759425,', 'assoc_mat': '6600538:pet_fonce:4200:6600921,6601136:pet_fonce:4200:6602052,6604397:pet_fonce:4200:6604879,6605689:pet_fonce:4200:6606107,6608145:pet_fonce:4200:6609173,6612632:pet_fonce:4200:6613064,6615365:pet_fonce:4200:6615625,6618309:pet_fonce:4200:6618497,6620043:pet_fonce:4200:6620185,6620439:pet_fonce:4200:6620862,6621653:pet_fonce:4200:6621905,6626271:pet_fonce:4200:6626786,6627944:pet_fonce:4200:6628358,6630848:pet_fonce:4200:6646009,6631070:pet_fonce:4200:6649279,6631186:pet_fonce:4200:6634244,6631194:pet_fonce:4200:6649915,6631961:pet_fonce:4200:6655703,6632583:pet_fonce:4200:6662391,6632786:pet_fonce:4200:6635391,6632966:pet_fonce:4200:6670061,6635673:pet_fonce:4200:6637445,6635681:pet_fonce:4200:6637070,6637843:pet_fonce:4200:6638364,6638774:pet_fonce:4200:6639248,6641286:pet_fonce:4200:6641566,6646447:pet_fonce:4200:6647335,6648940:pet_fonce:4200:6649476,6650951:pet_fonce:4200:6653441,6654041:pet_fonce:4200:6654686,6657889:pet_fonce:4200:6658466,6664429:pet_fonce:4200:6665191,6668950:pet_fonce:4200:6669589,6'}, {'unwanted_material': 'pet_opaque', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0009264502462378221, 'nb_photo': 9, 'list_port_cont': '6608814,6660097,6712722,6735145,6799275,6808109,6869931', 'assoc_port': '6608146:6608814,6632879:6660097,6685476:6799275,6690887:6735145,6712332:6712722,6786466:6808109,6869544:6869931', 'assoc_mat': '6608146:pet_opaque:4203:6608814,6632879:pet_opaque:4203:6660097,6685476:pet_opaque:4203:6799275,6690887:pet_opaque:4203:6735145,6712332:pet_opaque:4203:6712722,6786466:pet_opaque:4203:6808109,6869544:pet_opaque:4203:6869931'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.007255625347838711, 'nb_photo': 19, 'list_port_cont': '6616740,6617521,6620419,6621938,6627043,6638500,6668139,6668209,6670564,6670896,6678093,6740485,6791207', 'assoc_port': '6616168:6616740,6616963:6617521,6620037:6620419,6621646:6621938,6626722:6627043,6631190:6668139,6631196:6668209,6632661:6670564,6632883:6670896,6638162:6638500,6677866:6678093,6740030:6740485,6790915:6791207', 'assoc_mat': '6616168:pet_opaque:4209:6616740,6616963:pet_opaque:4209:6617521,6620037:pet_opaque:4209:6620419,6621646:pet_opaque:4209:6621938,6626722:pet_opaque:4209:6627043,6631190:pet_opaque:4209:6668139,6631196:pet_opaque:4209:6668209,6632661:pet_opaque:4209:6670564,6632883:pet_opaque:4209:6670896,6638162:pet_opaque:4209:6638500,6677866:pet_opaque:4209:6678093,6740030:pet_opaque:4209:6740485,6790915:pet_opaque:4209:6791207'}, {'unwanted_material': 'pet_opaque', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.0016345171833525593, 'nb_photo': 222, 'list_port_cont': '6600901,6602283,6603013,6606069,6608294,6610401,6610657,6611146,6614528,6615400,6621062,6621675,6621998,6622129,6622205,6626613,6629184,6629244,6634651,6635837,6638286,6639164,6650157,6668179,6669616,6672038,6674023,6674634,6674773,6675099,6675136,6677119,6677811,6678215,6678578,6678957,6679224,6681250,6691510,6691659,6693900,6694213,6694417,6698434,6698768,6698799,6699430,6711411,6711493,6712146,6712497,6712754,6713439,6716561,6717501,6717546,6718028,6719452,6720657,6720722,6724914,6725690,6725833,6728841,6729870,6730329,6730727,6731863,6732437,6735376,6738126,6740205,6740607,6741938,6743382,6745286,6751579,6751673,6754142,6754990,6755968,6756060,6756975,6762171,6763524,6767274,6768410,6773018,6774679,6775056,6778932,6780264,6780486,6783061,6791499,6793831,6794248,6794400,6795572,6796823,6799103,6799441,6799779,6800492,6800530,6801272,6801402,6806174,6808133,6808682,6808713,6808832,6808873,6809086,6810005,6815419,6822330,6839899,6842610,6853846,6855228,6855448,6863478,6866679,6869888,6870654,6875922,6890225,', 'assoc_port': '10844011:10845551,10844021:10844440,6600551:6600901,6601649:6602283,6602728:6603013,6605499:6610657,6605688:6606069,6607835:6610401,6607839:6608294,6610992:6611146,6614199:6614528,6614967:6615400,6620422:6621675,6620444:6621062,6621647:6622205,6621651:6622129,6621659:6621998,6626268:6626613,6628987:6629184,6628989:6629244,6630852:6674023,6630856:6634651,6630932:6674634,6630935:6674773,6630939:6675099,6631489:6677119,6631960:6677811,6632585:6678215,6632660:6678578,6632877:6678957,6632963:6679224,6633862:6635837,6635671:6693900,6635682:6694213,6637236:6698768,6637845:6638286,6638776:6639164,6640595:6806174,6647215:6717546,6648944:6650157,6653523:6725690,6654038:6724914,6654837:6725833,6655426:6728841,6656895:6729870,6657891:6731863,6666361:6668179,6668952:6751579,6668953:6669616,6671805:6756060,6671810:6672038,6672701:6755968,6674723:6675136,6681005:6681250,6683620:6691510,6685431:6799103,6685494:6799441,6685525:6799779,6686006:6855228,6686021:6855448,6686094:6800530,6686269:6800492,6686302:6801272,6686308:6801', 'assoc_mat': '10844011:pet_opaque:4211:10845551,10844021:pet_opaque:4211:10844440,6600551:pet_opaque:4211:6600901,6601649:pet_opaque:4211:6602283,6602728:pet_opaque:4211:6603013,6605499:pet_opaque:4211:6610657,6605688:pet_opaque:4211:6606069,6607835:pet_opaque:4211:6610401,6607839:pet_opaque:4211:6608294,6610992:pet_opaque:4211:6611146,6614199:pet_opaque:4211:6614528,6614967:pet_opaque:4211:6615400,6620422:pet_opaque:4211:6621675,6620444:pet_opaque:4211:6621062,6621647:pet_opaque:4211:6622205,6621651:pet_opaque:4211:6622129,6621659:pet_opaque:4211:6621998,6626268:pet_opaque:4211:6626613,6628987:pet_opaque:4211:6629184,6628989:pet_opaque:4211:6629244,6630852:pet_opaque:4211:6674023,6630856:pet_opaque:4211:6634651,6630932:pet_opaque:4211:6674634,6630935:pet_opaque:4211:6674773,6630939:pet_opaque:4211:6675099,6631489:pet_opaque:4211:6677119,6631960:pet_opaque:4211:6677811,6632585:pet_opaque:4211:6678215,6632660:pet_opaque:4211:6678578,6632877:pet_opaque:4211:6678957,6632963:pet_opaque:4211:6679224,6633862:pet_opaque:4211:6635'}, {'unwanted_material': 'pet_opaque', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.006625372408584668, 'nb_photo': 447, 'list_port_cont': '6600927,6602060,6604867,6606114,6609165,6613073,6615618,6618507,6620186,6620865,6621897,6626785,6628362,6634254,6635389,6637069,6637443,6638357,6639233,6641563,6646016,6647330,6649289,6649475,6649908,6653440,6654682,6655700,6658472,6662384,6665186,6669577,6670052,6672606,6674518,6683671,6684478,6686802,6687225,6687464,6696442,6696817,6698458,6711562,6712277,6712786,6714508,6717607,6719819,6720158,6720206,6720247,6720358,6721243,6722532,6724146,6739282,6740800,6745521,6745789,6750245,6754191,6754564,6759415,6765265,6766702,6767358,6780406,6780528,6780714,6784016,6786843,6788645,6791330,6793704,6795548,6797668,6817665,6821371,6824096,6835190,6840200,6842443,6849228,6851781,6852374,6854530,6859022,6862735,6865768,6866326,6867982,6869259,6874403,6875763,6882730,6886804,6888096,6889498,6895283,6900338,6902503,6904834,6914452,6920753,6921628,6924247', 'assoc_port': '6600538:6600927,6601136:6602060,6604397:6604867,6605689:6606114,6608145:6609165,6612632:6613073,6615365:6615618,6618309:6618507,6620043:6620186,6620439:6620865,6621653:6621897,6626271:6626785,6627944:6628362,6630848:6646016,6631070:6649289,6631186:6634254,6631194:6649908,6631961:6655700,6632583:6662384,6632786:6635389,6632966:6670052,6635673:6637443,6635681:6637069,6637843:6638357,6638774:6639233,6641286:6641563,6646447:6647330,6648940:6649475,6650951:6653440,6654041:6654682,6657889:6658472,6664429:6665186,6668950:6669577,6671809:6672606,6673895:6674518,6681680:6683671,6683618:6684478,6685440:6720358,6685511:6720206,6686027:6719819,6686041:6720158,6686295:6687464,6686312:6687225,6686325:6686802,6688871:6714508,6690894:6698458,6693641:6696442,6696476:6696817,6710860:6711562,6711680:6712277,6712324:6712786,6716688:6717607,6718843:6720247,6720803:6721243,6722354:6722532,6723892:6724146,6738748:6739282,6740018:6740800,6740709:6745521,6744268:6745789,6748062:6750245,6753406:6754564,6753419:6754191,6759057:6759415,', 'assoc_mat': '6600538:pet_opaque:4200:6600927,6601136:pet_opaque:4200:6602060,6604397:pet_opaque:4200:6604867,6605689:pet_opaque:4200:6606114,6608145:pet_opaque:4200:6609165,6612632:pet_opaque:4200:6613073,6615365:pet_opaque:4200:6615618,6618309:pet_opaque:4200:6618507,6620043:pet_opaque:4200:6620186,6620439:pet_opaque:4200:6620865,6621653:pet_opaque:4200:6621897,6626271:pet_opaque:4200:6626785,6627944:pet_opaque:4200:6628362,6630848:pet_opaque:4200:6646016,6631070:pet_opaque:4200:6649289,6631186:pet_opaque:4200:6634254,6631194:pet_opaque:4200:6649908,6631961:pet_opaque:4200:6655700,6632583:pet_opaque:4200:6662384,6632786:pet_opaque:4200:6635389,6632966:pet_opaque:4200:6670052,6635673:pet_opaque:4200:6637443,6635681:pet_opaque:4200:6637069,6637843:pet_opaque:4200:6638357,6638774:pet_opaque:4200:6639233,6641286:pet_opaque:4200:6641563,6646447:pet_opaque:4200:6647330,6648940:pet_opaque:4200:6649475,6650951:pet_opaque:4200:6653440,6654041:pet_opaque:4200:6654682,6657889:pet_opaque:4200:6658472,6664429:pet_opaque:4200:6665186,'}, {'unwanted_material': 'textiles_sanitaires', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 5.317677982697793e-05, 'nb_photo': 1, 'list_port_cont': '6754069', 'assoc_port': '6753416:6754069', 'assoc_mat': '6753416:textiles_sanitaires:4203:6754069'}, {'unwanted_material': 'textiles_sanitaires', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.00014015295179752374, 'nb_photo': 14, 'list_port_cont': '6608063,6675111,6678967,6709734,6720656,6724749,6725824,6735649,6787860,6791503,6813248,6828520,6855445,10845973', 'assoc_port': '10844012:10845973,6604399:6608063,6630939:6675111,6632877:6678967,6654837:6725824,6663625:6735649,6686021:6855445,6709374:6709734,6720298:6720656,6724591:6724749,6787210:6787860,6790919:6791503,6812962:6813248,6814154:6828520', 'assoc_mat': '10844012:textiles_sanitaires:4211:10845973,6604399:textiles_sanitaires:4211:6608063,6630939:textiles_sanitaires:4211:6675111,6632877:textiles_sanitaires:4211:6678967,6654837:textiles_sanitaires:4211:6725824,6663625:textiles_sanitaires:4211:6735649,6686021:textiles_sanitaires:4211:6855445,6709374:textiles_sanitaires:4211:6709734,6720298:textiles_sanitaires:4211:6720656,6724591:textiles_sanitaires:4211:6724749,6787210:textiles_sanitaires:4211:6787860,6790919:textiles_sanitaires:4211:6791503,6812962:textiles_sanitaires:4211:6813248,6814154:textiles_sanitaires:4211:6828520'}) select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(449271,454376,454388,454411,454399,454424,451519,452689,468274,455185,456478,457640,459828,461074,462392,463490,464885,465870,469411,470781,897152,474755,474338,475306,477478,478927,479886)); after get_hostname_from_raspi hasthag : emr hasthag that could be used but not yet : _______carton,background hasthag : jrm hasthag that could be used but not yet : _______papier,background hasthag : aluminium hasthag that could be used but not yet : _______metal,background hasthag : pet_fonce hasthag that could be used but not yet : _______pet_fonce,background hasthag : gm hasthag that could be used but not yet : _______papier,background after impurety_average_per_hashtag ############################### TEST csv ################################ Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_csv VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier create_report_from_API() got an unexpected keyword argument 'outfolder' ERROR csv FAILED #&_#_#&_# TEST memo SUCCEEDED #&_#_#&_# #&_#_#&_# TEST one_day SUCCEEDED #&_#_#&_# #&_#_#&_# TEST get_data SUCCEEDED #&_#_#&_# #&_#_#&_# TEST csv FAILED #&_#_#&_# #&_# TEST FAILED #&_# : prod/memo/memo #&_# #&_# END OF TEST #&_# : prod/memo/memo #&_# /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) /usr/lib/python3/dist-packages/paramiko/transport.py:220: CryptographyDeprecationWarning: Blowfish has been deprecated "class": algorithms.Blowfish, #######all_test_python_finish###### insert result in bdd : Test are not running on charlot, the path should be tested ! /data_2/data_log/job/2025/March/08032025/python_test3/output_tests_python-1235.html new path : /proc/4154994/ /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:720: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_origin_portfolio_ids = [int(item) for item in options.list_origin_portfolio_ids.split(",")] if options.list_origin_portfolio_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:721: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_photo_ids = [int(item) for item in options.list_photo_ids.split(",")] if options.list_photo_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:722: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval = [int(item) for item in options.interval_rotation.split(",")] if options.interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:723: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval = [float(item) for item in options.interval_resize.split(",")] if options.interval_resize is not "" else None /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:750: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi = [float(item) for item in options.mother_crop_portfolio_multi.split(",")] if options.mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1950: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1957: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2141: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2148: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None Name Stmts Miss Cover Missing ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ /home/admin/.local/lib/python3.8/site-packages/Crypto/Hash/SHA256.py 46 29 37% 72-80, 89-93, 104-112, 122, 135-140, 145, 158, 171-185 /home/admin/.local/lib/python3.8/site-packages/Crypto/Hash/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/Numbers.py 11 7 36% 36-42 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/Primality.py 154 141 8% 65-116, 134-213, 244-277, 314-335, 354-369 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/_IntegerBase.py 226 121 46% 43, 47, 51, 55, 60, 65, 69, 73, 77, 81, 85, 89, 94, 99, 103, 107, 111, 115, 119, 123, 127, 131, 135, 139, 143, 147, 152, 156, 160, 164, 168, 172, 176, 181, 185, 189, 193, 197, 201, 205, 209, 213, 217, 221, 225, 230, 249-295, 322-343, 364-391 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/_IntegerGMP.py 407 329 19% 98, 104, 110-118, 134-137, 155-191, 196-212, 215, 218, 222, 226, 248-276, 292-308, 312-314, 317-319, 322-324, 327, 330, 333, 336, 339, 343, 347-356, 359-368, 371-380, 383-392, 395-407, 411-446, 449-450, 453-455, 461-473, 476-491, 494-509, 512-528, 531-542, 546-552, 555-561, 564-575, 578-588, 591-597, 600-605, 611-617, 622, 625, 630-632, 636, 639, 644-653, 658-675, 680-684, 693-708, 711-713, 719-728, 734-738, 744-750, 755-762 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/PublicKey/RSA.py 325 267 18% 96-105, 109, 113, 117-119, 123-125, 129-131, 135-137, 141, 145, 148-150, 153-174, 179, 182, 185, 193, 196-202, 205, 209-210, 213-218, 221-225, 304-368, 376, 379, 382, 385, 388, 391, 394, 426-475, 518-618, 635-638, 646-647, 652-655, 660-661, 665-670, 676-688, 693-712, 756-791 /home/admin/.local/lib/python3.8/site-packages/Crypto/PublicKey/__init__.py 33 29 12% 46-60, 65-73, 79-94 /home/admin/.local/lib/python3.8/site-packages/Crypto/Random/__init__.py 16 6 62% 31, 35, 39, 43, 48, 52 /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/PKCS1_v1_5.py 14 9 36% 42-46, 49-52 /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/pkcs1_15.py 49 39 20% 49, 53, 73-84, 106-138, 191-207, 221 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/_file_system.py 8 1 88% 46 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/_raw_api.py 164 105 36% 42-46, 61, 66, 77, 99, 113, 118-126, 130, 134, 137-143, 149, 152, 155, 158, 162-261, 268-269, 272, 275-276, 279-284, 304-305, 307-309, 314, 318 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/asn1.py 316 251 21% 34-39, 47-49, 52, 55-56, 59, 62-68, 71, 107-141, 147-153, 160-163, 171-180, 187-197, 210-220, 225-246, 289-291, 297-306, 319, 325-341, 400-404, 409, 412, 415, 418, 421, 424, 427, 430-431, 434-435, 446-447, 460, 470-478, 501-507, 512-543, 589, 598, 643-644, 650-664, 680, 686-698, 743-749, 756-757, 772, 778-787, 827-836, 839, 842, 845, 856-869, 887, 892-919, 928-939 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/number.py 223 199 11% 40-47, 53-59, 72-81, 93-98, 111-114, 120-123, 128-136, 147-157, 173-206, 246-339, 359-374, 407-447, 464-483, 489-490, 492-493 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/py3compat.py 82 59 28% 66-110, 114, 116, 118-121, 123, 125-134, 136, 138, 147, 150, 153, 166-171 /home/admin/.local/lib/python3.8/site-packages/Crypto/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/BmpImagePlugin.py 218 179 18% 76-264, 276-284, 291-355, 366, 384, 388-449 /home/admin/.local/lib/python3.8/site-packages/PIL/ExifTags.py 340 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/GifImagePlugin.py 585 526 10% 71-74, 77-80, 84-108, 112-120, 124-139, 142-155, 158-410, 413-430, 433-456, 459, 480-491, 506-543, 547-564, 568-574, 578-649, 653, 658-670, 674-680, 684-746, 756-793, 812-844, 849-854, 865-872, 882, 886-905, 914-967, 971-983, 1002-1015, 1036-1048 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpGradientFile.py 68 53 22% 32-43, 47, 51, 55, 59, 70-98, 105-137 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpPaletteFile.py 26 20 23% 28-53, 56 /home/admin/.local/lib/python3.8/site-packages/PIL/Image.py 1693 1005 41% 44-45, 66-67, 78-79, 106-128, 135-136, 275, 287, 302, 313, 333-334, 339-340, 345-346, 351-352, 357-358, 375-387, 397, 406, 411-413, 420, 429, 434-436, 445-446, 454-455, 458, 461-463, 468, 471, 474-476, 481-483, 487-488, 526-527, 548-553, 564-566, 568, 585-587, 589, 595, 615-634, 637, 648, 662, 678-684, 694, 697-707, 712, 715-723, 749, 757, 772-773, 788-793, 817, 825-826, 828-829, 848-861, 865-871, 883, 938-943, 945, 949-969, 972, 978-1037, 1040-1057, 1060-1070, 1078-1088, 1092-1094, 1097, 1099-1111, 1150-1191, 1219-1230, 1244-1250, 1273, 1276-1279, 1294, 1296-1297, 1303-1306, 1316, 1330-1331, 1347-1357, 1379, 1392-1398, 1401-1429, 1440-1441, 1447, 1451-1453, 1461-1463, 1468-1471, 1474-1514, 1523-1524, 1539-1546, 1554-1569, 1581-1584, 1594-1596, 1619-1627, 1645-1653, 1697-1698, 1707-1712, 1716-1718, 1750-1785, 1811-1834, 1847-1890, 1906-1908, 1929-1943, 1967-1986, 1999-2071, 2077-2083, 2125-2126, 2135-2149, 2152-2153, 2161, 2164, 2167, 2170-2172, 2177-2186, 2208-2226, 2299, 2304, 2380-2381, 2385-2389, 2392, 2407, 2410-2412, 2415, 2417, 2427, 2433-2441, 2463-2464, 2486, 2502-2507, 2520-2529, 2540, 2582-2626, 2686, 2693, 2697, 2700-2701, 2705, 2731-2735, 2738, 2762-2763, 2770-2788, 2795, 2819-2820, 2824-2829, 2833-2838, 2873, 2885-2886, 2888-2889, 2891-2892, 2922-2924, 2929-2932, 2967, 3013, 3017, 3022-3024, 3085-3087, 3090-3092, 3100, 3102-3103, 3110, 3117-3122, 3127-3132, 3164, 3169-3173, 3176, 3213-3214, 3216-3220, 3224-3226, 3231, 3241-3243, 3255, 3260, 3266-3275, 3280-3282, 3293-3298, 3315-3317, 3338-3340, 3355-3357, 3373, 3388-3400, 3485-3486, 3514, 3522-3524, 3541, 3552, 3561, 3570, 3589-3606, 3660, 3668, 3671-3682, 3685-3693, 3703, 3711-3712, 3725-3744, 3747-3761, 3764-3782, 3785-3882, 3885-3888, 3891-3896, 3899-3902, 3914-3916, 3919-3922, 3927 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageChops.py 77 55 29% 27, 36, 48-49, 62-64, 77-79, 92-94, 109-111, 123-125, 135-137, 147-149, 159-161, 174-176, 189-191, 202-204, 215-217, 233-235, 248-250, 263-265, 275, 285, 300-303 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageColor.py 55 50 9% 35-120, 136-150 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageDraw.py 542 471 13% 64, 69-73, 75, 83, 88, 111-116, 120-123, 126-130, 133, 135, 141-143, 147-152, 156-160, 164-168, 172-228, 232-237, 241-245, 249-251, 259-281, 287-288, 292-296, 302-423, 426-428, 431-433, 437-439, 467-564, 582-646, 659-677, 695-712, 724-749, 766-791, 807-890, 913-914, 928-938, 960-994, 1038-1115, 1124-1127 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageEnhance.py 30 19 37% 36, 49-54, 66-71, 83-87, 99-103 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageFile.py 375 213 43% 65-72, 105-107, 118-125, 128-134, 137-140, 143-144, 151-153, 159-160, 178-179, 183, 189-214, 243-244, 250-256, 259-266, 288, 298, 313-325, 337-338, 341-350, 354-355, 377, 388-454, 457, 460, 472-490, 507, 527, 532-533, 546-547, 566, 570-571, 573-584, 589-592, 595, 600-604, 613, 621, 630, 643-667, 682, 693, 705-716, 731, 742, 752-757, 768-773 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageFilter.py 198 116 41% 30-33, 57-63, 80-81, 84-88, 102-103, 117-118, 132-133, 148, 151, 165, 168, 187-188, 213-215, 218, 363-420, 424-436, 451-466, 493-520, 529-536, 539-541 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageFont.py 280 227 19% 47-54, 59-62, 88-110, 114-135, 151-152, 172, 188-189, 198-199, 213-252, 255, 258-259, 262-263, 270, 278, 349, 408-413, 477-481, 541-553, 571-572, 652, 752-778, 792-797, 810-815, 822-834, 841-848, 855-859, 876-877, 887-893, 896-899, 904-909, 912-915, 927-929, 992-1039, 1051-1060, 1070-1202 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageMode.py 21 1 95% 33 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageOps.py 256 230 10% 31-38, 42-46, 50-60, 89-155, 183-237, 254-266, 291-304, 318-319, 335-342, 356, 372-389, 401-415, 453-501, 521, 531-534, 555-559, 570-576, 590-621 /home/admin/.local/lib/python3.8/site-packages/PIL/ImagePalette.py 162 135 17% 38-46, 50, 54-55, 59-67, 71, 74-82, 91-93, 100-106, 116-167, 174-189, 197-201, 209-215, 219-222, 226-228, 232-237, 241-242, 246-247, 253-272 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageSequence.py 31 25 19% 32-36, 39-43, 46, 49-54, 66-76 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageStat.py 89 73 18% 31-41, 45-50, 55-67, 72-75, 80-86, 91-97, 102-105, 110-120, 125-128, 133-137, 142-145 /home/admin/.local/lib/python3.8/site-packages/PIL/JpegImagePlugin.py 438 250 43% 82-83, 86, 94-156, 169, 172, 185-190, 207-208, 212, 215-219, 222, 226-235, 258-259, 262, 354-355, 377-378, 387, 392-399, 412-413, 418-451, 456-477, 480, 493-498, 502-504, 518-580, 616-617, 628-631, 636-637, 641-643, 654-656, 658-661, 663-664, 667, 669, 672, 674, 676, 680, 682-685, 690-722, 725-728, 736-753, 766, 768-769, 795, 798, 811-816, 827-830, 834-835 /home/admin/.local/lib/python3.8/site-packages/PIL/JpegPresets.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/PaletteFile.py 24 19 21% 25-48, 51 /home/admin/.local/lib/python3.8/site-packages/PIL/PngImagePlugin.py 871 574 34% 137-140, 146-151, 172-173, 181-183, 188, 191, 197, 219-223, 234-248, 267-270, 294, 308-323, 335, 341-342, 348, 378-382, 385, 392-394, 398-421, 427-430, 434-435, 437, 439-440, 446, 449, 457, 461-464, 468-484, 488-490, 496-499, 508-515, 521-524, 530-531, 539-542, 555-585, 589-625, 628-630, 634-651, 654-680, 683-695, 716-717, 737-741, 762-763, 766, 771-778, 784-794, 799-811, 814-826, 829-919, 922, 928, 944-945, 948-952, 958, 969, 975-976, 982-984, 989, 992, 994-998, 1004-1015, 1018-1022, 1025-1028, 1037, 1092-1094, 1097-1098, 1102-1223, 1227, 1234-1250, 1257-1274, 1287-1289, 1317-1323, 1331-1332, 1335-1339, 1342-1346, 1351-1371, 1374-1376, 1393-1394, 1398-1402, 1405, 1414-1416, 1431-1453 /home/admin/.local/lib/python3.8/site-packages/PIL/PpmImagePlugin.py 219 188 14% 46, 58-65, 68-91, 94-141, 152, 155-157, 160-191, 198-216, 219-264, 267-276, 283-302, 310-329 /home/admin/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py 1123 782 30% 277-279, 283-293, 324-327, 330-331, 337, 341, 358-362, 365, 368, 371-376, 379, 382-386, 390, 430, 506-507, 514-515, 532-533, 544, 552, 558, 568, 584-610, 613, 641, 644-655, 663, 669, 724, 728-732, 736-738, 743-747, 760, 766, 770-772, 776-781, 785, 793-797, 823-825, 838-844, 847-848, 863-865, 869-942, 945-952, 985-986, 1007-1011, 1024-1028, 1031, 1034, 1037, 1040-1041, 1044-1053, 1070-1076, 1082-1103, 1107-1113, 1117-1124, 1127-1162, 1166, 1175, 1185-1196, 1199-1201, 1204-1232, 1238-1325, 1330-1551, 1587-1877, 1906-1917, 1921-1942, 1945-1967, 1971-1972, 1975, 1978-1980, 1983, 1986-1990, 1993-2000, 2003-2006, 2010-2017, 2020, 2023-2024, 2027-2028, 2031-2035, 2038-2042, 2045-2049, 2052-2055, 2058-2061, 2064-2065, 2068-2100, 2103-2125, 2129-2152 /home/admin/.local/lib/python3.8/site-packages/PIL/TiffTags.py 46 1 98% 50 /home/admin/.local/lib/python3.8/site-packages/PIL/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/_binary.py 28 9 68% 22, 26, 37, 47, 57, 77, 90, 94, 98 /home/admin/.local/lib/python3.8/site-packages/PIL/_deprecate.py 25 12 52% 44, 46-47, 50-54, 57-58, 62-65 /home/admin/.local/lib/python3.8/site-packages/PIL/_util.py 11 2 82% 11, 19 /home/admin/.local/lib/python3.8/site-packages/PIL/_version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/apiclient/__init__.py 10 2 80% 7-10 /home/admin/.local/lib/python3.8/site-packages/av/__init__.py 21 1 95% 12 /home/admin/.local/lib/python3.8/site-packages/av/about.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/av/audio/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/codec/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/container/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/av/deprecation.py 36 17 53% 33-34, 37-42, 45-55, 58-68, 77-81 /home/admin/.local/lib/python3.8/site-packages/av/filter/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/sidedata/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/av/video/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/boto3/__init__.py 31 17 45% 35, 61-70, 80-84, 93, 102, 109 /home/admin/.local/lib/python3.8/site-packages/boto3/compat.py 32 17 47% 29, 34, 38-48, 58, 67-86 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/__init__.py 11 8 27% 30-39 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/action.py 44 30 32% 28-64, 85-97, 129-141 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/attr.py 20 13 35% 24-30, 39-44, 48-52 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/base.py 15 10 33% 18-26, 31 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/client.py 10 7 30% 18-27 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/collection.py 56 43 23% 25-38, 41-60, 81-85, 114-130, 163-235 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/docstring.py 41 10 76% 29, 34, 39, 44, 49, 54, 59, 64, 69, 74 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/method.py 24 21 12% 23-63, 67-71 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/resource.py 159 123 23% 32-33, 36-46, 49, 52-73, 76-78, 83-98, 101-108, 111-121, 124-139, 146-170, 180-195, 201-206, 209-214, 217-222, 225-233, 239, 242, 245-247, 251-259 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/service.py 66 48 27% 31-42, 58-72, 75-81, 84, 89-113, 118, 123-131 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/subresource.py 50 40 20% 25-42, 65-112 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/utils.py 79 65 18% 26-38, 42-45, 49-57, 61-62, 66, 70, 76-89, 95-98, 102-109, 113-142 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/waiter.py 32 22 31% 24-25, 28-41, 54-91 /home/admin/.local/lib/python3.8/site-packages/boto3/exceptions.py 37 12 68% 42-49, 56-67, 72-73, 87-92, 101-105 /home/admin/.local/lib/python3.8/site-packages/boto3/resources/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/boto3/resources/action.py 70 51 27% 47-60, 72-87, 120-159, 177-178, 188-204, 228-231, 234-244 /home/admin/.local/lib/python3.8/site-packages/boto3/resources/base.py 57 43 25% 29-42, 45, 50-53, 59-61, 91-118, 122-126, 133-142, 145-148 /home/admin/.local/lib/python3.8/site-packages/boto3/resources/collection.py 133 94 29% 47-52, 55, 80-91, 110-114, 136-181, 202, 226, 244, 257, 307-312, 320, 336, 341, 345, 349, 353, 357, 391-426, 434-436, 450-503, 512-526 /home/admin/.local/lib/python3.8/site-packages/boto3/resources/factory.py 153 124 19% 39-40, 65-139, 147-149, 159-166, 178-203, 212-213, 231-250, 254-268, 277-278, 288-304, 311-324, 336-357, 365-380, 387-400, 410-439, 448-483, 494-539 /home/admin/.local/lib/python3.8/site-packages/boto3/resources/model.py 206 154 25% 43-44, 59-73, 86, 95-100, 119-131, 142-145, 160-166, 179-186, 195-201, 210, 234, 252-259, 299-335, 354-368, 386-389, 406-418, 427-436, 445-450, 459-465, 474-480, 494-543, 554-574, 583, 592, 601-607, 616-622 /home/admin/.local/lib/python3.8/site-packages/boto3/resources/params.py 55 47 15% 41-48, 72-98, 117-168 /home/admin/.local/lib/python3.8/site-packages/boto3/resources/response.py 92 79 14% 26-29, 51-76, 95-125, 140, 152-155, 185-189, 200-265, 284-300 /home/admin/.local/lib/python3.8/site-packages/boto3/session.py 90 61 32% 51-81, 84, 93, 100, 107, 114, 120-121, 132, 142, 150, 171, 183, 258, 339-409, 414-449 /home/admin/.local/lib/python3.8/site-packages/boto3/utils.py 27 16 41% 52-53, 57-65, 69-74, 87-89, 92 /home/admin/.local/lib/python3.8/site-packages/botocore/__init__.py 34 15 56% 24, 68, 71, 84-98 /home/admin/.local/lib/python3.8/site-packages/botocore/args.py 167 134 20% 61-66, 71-113, 129-176, 188-204, 208-220, 224-238, 241-245, 251-252, 261-265, 268-274, 277-279, 285-293, 296-298, 302, 308-314, 317-318, 330-350, 353-364, 367-370 /home/admin/.local/lib/python3.8/site-packages/botocore/auth.py 475 382 20% 57-66, 74-79, 86, 95, 98-123, 131-147, 152, 155-173, 183-188, 191-195, 202-211, 218-221, 225-236, 239-252, 261-267, 275, 278-280, 283-303, 307-313, 316-328, 331-332, 335-340, 343-348, 356-360, 363-369, 372-387, 390-395, 398-409, 415-426, 431-435, 441-469, 473, 481-483, 488-544, 550, 566, 573, 584-619, 638, 641-644, 647-661, 664-675, 681-684, 695-709, 713-719, 723-731, 734-742, 745, 748-756, 773-774, 777, 780-807, 819-845, 860-861 /home/admin/.local/lib/python3.8/site-packages/botocore/awsrequest.py 292 216 26% 40-41, 44-49, 65-76, 79-83, 86-95, 101-108, 111-144, 154-156, 162-193, 196-197, 200-204, 207-209, 248-267, 283-287, 291-315, 339-345, 348-352, 355-373, 376-381, 385-393, 397-421, 443-467, 471, 475-478, 494-498, 501-505, 520-528, 545-550, 556-563, 573-577, 582-583, 586, 589, 592, 595, 601-602, 605, 608, 611, 614, 617, 620, 623 /home/admin/.local/lib/python3.8/site-packages/botocore/client.py 443 348 21% 58-70, 77-99, 102-103, 106-117, 120-123, 126-133, 136-140, 143, 146-168, 174-181, 184-188, 191-213, 218-227, 230-232, 236-247, 254-257, 270-298, 315-317, 322-326, 331-336, 341-345, 349-372, 392-396, 400-416, 420-424, 428-444, 451-468, 472-482, 486-498, 507, 518-522, 526-533, 536-553, 556-573, 590-602, 605-614, 621-622, 629, 632-678, 681-690, 694-703, 708-728, 749-785, 802-812, 815-824, 836-846, 852-858, 862-864, 867, 886-891, 895, 899, 903, 907, 911, 915, 927-943 /home/admin/.local/lib/python3.8/site-packages/botocore/compat.py 183 114 38% 68, 73, 77, 80-144, 152-154, 170-173, 178-181, 191-192, 204, 212-213, 228-231, 235-241, 264-335, 342-346, 351-352, 358-359 /home/admin/.local/lib/python3.8/site-packages/botocore/config.py 51 37 27% 192-206, 209-239, 242-245, 249-266, 284-290 /home/admin/.local/lib/python3.8/site-packages/botocore/configloader.py 87 74 15% 72-84, 88-93, 106-107, 140-168, 172-179, 189-199, 253-272 /home/admin/.local/lib/python3.8/site-packages/botocore/configprovider.py 154 106 31% 141-152, 156-165, 186-189, 231-248, 254-261, 264-274, 288-292, 308-313, 339, 348, 365, 376, 397-400, 409-413, 416-418, 421, 436-437, 441-443, 446, 466-467, 471-477, 480, 497-498, 502-504, 507, 517-523, 526-538, 541, 553, 557, 560 /home/admin/.local/lib/python3.8/site-packages/botocore/credentials.py 968 718 26% 63-137, 152-155, 158, 169, 175-176, 182-183, 189, 199, 209-210, 214, 218-220, 224-228, 232-239, 243-254, 259-273, 287-290, 293, 296-297, 301-306, 309-320, 323-324, 340-348, 357-358, 361, 387-397, 400-401, 405-413, 421-422, 426, 434-435, 439, 447-448, 452, 455-456, 478-490, 494, 500-523, 528-556, 560, 563-582, 618-619, 628-636, 639-641, 650-656, 659, 663-664, 667, 670, 678-687, 695-703, 706, 710-712, 718-732, 737-739, 746-761, 799-804, 811-813, 817-831, 835-836, 876-878, 885-890, 894-898, 914, 933, 936-943, 951-954, 957-969, 979-1002, 1009-1013, 1021, 1024-1041, 1065-1068, 1072-1090, 1097-1118, 1121-1157, 1169-1174, 1180-1191, 1206-1212, 1215-1227, 1231-1233, 1257-1261, 1268-1283, 1286-1288, 1301-1306, 1312-1329, 1384-1401, 1404-1408, 1411, 1421-1458, 1466-1512, 1515-1522, 1528, 1535-1566, 1572-1573, 1576-1584, 1587-1614, 1617-1624, 1629-1639, 1660-1668, 1671, 1674-1678, 1681-1686, 1689-1692, 1695-1725, 1733, 1746, 1757-1760, 1772-1798, 1806-1810, 1814-1816, 1827-1832, 1837-1838, 1841-1848, 1858-1861, 1866-1882, 1885, 1895, 1911-1915, 1931-1932, 1941-1947, 1959, 1962-1965, 1973-1985, 1994-2000, 2009-2021, 2025-2027, 2031-2057, 2075-2083, 2086-2111, 2114-2128 /home/admin/.local/lib/python3.8/site-packages/botocore/discovery.py 183 139 24% 40-42, 47, 51-52, 56-60, 63-67, 70-75, 78, 83-90, 95-107, 110-115, 118-121, 124-125, 128, 131-133, 138-142, 145-148, 151-160, 163-168, 171, 174-214, 219, 222-228, 231-236, 239-251, 254-274 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/__init__.py 11 8 27% 28-38 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/docstringparser.py 119 83 30% 24-26, 29-30, 34-36, 39-42, 45, 48, 51, 61-64, 67-79, 82-87, 90, 93, 98, 101, 106-107, 110-111, 114, 117-118, 126-128, 131-133, 136-138, 141-143, 148, 151-152, 160-170, 178-181, 184, 187-200 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/restdoc.py 112 74 34% 25-33, 36-37, 43, 49, 56, 62, 68, 74-78, 81, 84-85, 88-97, 101-103, 120-128, 133, 141, 145, 149, 153, 156-157, 175-183, 187, 191, 201-209, 212, 215, 218 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/style.py 288 206 28% 22-25, 29, 33, 36, 39, 42-43, 46, 49, 52, 55, 58, 61, 64, 70-73, 76, 79, 82, 91-101, 104, 107, 110-111, 114-115, 118-121, 124-126, 129-132, 135, 138, 141, 144, 147, 150-153, 156-157, 160-161, 164-165, 168-169, 172-175, 178-181, 184-185, 188-191, 194-195, 198-201, 204-205, 208-219, 222, 225-230, 233-254, 257-258, 261-262, 265-267, 270-271, 274-277, 280-283, 286-289, 293-296, 299-302, 305, 308, 311, 314, 317-319, 322-323, 332-334, 337-342, 345-351, 354-357, 360-361, 364-368, 371-374, 377-378, 381-387, 390-391, 394-397, 400-401, 404-406, 409-412, 415-418 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/client.py 170 130 24% 28-32, 39-43, 46, 49-67, 71, 75-81, 84-86, 90-94, 97, 100, 103-110, 113-132, 167-168, 171-174, 177, 180-192, 195-196, 199-210, 213-214, 217-223, 226-229, 232-246, 249-254, 257-264, 267-276, 281-290 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/docstring.py 38 19 50% 33-36, 40, 43, 58-60, 63, 70-72, 75-81, 86, 91, 96 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/example.py 127 107 16% 36-41, 46, 50-56, 60-66, 70-81, 85-110, 114-125, 128-132, 135-139, 142-146, 152-158, 166-169, 177-208 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/method.py 97 86 11% 33-39, 60-78, 99-105, 117-123, 169-281 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/paginator.py 49 39 20% 22-24, 31-43, 46-65, 94-167 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/params.py 132 108 18% 33-34, 39, 43, 47-56, 60-77, 82-95, 98, 101-105, 108-117, 120-121, 124-125, 134-145, 149, 159-177, 182-212, 215-216, 219-220 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/service.py 53 36 32% 24-32, 46-55, 58-59, 66, 69-75, 78, 81-88, 91-96, 99-102 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/shape.py 39 31 21% 27-32, 61-89, 92-98, 101-107, 110-117 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/sharedexample.py 143 123 14% 33-38, 41-55, 58-72, 88-97, 101-125, 128-147, 150-164, 169-170, 173, 176-180, 183-187, 190-192, 195-198, 213-218 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/utils.py 75 33 56% 27, 50, 69-77, 117-128, 160-166, 176-180, 197 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/waiter.py 35 25 29% 22-24, 31-38, 41-56, 83-119 /home/admin/.local/lib/python3.8/site-packages/botocore/endpoint.py 153 117 24% 54-70, 85-94, 97, 100-102, 105-118, 122-124, 127-128, 131-158, 166-183, 186-230, 236-245, 249-266, 269, 274, 285-303, 314, 323-327 /home/admin/.local/lib/python3.8/site-packages/botocore/errorfactory.py 32 21 34% 28, 44, 47-51, 58, 70-74, 77-88 /home/admin/.local/lib/python3.8/site-packages/botocore/eventstream.py 246 149 39% 33-34, 40-43, 49-52, 58-61, 71-72, 110, 123, 135-136, 148-149, 161-162, 175-176, 189-190, 203-204, 225-229, 251-253, 265, 279, 285-287, 293-295, 306, 318, 328, 334-337, 340-343, 384, 396-397, 400-406, 409-411, 414-416, 419-421, 424-428, 431, 442-444, 452, 455-459, 462-468, 471-472, 475-477, 480-483, 487-488, 491-494, 497-502, 506-507, 515-524, 527, 530, 577-581, 584-587, 590-594, 597-602, 605-612, 616 /home/admin/.local/lib/python3.8/site-packages/botocore/exceptions.py 200 32 84% 24-28, 40-42, 45, 82-84, 87, 369-370, 410-420, 423-430, 436, 620-622 /home/admin/.local/lib/python3.8/site-packages/botocore/handlers.py 434 313 28% 80, 90-106, 125-134, 138-152, 163-185, 189-198, 202-205, 210-214, 218-224, 228-236, 246, 256, 260-272, 276, 285, 289-297, 305-312, 316-330, 365-374, 378-392, 396-401, 414-420, 427-429, 434-443, 450-465, 475-477, 483-496, 505-514, 518-522, 544-562, 573-584, 588-593, 597-598, 603-605, 620-637, 658-668, 672, 676-690, 696-699, 703-707, 719, 732, 745, 763-773, 777-781, 822-835, 838-855, 858-862, 875, 885, 894-900, 903-905, 911-923, 927-929, 934-935 /home/admin/.local/lib/python3.8/site-packages/botocore/history.py 27 10 63% 22, 31, 34, 37, 40-47 /home/admin/.local/lib/python3.8/site-packages/botocore/hooks.py 249 196 21% 31-35, 60-63, 82, 99, 112, 124, 130-132, 143, 146-147, 158-163, 171-175, 195-215, 228, 243-247, 251, 256, 261, 266-302, 306-336, 339-344, 349-352, 355-356, 359-360, 364-365, 371-372, 378-379, 385-386, 391-420, 423-427, 430, 464, 472-483, 493-497, 500-528, 538-540, 543-564, 572-575, 581-589 /home/admin/.local/lib/python3.8/site-packages/botocore/httpsession.py 210 158 25% 39-41, 45-51, 61-88, 99-105, 109-113, 117-122, 126, 129-134, 137-139, 142-146, 169-195, 199-206, 209-219, 222, 225-234, 237-243, 246-251, 254-274, 277-281, 284-301, 304, 307-359 /home/admin/.local/lib/python3.8/site-packages/botocore/loaders.py 139 98 29% 127-134, 154, 166-175, 192-199, 222-238, 242, 246, 267-287, 310, 327-340, 374-389, 393-400, 419-424, 430-441, 455-456, 460-461 /home/admin/.local/lib/python3.8/site-packages/botocore/model.py 398 242 39% 42, 81-93, 118-126, 145-150, 161, 164, 167, 172, 178-188, 192-195, 199-206, 212, 218, 222, 228, 259-266, 269, 273, 277-281, 284, 288, 292-297, 301-305, 309, 313, 327-330, 334-337, 348-351, 355, 359, 363, 367-370, 374-379, 382-385, 394-397, 401, 404, 437-444, 448-451, 465, 469, 473, 477, 483, 487, 491-495, 500-505, 510-514, 520, 524-525, 529, 533, 537, 541, 544, 547, 551-556, 560, 564, 567, 570, 574-581, 584, 599-600, 603-616, 624-636, 643, 647, 688-691, 702-703, 712-719, 724-734, 737-745, 748-752, 755-762, 765-773, 776, 779-782, 793, 821-823 /home/admin/.local/lib/python3.8/site-packages/botocore/monitoring.py 221 149 33% 42-43, 47-48, 56-61, 73, 89, 92, 97, 104-110, 114-124, 127-129, 136-138, 141, 145-147, 150, 153, 171-173, 176, 179-181, 214-220, 229-235, 287-296, 342-343, 346-347, 362-370, 374, 380, 383, 386, 389-391, 394-408, 412-415, 419, 422, 426-433, 438, 442-444, 448-451, 456-460, 464-467, 470-472, 475-481, 484, 490, 493, 496-500, 503-508, 531-533, 542-550 /home/admin/.local/lib/python3.8/site-packages/botocore/paginate.py 369 304 18% 52-68, 72-79, 83-90, 94-101, 105, 127-136, 140-144, 158-161, 172-173, 178, 181-186, 193-207, 211, 216, 220-232, 236, 239-303, 321-329, 332, 335, 338-344, 349-357, 360-364, 370-397, 401-424, 427-440, 443-444, 448-490, 493-506, 513-534, 541-550, 557-566, 570, 573-576, 579-585, 588-591, 594-596, 599-604, 607, 617-618, 629-646, 668-669, 672-677 /home/admin/.local/lib/python3.8/site-packages/botocore/parsers.py 528 407 23% 135, 147, 150-151, 155, 165-174, 199-207, 214, 233-262, 265, 279-284, 289-291, 299, 302, 306, 310-312, 317-321, 324, 327-329, 334-336, 339-357, 360, 368-370, 373-399, 402-406, 413-421, 427-444, 447-458, 461-467, 471-474, 478, 482, 486, 490, 494, 504-517, 520, 523, 526-538, 541-542, 545-551, 557-560, 575-580, 583-588, 594-609, 612-619, 622, 625, 628-652, 655-656, 660-669, 675-685, 688-706, 709-726, 730-740, 747, 753, 759-761, 770-778, 781, 784-793, 799-800, 806-810, 813-818, 821-823, 826-836, 839-860, 864-879, 885-893, 900, 903-907, 915, 918-920, 925-933, 942-944, 960-970, 973, 986-1006, 1010-1011 /home/admin/.local/lib/python3.8/site-packages/botocore/regions.py 90 69 23% 58, 65, 85, 94-96, 99-102, 106-116, 119-136, 141-165, 169-173, 176-190, 193-195, 199 /home/admin/.local/lib/python3.8/site-packages/botocore/response.py 67 45 33% 44-46, 60-69, 76-87, 92, 97-100, 110-117, 124-127, 133-135, 141, 145-162 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/retries/adaptive.py 70 50 29% 14-35, 44-50, 53-54, 58-77, 90-96, 99-113, 117 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/base.py 6 2 67% 9, 27 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/bucket.py 70 48 31% 10, 13, 16, 24-32, 36, 40-56, 60, 64, 76-77, 80-100, 103, 106-114 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/quota.py 24 16 33% 12-16, 28-32, 46-53, 57 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/special.py 28 17 39% 23-27, 36-48 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/standard.py 200 132 34% 40-61, 71-73, 77-93, 108-129, 136-138, 172-189, 199-204, 215, 218, 223-224, 227, 230, 239-241, 257, 265, 268-272, 290-298, 301-310, 334-336, 341, 348, 351-354, 376-391, 396-398, 402-406, 421-422, 433, 439, 442, 458-460, 463-475, 478, 490-498 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/throttling.py 25 15 40% 13-17, 20-21, 24-28, 35-38, 50 /home/admin/.local/lib/python3.8/site-packages/botocore/retryhandler.py 158 118 25% 52-58, 68, 73-77, 85-87, 93-118, 124-128, 132-144, 148-155, 173-174, 183-187, 219-225, 228, 231, 245-247, 250-263, 266-277, 282, 285-291, 296-297, 300-307, 312, 315-320, 326, 329-340, 359 /home/admin/.local/lib/python3.8/site-packages/botocore/serialize.py 357 275 23% 65-69, 117, 122-130, 135-139, 142, 145-147, 150-157, 162, 168-170, 174-186, 190-192, 200-222, 232-234, 237-243, 246-262, 265-278, 282, 285, 289-292, 295, 298, 314-322, 325-328, 335-357, 360-362, 365-379, 382-385, 388-397, 400, 403, 407, 428-476, 485-493, 502-519, 523-525, 533-569, 572-574, 577, 580-591, 597-599, 606-610, 613-615, 618-641, 644-652, 663-671, 677-682, 685-686, 689-690, 694-695 /home/admin/.local/lib/python3.8/site-packages/botocore/session.py 384 276 28% 102-131, 134-141, 144, 147, 151, 156, 161-165, 169, 173, 177-186, 189-192, 196, 200-215, 218-226, 230, 236-238, 242-245, 248-251, 261-282, 308-313, 316, 340-353, 365-388, 398, 408, 427, 440-443, 468-480, 489, 505-506, 509-512, 515-518, 524-534, 540, 548, 573-588, 602-616, 652, 685, 690, 693-694, 697-707, 713, 719, 722, 725, 800-855, 860-877, 880-884, 892-893, 915-925, 931-932, 935-945, 948-952, 955-959, 964-965, 968, 971-972, 975, 978, 981, 992-995, 1017-1018, 1032-1038, 1051 /home/admin/.local/lib/python3.8/site-packages/botocore/signers.py 216 174 19% 64-71, 75, 79, 83, 90, 120-162, 174-199, 221-242, 271-276, 315-316, 333-351, 354-355, 385-396, 401, 406, 427-457, 462, 506-533, 537, 561-598, 604, 671-719, 725-734 /home/admin/.local/lib/python3.8/site-packages/botocore/translate.py 21 16 24% 21-38, 42-55, 70-76 /home/admin/.local/lib/python3.8/site-packages/botocore/utils.py 1126 888 21% 181-184, 196, 207-214, 218-220, 226-229, 236-256, 262-267, 276-300, 310, 322-331, 337, 340-364, 367-395, 413-432, 435-436, 439-441, 444, 450-453, 456-459, 462-472, 482-513, 516, 523-528, 531-536, 539, 545, 552-558, 569-587, 592-595, 599-604, 611-619, 642-655, 671-676, 681-695, 710-716, 747-762, 775-783, 804-810, 827-843, 855-860, 875-880, 910, 922-923, 926-949, 952-958, 963-966, 971-974, 980-981, 993-1004, 1007, 1011-1016, 1030-1039, 1053-1060, 1077-1124, 1128, 1150-1159, 1170-1179, 1184-1187, 1191-1193, 1197-1212, 1222-1230, 1238, 1243-1250, 1253-1256, 1266-1343, 1358-1376, 1379-1381, 1388-1395, 1398, 1407-1413, 1435-1437, 1440, 1443-1451, 1454-1462, 1465-1470, 1480-1481, 1490-1499, 1515-1523, 1526-1527, 1533-1553, 1556-1567, 1570, 1573-1600, 1608-1615, 1618-1619, 1622-1633, 1636-1662, 1669-1671, 1674-1679, 1682-1688, 1691-1697, 1705-1732, 1738-1744, 1750-1766, 1777-1785, 1788, 1791-1802, 1805, 1808, 1811-1835, 1838-1839, 1847-1853, 1856-1858, 1861-1862, 1865-1876, 1879-1883, 1886, 1889-1891, 1894-1906, 1909-1918, 1921, 1924-1925, 1928-1933, 1936-1942, 1945-1951, 1954-1957, 1960, 1967-1969, 1972, 1978-1985, 1988-1997, 2000, 2003-2013, 2016-2018, 2022-2031, 2037-2043, 2046-2051, 2054-2063, 2069-2075, 2078-2083, 2095-2100, 2111-2112, 2115-2119, 2125-2127, 2138-2139, 2142-2156, 2159-2180, 2183, 2187-2190, 2205-2211, 2221-2232, 2236-2240, 2244-2245, 2249-2254, 2259-2263, 2268-2269, 2272-2273, 2278-2280, 2283, 2286-2296 /home/admin/.local/lib/python3.8/site-packages/botocore/validate.py 164 114 30% 46-49, 55-56, 59-64, 71-84, 89, 92-94, 97-100, 103-128, 132-137, 140, 160-162, 165-166, 169-173, 179-182, 187-202, 215, 219-222, 226-231, 235, 238-244, 250, 254, 260, 266-269, 273-279, 284-285, 288-294 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/exceptions.py 26 6 77% 21-27 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/exceptions.py 67 15 78% 17-18, 22, 28-29, 33, 73-78, 85-87, 135-138 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/six.py 444 211 52% 49-72, 98-99, 112, 118-121, 131-133, 145, 154-157, 192-193, 203, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /home/admin/.local/lib/python3.8/site-packages/botocore/waiter.py 175 140 20% 44-73, 79-82, 87, 90-93, 113-121, 124-125, 132-136, 147-154, 158-162, 167-171, 175-186, 197-208, 212-219, 222-239, 242-259, 262-271, 274-284, 303-307, 310-367 /home/admin/.local/lib/python3.8/site-packages/cached_property.py 93 61 34% 14-15, 30-37, 40-47, 57-59, 62-74, 85-91, 94-95, 98-115, 118, 121, 124-128, 143-144, 147-148 /home/admin/.local/lib/python3.8/site-packages/cffi/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/cffi/api.py 544 341 37% 8-11, 52-59, 82, 97-98, 115-117, 121-123, 129-130, 133-135, 147, 160, 166, 169, 174, 190, 200, 203-211, 217-221, 227-229, 238-240, 284-291, 299, 318, 335, 361-365, 382, 392-403, 411-419, 431, 454-473, 476, 478, 483, 486-487, 495-508, 511-515, 526-538, 541, 544, 547, 556-577, 580-585, 589-635, 638-647, 652-658, 661-684, 687-695, 699-707, 720-725, 735-751, 754-777, 780, 788-801, 807-809, 815-816, 820-827, 842-847, 852-864, 867, 871, 879, 887, 889-895, 897, 903, 915-921, 923-925, 927-936, 939-940, 945, 947-948, 955-965 /home/admin/.local/lib/python3.8/site-packages/cffi/commontypes.py 37 10 73% 12-13, 31, 34-44, 56, 80 /home/admin/.local/lib/python3.8/site-packages/cffi/cparser.py 672 327 51% 12, 16-17, 23-24, 67-96, 116-142, 151-158, 161-163, 174-176, 182-186, 196, 201-203, 207, 234-242, 287-288, 337-338, 349-357, 360-367, 372-377, 379-380, 404, 415, 420, 426, 432-446, 449-454, 457-469, 473-479, 491, 493, 495, 507-549, 559, 564-568, 574, 583, 585, 594, 611, 621, 651, 667, 677-685, 692-699, 706, 718-720, 726, 734, 741, 777-778, 781-783, 788-795, 797-800, 812, 818, 821, 832-833, 837, 842, 844, 854-855, 859-860, 864-868, 879, 882-940, 944-947, 950-971, 974-981, 984-999, 1003-1005 /home/admin/.local/lib/python3.8/site-packages/cffi/error.py 19 8 58% 8-15 /home/admin/.local/lib/python3.8/site-packages/cffi/lock.py 10 6 40% 4-7, 11-12 /home/admin/.local/lib/python3.8/site-packages/cffi/model.py 389 160 59% 16, 21, 30-45, 51, 54, 66, 79, 99, 166, 168, 170, 172, 182-183, 186, 189, 196-197, 200, 215, 219, 232, 249-253, 258, 269, 288-290, 304, 314, 318, 359-362, 365-376, 382-394, 400, 405-408, 414, 422-425, 430-462, 467, 471, 495-499, 502-505, 508-509, 512-514, 520-557, 562, 569-572, 584-587, 598-599, 610, 613, 616-617 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/api.py 195 181 7% 62-497, 515, 543-544 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/assets/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/cd.py 189 164 13% 24-50, 63-71, 80-91, 100-112, 120-129, 138-164, 175-244, 253-283, 291-311, 319-338, 350-388 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/constant.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/legacy.py 19 14 26% 22-50 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/models.py 174 110 37% 20-34, 37-43, 49-62, 66, 70-72, 75, 78-86, 90, 97-103, 107, 111, 119, 127-147, 151, 155-157, 161, 165, 172, 176, 180, 184-192, 201, 208-212, 219, 229, 232, 239-246, 249, 252, 259-272, 278-280, 286, 308-318, 322, 337 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/utils.py 214 158 26% 24-28, 40-46, 54-60, 65-69, 74-78, 83-93, 98-108, 113-118, 123-128, 133, 137-139, 144-149, 154-159, 164-169, 174-179, 184-189, 194, 199, 212-237, 245, 266-276, 280, 284-296, 300-310, 314-334, 342, 353-358, 372-414 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/colorama/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/colorama/ansi.py 74 8 89% 16, 19, 22, 38, 40, 42, 44, 46 /home/admin/.local/lib/python3.8/site-packages/colorama/ansitowin32.py 131 100 24% 13, 25-26, 29, 35, 38, 41, 44-53, 57-61, 75-104, 114, 117-158, 161-167, 171-174, 183-190, 194-196, 200-202, 206-220, 224-242, 246-257 /home/admin/.local/lib/python3.8/site-packages/colorama/initialise.py 48 32 33% 19-20, 25-48, 52-55, 60-64, 68-71, 75-80 /home/admin/.local/lib/python3.8/site-packages/colorama/win32.py 78 68 13% 11, 17-152 /home/admin/.local/lib/python3.8/site-packages/colorama/winterm.py 119 90 24% 25-34, 37, 40-42, 45-47, 50-58, 61-69, 72-75, 78-83, 86-91, 94-101, 104-109, 115-141, 147-166, 169 /home/admin/.local/lib/python3.8/site-packages/cryptography/__about__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/__init__.py 7 1 86% 18 /home/admin/.local/lib/python3.8/site-packages/cryptography/exceptions.py 37 5 86% 11, 33-34, 61-62 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/_oid.py 122 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/aead.py 143 127 11% 10-19, 28-46, 50-62, 70-85, 97-137, 141-145, 149-158, 162-166, 170-175, 179-187, 199-245, 257-310 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/backend.py 1215 945 22% 189, 200, 219, 229-231, 234-244, 250-264, 269-276, 279-285, 294, 299, 304, 308, 323-327, 334-336, 339-342, 346-349, 357-368, 372, 458, 468-482, 506-514, 519-535, 542, 553-582, 592-602, 605-608, 611-614, 633-638, 644-649, 671-721, 729-780, 783-786, 798-814, 817-820, 823-844, 849-857, 862-863, 868-871, 876-892, 897-911, 916-927, 930-933, 936, 939-941, 944, 949, 957, 965-1004, 1007-1016, 1037, 1049, 1055-1056, 1059-1079, 1082-1097, 1100-1105, 1108-1111, 1114-1115, 1120-1165, 1170-1207, 1216-1229, 1237-1240, 1249-1259, 1267-1314, 1319-1326, 1331-1348, 1353-1390, 1393-1394, 1397-1399, 1404-1409, 1414-1417, 1424-1434, 1438-1445, 1459-1478, 1490-1600, 1605-1611, 1622-1625, 1635-1680, 1683, 1688-1709, 1712-1715, 1720-1729, 1734, 1741-1784, 1789-1813, 1818-1833, 1838-1857, 1860, 1868, 1871-1881, 1888, 1892-1900, 1903-1912, 1915-1916, 1919-1921, 1927-1929, 1934-1945, 1950-1961, 1964-1965, 1968-1970, 1976-1986, 1989-2000, 2003-2004, 2015-2039, 2042-2051, 2059-2060, 2073-2083, 2092-2093, 2102-2172, 2182-2331, 2334-2336, 2339-2343, 2346, 2351-2361, 2366-2374, 2377-2395, 2413, 2424-2425 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ciphers.py 124 57 54% 12, 32, 42-43, 53-60, 63, 67, 70-73, 90-105, 134, 151, 171-172, 184-245, 248-266, 269-277, 281 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/cmac.py 46 35 24% 16-17, 27-59, 62-63, 66-73, 76-82, 85-87 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/dh.py 173 143 17% 12, 16-30, 34-35, 40-41, 44-55, 62, 69-104, 108-111, 116-119, 123, 126-141, 154-187, 190-192, 198-212, 215, 223-241, 253-256, 260, 263-278, 288, 295-315 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/dsa.py 119 87 27% 17, 23-35, 46-52, 57-58, 61-68, 75, 82-91, 95, 98-110, 123-139, 142-147, 155, 169-170, 177-185, 189, 192-204, 214-218, 225, 235-236 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ec.py 145 111 23% 20, 26-27, 34-60, 70, 76-81, 87-90, 99-108, 117-122, 127-134, 138, 142, 147-162, 165-179, 182-184, 195, 209-214, 219-226, 230, 234, 237-255, 258-279, 286-302, 312-317 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ed25519.py 70 54 23% 17, 22-23, 30-44, 49-56, 59-77, 82-83, 86-94, 97-117, 125-143, 148-155 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ed448.py 72 54 25% 15, 23-24, 31-45, 50-57, 60-78, 83-84, 87-95, 98-118, 126-144, 149-156 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/hashes.py 45 12 73% 11, 29, 47-53, 65, 79-86 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/hmac.py 46 34 26% 15, 26-48, 52, 55-62, 67-69, 72-79, 82-84 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/poly1305.py 34 23 32% 15, 20-47, 50-54, 57-62, 65-67 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/rsa.py 257 136 47% 37, 46-59, 68-95, 107-162, 172, 180-201, 226-227, 233-234, 242-243, 250-261, 288-289, 306-322, 332-358, 383-384, 395-396, 436, 439-444, 447-451, 494, 520-532, 536, 539, 542-549, 559, 570-571, 581-586 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/utils.py 34 22 35% 11, 15-41, 53, 56 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/x448.py 53 37 30% 15, 22-23, 30-44, 49-56, 61-62, 65-73, 76-79, 87-105, 110-117 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/_conditional.py 74 34 54% 9, 25, 32, 38, 46, 52, 58, 65, 72, 79, 92, 98, 106, 118, 126, 132, 146, 155, 175, 181, 189, 199, 205, 211, 218, 227, 239, 247, 253, 257, 261, 265, 271, 275 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/binding.py 84 16 81% 25-28, 42, 88-99, 110-111, 161, 185, 200 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_asymmetric.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_cipheralgorithm.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_serialization.py 79 35 56% 37-42, 64-67, 83-87, 90-99, 109-116, 126-133, 141-144, 163-168 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/dh.py 112 47 58% 17-19, 24-39, 42-45, 50-54, 58, 62, 66, 71-80, 83-86, 92-96, 100, 104, 109-118, 121-124, 130-134, 138, 142 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/dsa.py 127 57 55% 128-139, 143, 147, 151, 154-158, 161-164, 167, 175-184, 188, 192, 195-199, 202-205, 211, 219-227, 231, 235, 238-242, 245-248, 256-258, 264-266, 270-278, 282-288 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ec.py 216 56 74% 171-184, 315, 321, 327-329, 337-348, 353-361, 364-368, 372, 376, 380, 383-386, 394, 397, 407-417, 422-426, 430, 434, 437-440, 446, 477-480 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ed25519.py 39 14 64% 18-26, 43, 57-65, 69-77, 101 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ed448.py 37 14 62% 15-23, 40, 54-61, 65-73, 103 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/padding.py 46 20 57% 44-57, 69-74, 85-88, 95-101 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/rsa.py 181 91 50% 129-132, 136-143, 156-190, 194-201, 208-214, 221, 229, 237, 254-288, 310, 316, 331, 335, 339, 343, 347, 351, 363-367, 372-375, 386, 402, 416-420, 423, 426-429, 432 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/types.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/utils.py 13 5 62% 15-19, 23 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/x25519.py 40 6 85% 19, 55, 63-71 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/x448.py 35 14 60% 15-23, 40, 48-55, 59-67, 91 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/algorithms.py 128 30 77% 19, 48, 58, 67, 71, 80-84, 88, 97, 101, 120, 124, 142, 146, 155, 159, 178, 182, 200-206, 210, 214, 223, 227 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/base.py 137 61 55% 18, 85, 100, 106, 110-111, 125, 131, 147-150, 174, 178-180, 183-187, 195-199, 202-207, 214-217, 220-223, 226-231, 234-247, 252-257, 263-268 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/modes.py 139 50 64% 72, 80-81, 92, 97, 103-109, 116-117, 121, 130-135, 139, 142-149, 165-166, 170, 179-180, 184, 193-194, 198, 232-250, 254, 258, 261-269 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/constant_time.py 5 1 80% 11 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/hashes.py 153 27 82% 79, 89, 97, 102-104, 108, 185-191, 195, 203-209, 213, 229-232, 236, 246-249, 253 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/kdf/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/kdf/scrypt.py 36 25 31% 33-56, 59-66, 71-73 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/base.py 21 10 52% 22-24, 32-34, 40-42, 62-64, 70-72 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/pkcs12.py 81 49 40% 44-49, 53, 57, 60-63, 69, 72, 84-110, 114, 118, 122, 125-128, 135, 138-141, 153-155, 163-165, 181-225 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/ssh.py 723 580 20% 39-49, 105-120, 125-130, 138, 143-144, 149-150, 160-165, 170-172, 177-179, 184-187, 192-195, 200-205, 216-218, 222, 226, 230, 234-239, 243, 247, 251-255, 259-261, 275-277, 283-286, 292-308, 314-316, 322-331, 347-351, 357-362, 368-378, 384-391, 397-398, 401-403, 426-432, 438-442, 448-454, 460-464, 470-474, 491-492, 498-502, 508-516, 522-525, 531-541, 556-560, 577-653, 662-730, 773-792, 796, 801, 805, 809, 813, 817, 821, 825, 829, 833, 836-839, 842, 849-873, 882-888, 895-983, 989, 993-1005, 1011-1025, 1030-1045, 1074-1083, 1088-1100, 1114-1121, 1135-1140, 1154-1159, 1175-1195, 1209-1217, 1233-1241, 1257-1265, 1281-1287, 1303-1309, 1323-1444 /home/admin/.local/lib/python3.8/site-packages/cryptography/utils.py 75 25 67% 29-30, 36-37, 41, 47-50, 80-84, 87, 108-119, 126, 129 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/__init__.py 69 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/base.py 449 219 51% 55-56, 64-66, 76-78, 87-92, 102-104, 108, 112, 115, 118-121, 128, 136, 141, 144-148, 158-159, 328-330, 334, 338, 342, 440, 444, 572, 576, 583, 590, 597, 604, 611, 626-628, 634-638, 648-654, 670-686, 701-703, 719-726, 732-736, 750-754, 771-791, 805-818, 834-849, 863-881, 897-903, 922-940, 955-959, 964-968, 979-992, 1003-1016, 1030-1035, 1049-1052, 1066-1075, 1085-1087, 1090-1103, 1110-1119, 1126-1131, 1138-1144, 1152 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/certificate_transparency.py 41 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/extensions.py 989 542 45% 52-70, 75, 78, 81, 88-89, 94-95, 105, 116, 121-125, 130-141, 148, 155-158, 161-164, 167, 170, 174, 177, 189-214, 225-226, 236, 243, 251-254, 262-266, 272, 278, 282, 285, 292, 298, 302, 306, 309, 312-315, 318, 321, 330-337, 342, 345-348, 351, 354, 363-370, 375, 378-381, 384, 387, 394-401, 404, 410-413, 419, 423, 427, 434-448, 452, 456, 459, 464-467, 470, 473, 480-483, 487, 490-493, 496, 499, 502, 511-520, 527, 530-533, 536, 539, 548-557, 564, 567-570, 573, 576, 587-636, 639, 646-649, 657-671, 675, 679, 683, 687, 746-768, 771, 778-781, 787, 793, 797, 800, 807-814, 819, 822-825, 828, 831, 842-857, 860, 866-869, 875-882, 886, 892, 901-909, 912, 918-921, 927, 931, 935, 944-949, 952, 958-961, 967, 971, 975, 982-988, 993, 996-999, 1002, 1005, 1012-1015, 1018, 1021, 1024, 1031-1034, 1037, 1040, 1043, 1050-1060, 1065, 1068-1071, 1074, 1077, 1098-1104, 1107, 1110-1113, 1116, 1120, 1123, 1141-1155, 1159, 1163, 1167, 1171, 1175, 1179, 1183, 1187-1192, 1196-1201, 1204-1214, 1225-1228, 1241, 1256, 1267-1302, 1305-1308, 1314-1315, 1318-1325, 1331-1334, 1340, 1346-1360, 1366, 1372, 1375, 1382-1392, 1396, 1400, 1404, 1407, 1413-1416, 1423, 1428-1435, 1448, 1455, 1462, 1468, 1474, 1497-1500, 1503, 1506-1509, 1512, 1519, 1532, 1539, 1546, 1552, 1558, 1578, 1581, 1584-1587, 1590, 1593, 1600, 1613, 1620, 1627, 1633, 1639, 1659, 1662, 1665-1668, 1671, 1674, 1681, 1694, 1701, 1708, 1714, 1720, 1740, 1743, 1746-1749, 1752, 1755, 1762-1765, 1768, 1771-1774, 1777, 1781, 1784, 1791-1794, 1797, 1802-1805, 1808, 1812, 1815, 1827-1836, 1843, 1848, 1851-1854, 1860, 1872-1881, 1888, 1891, 1894-1897, 1903, 1910-1913, 1916-1919, 1922, 1925, 1929, 1932, 1948-2019, 2022, 2034-2037, 2049, 2063, 2067, 2071, 2075, 2081, 2085, 2089, 2092, 2097-2100, 2104, 2108, 2111, 2117-2120, 2123, 2126 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/general_name.py 165 94 43% 37-55, 59, 63-65, 68, 71-74, 77, 82-94, 98, 102-104, 107, 110-113, 116, 121-133, 137, 143-145, 148, 151-154, 157, 162-165, 169, 172, 175-178, 181, 186-189, 193, 196, 199-202, 205, 210-225, 229, 232-237, 242, 245-248, 251, 256-262, 266, 270, 273, 278-281, 284 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/name.py 232 142 39% 64-87, 91-106, 118-164, 168, 172, 180, 191-197, 200-203, 206, 209, 214-225, 230, 241, 247-250, 253, 256, 259, 262, 268, 274, 282-293, 304, 319, 327, 331, 334, 337-340, 345, 348-350, 353, 356-357, 388-391, 394, 397-399, 402-404, 407-412, 423-429, 432-437, 440-460 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/oid.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/__init__.py 16 2 88% 18-19 /home/admin/.local/lib/python3.8/site-packages/cv2/data/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/__init__.py 13 4 69% 6-7, 17, 24 /home/admin/.local/lib/python3.8/site-packages/dateutil/_common.py 25 15 40% 14-17, 20-25, 28, 34, 37-41 /home/admin/.local/lib/python3.8/site-packages/dateutil/_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/easter.py 27 20 26% 52-89 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/__init__.py 33 4 88% 31-32, 47-48 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/_parser.py 812 517 36% 64, 68-69, 92, 108, 118, 122-123, 129-137, 144-145, 149-171, 175-179, 182, 197, 226-231, 238, 323-327, 330-334, 343-346, 349, 352, 355-358, 367-378, 383, 387-388, 390, 404, 408, 412, 415-426, 431-439, 444-446, 448-450, 452-454, 461-472, 485, 490, 493-505, 511, 514, 517, 522-563, 643, 646, 650-651, 657, 708, 735-736, 743-852, 863-864, 867, 870-871, 880-881, 893-897, 901-913, 917-927, 931-936, 940-949, 961-966, 970-976, 981-1002, 1013, 1019, 1026, 1033, 1042-1054, 1057, 1070-1090, 1093-1097, 1103-1109, 1116-1127, 1135-1139, 1147-1150, 1160-1175, 1179-1181, 1184-1192, 1195, 1198, 1204-1213, 1228-1233, 1238, 1243-1248, 1257-1264, 1366, 1383, 1386-1388, 1391-1579, 1586, 1598-1601, 1604-1605 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/isoparser.py 185 150 19% 26-37, 51-55, 134-146, 159-163, 176-179, 199, 207-210, 213-251, 254-295, 316-328, 331-381, 384-412 /home/admin/.local/lib/python3.8/site-packages/dateutil/relativedelta.py 241 206 15% 112-229, 232-262, 266, 270, 273-280, 294-308, 318-402, 405, 408, 411-413, 440, 458, 476, 496-501, 521-531, 548, 568, 571-576, 581-592, 597 /home/admin/.local/lib/python3.8/site-packages/dateutil/rrule.py 979 862 12% 26-27, 72, 87-89, 96-103, 106-111, 114-122, 125-147, 150-169, 172-180, 186-189, 195-210, 216-228, 249-269, 276-302, 434-698, 707-760, 765-774, 777-1030, 1062-1077, 1103-1109, 1119-1121, 1125-1251, 1254, 1257-1261, 1265-1276, 1279-1282, 1285-1292, 1295-1300, 1303, 1317-1323, 1326-1333, 1338, 1341, 1344, 1347, 1350-1354, 1360, 1366, 1374, 1381, 1384-1413, 1475, 1478, 1493, 1497-1504, 1507, 1513-1533, 1542-1561, 1566-1613, 1625-1725, 1732 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_common.py 161 124 23% 20-28, 55-129, 139-144, 169-177, 196-202, 205, 222-242, 258-264, 290, 293-300, 303-310, 314-317, 321-350, 366-372, 375-393, 396-405, 409, 414, 417 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_factories.py 49 21 57% 22, 34-52, 64-79 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/tz.py 803 557 31% 75, 78, 82, 98, 106, 109-112, 118, 121, 144-152, 155, 158, 162, 166, 180, 183-186, 191, 194, 206-216, 219-225, 228-234, 238, 254-255, 259-260, 287-300, 303-315, 320, 323, 337-342, 345-348, 359, 362-365, 368-370, 466-471, 475, 501, 538, 551, 585, 640-658, 674-704, 714-725, 729-736, 739-741, 763-777, 793-806, 809-819, 822-828, 831-844, 848-850, 853-855, 862, 865, 868, 871, 954-994, 1010-1018, 1021-1024, 1033, 1081-1109, 1112-1150, 1153, 1159-1164, 1169-1175, 1178-1220, 1223-1228, 1231-1234, 1237-1241, 1245, 1248, 1266-1279, 1285, 1305-1312, 1315-1328, 1331-1453, 1456, 1466-1467, 1473, 1570, 1575, 1580-1583, 1586-1588, 1595-1598, 1600-1616, 1620-1626, 1628-1631, 1636-1638, 1642-1673, 1702-1714, 1737-1760, 1799-1806, 1814, 1821-1828, 1834-1847 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/win.py 153 149 3% 14-370 /home/admin/.local/lib/python3.8/site-packages/dill/__info__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dill/__init__.py 34 16 53% 69-86, 111-112, 116-117 /home/admin/.local/lib/python3.8/site-packages/dill/_dill.py 1335 949 29% 91-93, 99, 103-104, 107-110, 113-121, 123, 125-133, 148-149, 152, 172-173, 186, 212, 214, 237-238, 246-251, 277-279, 287, 300-301, 315-318, 321-324, 350-363, 367-412, 417-418, 427-432, 435-439, 442-449, 463-464, 490-496, 515, 550-551, 565-566, 573, 585, 588, 591, 597-607, 631-632, 634, 636, 639-644, 648-650, 652, 671-760, 763-767, 770-774, 777-782, 785-790, 797-862, 865-868, 871-876, 880, 882-883, 887-888, 890-897, 912-913, 924-926, 929-933, 936-941, 944, 948-952, 955-958, 961-966, 969-996, 1000-1012, 1016, 1019-1032, 1036-1046, 1049-1062, 1066-1067, 1071-1114, 1129-1184, 1188, 1192-1214, 1218-1226, 1238-1241, 1244-1247, 1250-1252, 1274-1285, 1289-1302, 1306-1309, 1313-1319, 1323-1326, 1329-1352, 1361-1364, 1372-1375, 1382-1404, 1410-1423, 1427-1430, 1433-1448, 1452-1455, 1464-1477, 1481-1522, 1525-1531, 1535-1538, 1542-1545, 1551-1554, 1558-1575, 1579-1591, 1595-1600, 1606-1610, 1613-1620, 1632, 1654-1678, 1686-1707, 1710-1728, 1732-1851, 1856-1859, 1866-1878, 1882-1992, 2029-2036, 2041, 2051-2053, 2058-2060, 2063-2067, 2072-2074, 2091-2114, 2130-2155, 2160-2162, 2170-2171 /home/admin/.local/lib/python3.8/site-packages/dill/_shims.py 57 23 60% 89, 95, 97, 103-107, 133-138, 144-152, 176-177, 180 /home/admin/.local/lib/python3.8/site-packages/dill/detect.py 179 159 11% 24-30, 37-58, 62-71, 78-83, 94-114, 121-136, 141-159, 163, 169-215, 222-225, 230-235, 240-244, 249-253, 258-281 /home/admin/.local/lib/python3.8/site-packages/dill/logger.py 124 79 36% 123, 126, 129-135, 137-168, 182-184, 187-188, 192-209, 257, 263-266, 268-278, 280-285 /home/admin/.local/lib/python3.8/site-packages/dill/objtypes.py 9 1 89% 18 /home/admin/.local/lib/python3.8/site-packages/dill/pointers.py 60 50 17% 29-34, 44-51, 67-74, 84-115 /home/admin/.local/lib/python3.8/site-packages/dill/session.py 266 226 15% 40-55, 66-75, 78-117, 120-128, 221-262, 266-267, 273, 275, 277, 279, 281, 283-292, 296-304, 308-327, 431-507, 511-512, 571-603 /home/admin/.local/lib/python3.8/site-packages/dill/settings.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dill/source.py 613 574 6% 37-40, 45-49, 54-102, 115-258, 271-329, 345-346, 368-439, 444, 448-463, 468-473, 478, 483-508, 513-521, 525-529, 537-546, 552-560, 571-599, 604-624, 640-666, 679-713, 727-766, 791-827, 833-881, 891-921, 941-1001, 1006, 1009, 1011 /home/admin/.local/lib/python3.8/site-packages/dill/temp.py 94 79 16% 33-40, 44-45, 60-73, 107-118, 130-133, 159-165, 176-180, 190-195, 209-222, 237-246 /home/admin/.local/lib/python3.8/site-packages/easydict/__init__.py 30 12 60% 116, 118, 122, 129, 136-139, 142-143, 147-148 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/curve.py 20 2 90% 29, 32 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/ecdsa.py 35 24 31% 13-27, 31-41 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/math.py 71 53 25% 18, 40, 59-69, 79, 90-92, 107-116, 130-154, 168-191 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/point.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/publicKey.py 48 32 33% 11-12, 15-23, 26-32, 35, 39, 43-76, 80-97 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/signature.py 35 23 34% 10-12, 15-18, 21, 25-40, 44-45 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/base.py 8 2 75% 8, 12 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/binary.py 15 5 67% 15, 26, 36, 48-49 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/compatibility.py 24 13 46% 13, 19, 22-39 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/der.py 149 110 26% 27-28, 32-43, 47-53, 57, 61, 65, 69-74, 78-89, 93-111, 115-121, 125-131, 135-144, 148-152, 156-164, 168-180, 184-194, 198-207, 211-227, 231-232, 239 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/integer.py 5 1 80% 16 /home/admin/.local/lib/python3.8/site-packages/filetype/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/filetype/filetype.py 21 12 43% 28, 45-46, 63-64, 79-82, 95-98 /home/admin/.local/lib/python3.8/site-packages/filetype/helpers.py 23 12 48% 23-26, 41-44, 76, 92, 108, 124 /home/admin/.local/lib/python3.8/site-packages/filetype/match.py 27 6 78% 35, 69, 86, 103, 120, 137 /home/admin/.local/lib/python3.8/site-packages/filetype/types/__init__.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/filetype/types/application.py 9 1 89% 21 /home/admin/.local/lib/python3.8/site-packages/filetype/types/archive.py 198 28 86% 22, 55, 77, 99, 124, 144, 164, 187, 208, 227, 248, 270, 291, 312, 337, 365, 384, 407, 428, 466, 490, 511, 536, 557, 578, 598, 614, 630 /home/admin/.local/lib/python3.8/site-packages/filetype/types/audio.py 65 9 86% 22, 43, 69, 97, 118, 139, 164, 186, 204 /home/admin/.local/lib/python3.8/site-packages/filetype/types/base.py 16 5 69% 16, 20, 23, 26, 29 /home/admin/.local/lib/python3.8/site-packages/filetype/types/font.py 30 4 87% 22, 55, 88, 110 /home/admin/.local/lib/python3.8/site-packages/filetype/types/image.py 135 19 86% 82, 87, 125, 145, 172, 194, 216, 235, 255, 276, 297-305, 321 /home/admin/.local/lib/python3.8/site-packages/filetype/types/isobmff.py 20 12 40% 19-23, 26-33 /home/admin/.local/lib/python3.8/site-packages/filetype/types/video.py 86 23 73% 23-30, 47, 70-72, 89-91, 107-111, 128, 153, 180, 201, 222 /home/admin/.local/lib/python3.8/site-packages/filetype/utils.py 29 13 55% 6-7, 39-42, 68, 73-82 /home/admin/.local/lib/python3.8/site-packages/fontTools/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/cffLib/__init__.py 1891 1514 20% 62, 67-107, 110-112, 115-117, 120-125, 128-130, 133-135, 170-203, 206, 209, 212, 218-228, 239-292, 305-321, 325-389, 397-469, 472-500, 508-509, 512, 515-550, 554-562, 570-575, 578, 582-593, 596-615, 618-638, 643, 650-653, 656-659, 662-666, 669-673, 676-679, 688-691, 694-697, 700-717, 720, 728-732, 740-741, 749-753, 756, 767-794, 797, 800-811, 814, 817, 821, 824, 828, 878-884, 887-897, 909-922, 925-929, 932-935, 963-979, 982-991, 994-999, 1007-1012, 1015-1024, 1027-1035, 1040-1043, 1046-1054, 1057-1061, 1064, 1067-1074, 1077, 1080-1084, 1089-1136, 1139, 1142, 1145, 1148, 1178-1198, 1201, 1204-1207, 1210, 1215, 1218-1221, 1224-1228, 1231-1242, 1245-1262, 1265-1287, 1291, 1295-1296, 1300-1301, 1305, 1309, 1313, 1317, 1321, 1325, 1370-1377, 1380, 1383, 1386-1387, 1390, 1395, 1398, 1401-1402, 1405, 1410, 1413, 1416-1420, 1423, 1427-1431, 1435-1445, 1450-1462, 1465-1470, 1475-1489, 1492-1498, 1503-1507, 1510-1516, 1521, 1524-1536, 1539, 1544, 1547-1550, 1553, 1558-1584, 1587, 1590-1614, 1619-1662, 1665, 1671-1672, 1675, 1680-1688, 1691, 1694, 1697, 1702-1719, 1723, 1727, 1731-1740, 1744-1777, 1781-1790, 1794-1810, 1815-1822, 1825, 1828, 1831, 1836-1858, 1861-1865, 1868-1880, 1883-1893, 1897-1903, 1907-1917, 1921-1940, 1944-1974, 1979-1990, 1993, 1996-2002, 2007-2010, 2013, 2018-2019, 2022-2026, 2031-2035, 2038, 2041, 2044-2046, 2050-2054, 2058-2075, 2079-2096, 2101-2120, 2123, 2126, 2129, 2134-2138, 2141, 2144, 2147, 2152-2161, 2164, 2296, 2316-2335, 2338, 2341, 2344-2365, 2368-2369, 2372-2379, 2382, 2385-2388, 2391-2405, 2436-2475, 2479-2482, 2490-2544, 2551-2578, 2584-2589, 2598-2600, 2603-2606, 2611-2621, 2624-2628, 2631, 2634, 2637-2651, 2654-2676, 2679-2681, 2725-2734, 2738, 2741-2749, 2752-2765, 2769-2774, 2777-2789, 2836-2838, 2842-2847, 2858-2868, 2872, 2876-2882, 2890-2894, 2897, 2900, 2903-2906, 2909-2920, 2923, 2926-2928 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/builder.py 324 255 21% 57-79, 83-85, 89-91, 95-97, 101-103, 107-109, 113, 155-184, 218-257, 261-264, 268-278, 288-290, 300, 334-401, 413-418, 424-435, 440-447, 456-458, 462-480, 483, 486-508, 511-512, 523-538, 545-596, 599, 602-607, 613-616, 620-623, 632-659 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/errors.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/geometry.py 62 46 26% 8, 12, 16-19, 38-51, 56-57, 60, 63, 66-67, 73, 76, 91-143 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/table_builder.py 118 99 16% 49, 53, 57-72, 84-86, 89-119, 122-181, 186-188, 191-223 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/unbuilder.py 41 32 22% 6-10, 17-21, 26-34, 37-38, 41-58, 62-81 /home/admin/.local/lib/python3.8/site-packages/fontTools/config/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/designspaceLib/__init__.py 1422 1195 16% 49-56, 64, 68-70, 77-78, 81, 86-95, 105-109, 118-120, 185-296, 311, 315, 322, 329, 339-345, 379-390, 402, 411-421, 431-446, 525-650, 661, 665, 669, 672, 675, 678, 681, 684, 687, 690, 720-732, 744-752, 774-785, 794, 799-812, 830-869, 925-945, 953, 968-972, 976-982, 1031-1052, 1064, 1074-1076, 1116-1137, 1152-1156, 1161, 1187-1208, 1215, 1223, 1249-1271, 1286-1299, 1315-1320, 1338, 1342, 1346, 1350, 1353-1356, 1359-1409, 1421-1439, 1443-1460, 1463-1465, 1469-1497, 1500-1527, 1532-1548, 1551-1555, 1560-1568, 1577-1596, 1599-1702, 1705-1768, 1773-1807, 1810-1814, 1817-1850, 1866-1876, 1880-1883, 1886-1892, 1896-1933, 1936-1959, 1963-2006, 2009-2049, 2061-2099, 2102-2141, 2144-2196, 2201-2262, 2270-2274, 2282-2327, 2330-2332, 2342-2406, 2410, 2414, 2434-2482, 2486-2487, 2524-2608, 2615-2617, 2621-2626, 2630-2641, 2647-2654, 2658-2664, 2667-2668, 2712-2716, 2720, 2726-2728, 2732, 2738-2740, 2744, 2753-2758, 2762, 2768-2770, 2777, 2785-2787, 2794, 2802-2804, 2810-2815, 2825, 2839-2850, 2854, 2858, 2862, 2866-2869, 2873, 2877, 2885-2888, 2899, 2915, 2937-2948, 2952-2967, 2977-3032, 3062-3079, 3087-3092, 3109-3148, 3157-3167 /home/admin/.local/lib/python3.8/site-packages/fontTools/designspaceLib/split.py 113 95 16% 48-50, 87-117, 143-154, 166-171, 183-349, 353-360, 381-425, 432 /home/admin/.local/lib/python3.8/site-packages/fontTools/designspaceLib/statNames.py 89 70 21% 65-130, 146-160, 167-198, 209-249 /home/admin/.local/lib/python3.8/site-packages/fontTools/designspaceLib/types.py 77 56 27% 18, 31-32, 35-37, 40-43, 64-74, 78-88, 92-107, 111-147 /home/admin/.local/lib/python3.8/site-packages/fontTools/encodings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/encodings/codecs.py 62 50 19% 10-18, 21-36, 39, 42, 45-56, 109-132 /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/ast.py 1046 766 27% 81-84, 146-153, 161-163, 166, 172, 175, 190-192, 195, 202, 207, 210, 217-219, 223, 226, 233-237, 241, 244-250, 254, 258, 265-269, 275-279, 284-288, 296-298, 302, 305, 313-315, 319, 322, 329-331, 334-337, 344-345, 351-352, 355-356, 368-369, 372, 379-380, 386-395, 398-404, 412-414, 417-419, 422-425, 432-433, 437-439, 442-448, 455-456, 459-462, 469-471, 475, 478, 489-492, 496-500, 503, 527-529, 533-545, 549, 552-553, 580-583, 587, 590, 602-604, 608-614, 617-629, 649-652, 655-666, 673-674, 677-681, 688-690, 694-695, 698, 718-726, 730-733, 738-758, 776-784, 788-791, 796-816, 824-826, 830, 835-837, 844-845, 849, 852, 863-864, 869-873, 876-888, 899-900, 905-909, 912-921, 928-929, 933, 940, 947-951, 955, 963-969, 976-977, 981, 984, 992-993, 996, 999, 1007-1008, 1012-1013, 1016, 1026-1027, 1031-1032, 1035, 1051-1053, 1056-1059, 1064-1076, 1088-1091, 1095-1101, 1104-1117, 1126-1127, 1131, 1134, 1143-1144, 1148, 1151-1155, 1186-1187, 1191, 1194-1211, 1220-1221, 1225, 1228-1232, 1250-1253, 1257-1270, 1280-1293, 1315-1318, 1329-1355, 1364-1376, 1389-1392, 1395-1401, 1406-1416, 1429-1433, 1437-1443, 1452-1462, 1469-1470, 1474, 1477, 1488-1490, 1494-1497, 1500-1517, 1524, 1528, 1531, 1550-1555, 1558, 1568, 1571, 1583-1615, 1627, 1648-1650, 1653, 1657-1662, 1669-1674, 1678, 1688-1711, 1719-1720, 1723-1730, 1737-1740, 1747-1751, 1755, 1764-1767, 1776-1779, 1783-1788, 1791-1794, 1806-1808, 1812, 1815, 1823-1826, 1830, 1833-1838, 1849-1851, 1855, 1858-1884, 1891-1893, 1897, 1900-1902, 1909-1911, 1915, 1918-1920, 1933-1937, 1940, 1943-1947, 1958-1960, 1963, 1966-1970, 1981-1983, 1986, 1989, 2002-2005, 2008, 2011-2029, 2042-2044, 2047-2049, 2063-2065, 2068, 2071-2075, 2082-2083, 2092-2115, 2118-2125 /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/error.py 14 9 36% 3-4, 7-11, 16-22 /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/location.py 7 1 86% 12 /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/lookupDebugInfo.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/arrayTools.py 92 66 28% 20-24, 42, 57-59, 73-75, 89-92, 104-105, 117, 135-136, 151-152, 167-168, 183-184, 201-211, 226-234, 247-248, 261-262, 277-282, 295-298, 308, 348-359, 419-422 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/bezierTools.py 541 442 18% 14-18, 70, 76-78, 93-99, 125-126, 138, 148, 183, 218-234, 251, 287-295, 316-329, 357, 393-409, 430-447, 486-504, 542-549, 579-586, 609-610, 633-634, 657-658, 685-698, 702-725, 729-760, 779-798, 822-838, 879-930, 939-946, 950-960, 974-977, 981-990, 994-1006, 1021-1024, 1042, 1055-1057, 1070-1083, 1105-1108, 1112-1118, 1127-1137, 1141-1143, 1169-1221, 1228-1231, 1235-1244, 1268-1282, 1286-1290, 1294-1302, 1308-1369, 1393-1394, 1430-1445, 1453-1458, 1465-1466, 1470-1473 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/classifyTools.py 59 49 17% 13-17, 23-59, 65-66, 69-79, 89-90, 98-99, 107-108, 164-166, 170-172 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/cliTools.py 18 14 22% 37-52 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/configTools.py 128 66 48% 56, 63, 72, 81-86, 105-112, 116, 132-133, 150, 156, 162, 165, 168, 230-233, 236-248, 275-294, 321-326, 329, 332, 335, 338-339, 342, 345, 348 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/dictTools.py 26 13 50% 31, 34, 40, 43, 48, 53, 58, 63, 68, 73, 81-83 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/encodingTools.py 7 4 43% 69-72 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/etree.py 263 257 2% 47-478 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/filenames.py 76 64 16% 99-134, 168-191, 221-239, 243-246 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/fixedTools.py 35 20 43% 81, 109-110, 137-138, 157-158, 188-190, 212-213, 231-239, 251-253 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/intTools.py 9 2 78% 9, 25 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/loggingTools.py 240 173 28% 57-76, 79-86, 134-185, 192-226, 294-296, 306, 312, 316-319, 327-336, 340-342, 348-357, 370, 374-375, 384, 387, 390, 421-423, 426-434, 439-444, 447-456, 459-464, 467, 470-478, 511-514, 519, 528-533, 541-543 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/plistlib/__init__.py 259 182 30% 66-78, 82, 103-105, 109, 112, 117-122, 125, 131-142, 188-201, 204-207, 210-212, 215, 218-220, 225-238, 241-243, 250-252, 256-258, 262-264, 268-270, 274, 278, 282, 286, 290, 294, 298-301, 305, 331-333, 337-339, 343-347, 351-353, 359-373, 379-386, 390-392, 396-403, 407-416, 426, 479-490, 509-519, 544-554, 576-577, 612-632, 672-681 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/psCharStrings.py 950 717 25% 21-31, 35, 39-40, 44-45, 49-50, 54-55, 59-60, 64-65, 69-81, 131, 214-243, 255-259, 267-294, 303-309, 312-317, 320-348, 351-353, 356-358, 361, 364-365, 368, 371, 374-376, 379-381, 384, 387, 390, 393, 396-400, 405-406, 410, 413, 416, 419, 422, 425, 428, 431, 434, 437, 440, 443, 446, 449, 452, 455, 458, 461, 464, 467, 470, 473, 476-494, 497-499, 542-544, 547-549, 552-564, 567-568, 571, 574, 577, 580, 594-604, 607-610, 613-617, 620-623, 626-627, 630-632, 635-638, 641-643, 650-651, 673-674, 677-678, 681-682, 685-696, 702-705, 708, 711, 718-728, 732-736, 740-745, 749-758, 762-771, 777-781, 787-791, 797-801, 804-806, 809-814, 817-827, 831, 834, 837, 840, 843, 846, 849, 852-859, 862, 865, 868, 871, 874, 877, 880, 883, 886, 889, 892, 895, 898, 901, 907-914, 917-925, 928-936, 941-943, 946-949, 952-954, 957, 960-961, 967-970, 973-978, 981-987, 990, 993-995, 998, 1001-1004, 1007, 1011-1013, 1016-1023, 1027, 1030-1069, 1072, 1075, 1079-1086, 1089, 1100-1106, 1109-1115, 1118-1121, 1124, 1127, 1130-1134, 1137-1148, 1151-1153, 1156-1197, 1200, 1203-1204, 1207-1208, 1211-1224, 1227-1235, 1238, 1241-1276, 1279-1310, 1319-1320, 1323, 1326-1327, 1330-1339, 1342-1344, 1352-1355, 1358-1359, 1362-1371, 1374-1376, 1379-1381, 1384-1397, 1400-1404, 1407-1413, 1416, 1419, 1433-1453, 1456-1466, 1470-1477 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/roundTools.py 40 30 25% 20, 44, 48-49, 53-62, 88-109 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/sstruct.py 113 59 48% 63-76, 80-101, 105-106, 153, 157, 168, 178-216, 220 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/textTools.py 91 59 35% 16, 23, 29, 32, 37, 42-48, 53-58, 62-77, 81-87, 95-98, 114-119, 124, 130-133, 141, 145, 149, 153-155 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/timeTools.py 32 19 41% 48-56, 60, 64-69, 74-77, 81, 85-88 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/transform.py 126 78 38% 69-75, 175-177, 189-190, 202-204, 215-216, 227, 241-243, 255-259, 271-273, 285-287, 309-311, 332-338, 350, 354, 376, 379, 393, 405-407, 430-460, 480-488, 492-495 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/treeTools.py 26 24 8% 12-45 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/vector.py 80 42 48% 22-30, 33, 36-41, 44-46, 49, 52, 57, 60, 63, 68, 71, 74, 77, 80, 83-86, 89, 92, 97, 101, 105, 110-111, 116-120, 124-129, 133, 139, 143 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/visitor.py 88 42 52% 22, 54, 58-70, 82-94, 98, 102-103, 107-108, 113, 131-143 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/xmlWriter.py 146 118 19% 20-55, 58, 61, 64-65, 69, 73, 80, 84, 88-94, 97-102, 105-111, 114-116, 119-123, 126-130, 133-144, 147, 150-151, 154-167, 171-176, 180-182, 188-195, 199-204 /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/builder.py 1114 926 17% 55-59, 99-123, 130-137, 140, 149, 153, 156, 165-170, 173-177, 180-184, 187-191, 194-198, 201-208, 218, 249-250, 253, 262-265, 268, 271, 279, 284, 287, 293-296, 302-306, 309-319, 322-329, 334, 339-346, 349-359, 368-429, 432-475, 478-547, 550-559, 562-580, 583, 593-599, 612-621, 624-633, 636-640, 643-647, 652-673, 676, 712-714, 718-726, 757-759, 762-774, 778-787, 814-815, 818, 827-830, 833, 860-861, 864, 867-868, 871, 892-893, 896, 913-914, 923-924, 956-958, 961, 968-970, 979-997, 1032-1034, 1037, 1044-1046, 1055-1065, 1096-1098, 1101, 1108-1110, 1119-1137, 1167-1168, 1171, 1180-1190, 1194, 1220-1221, 1224, 1233-1234, 1237, 1240, 1255-1259, 1272-1286, 1290, 1294-1295, 1298-1304, 1328-1331, 1344, 1357-1372, 1375, 1385, 1398-1423, 1444-1446, 1458-1467, 1470-1472, 1475, 1484-1485, 1505-1509, 1537-1541, 1559-1563, 1576, 1605-1619, 1640-1653, 1682-1689, 1694-1696, 1714-1718, 1746-1759, 1779-1798, 1832-1840, 1845-1848, 1877-1884, 1934, 1958-1965, 2018, 2041-2048, 2052-2057, 2062-2064, 2069-2075, 2117-2154, 2189-2195, 2233-2262, 2295-2344, 2377-2396, 2400-2402, 2407-2417, 2425, 2435-2441, 2466-2469, 2490-2496, 2502-2507, 2512-2515, 2520-2523, 2558-2569, 2574-2584, 2612-2618, 2625-2627, 2630-2638, 2641-2652, 2667-2670, 2673-2681, 2772-2810, 2814-2853, 2859-2881, 2885-2908 /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/error.py 9 6 33% 3-4, 7-11 /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/maxContextCalc.py 57 52 9% 7-17, 26-59, 65-86, 92-96 /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/optimize/__init__.py 24 19 21% 8-43, 47-53 /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/optimize/gpos.py 220 176 20% 25-39, 56-62, 66-68, 72-82, 88-95, 101-122, 126-128, 140-149, 158-172, 202-206, 210-212, 216-221, 226, 230-260, 264-292, 301-302, 309, 317-452 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/basePen.py 178 122 31% 67, 71, 92, 109, 115, 121, 133, 147, 155, 158, 161, 164, 167, 170, 173, 176, 209-210, 214-224, 228, 240-241, 246, 249, 252, 257, 260, 267-274, 282, 285-286, 289-290, 293-294, 297-298, 301-327, 330-355, 368-390, 403-412, 419, 422, 425, 431, 435-444 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/boundsPen.py 56 41 27% 25-27, 30-31, 34-36, 39-47, 50-51, 54-59, 62-66, 83-90, 93-100 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/filterPen.py 55 31 44% 8, 58-59, 62-63, 66-67, 70-71, 74-75, 78-79, 82-83, 95-96, 99-100, 103-104, 107-111, 126, 155, 158, 161, 164 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/pointPen.py 292 246 16% 36, 40, 52, 62, 78, 91, 94-96, 124, 127-172, 177-179, 192-194, 197-259, 262-264, 274-278, 281-285, 288-289, 292-294, 297-303, 306-316, 319-331, 334-337, 340-342, 352-354, 357-394, 397-402, 405-407, 412-416, 419-423, 428-432, 446-448, 451-498, 501-505, 508-511, 516-520, 523-525 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/recordingPen.py 61 35 43% 22-23, 50, 53, 56, 59, 62, 65, 68, 71, 74, 77, 135, 138-140, 143, 148-150, 153-155, 160-162, 167-168, 172-179 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/transformPen.py 52 35 33% 18-25, 28, 31, 34, 37-41, 44-45, 48, 51, 54-55, 86-92, 95, 100-101, 105-111 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/__init__.py 2014 1674 17% 451-454, 466, 470-471, 477, 483, 489-491, 497, 503, 512-514, 520-534, 540, 545, 550-553, 558-560, 565-570, 575, 580-585, 590, 601-611, 616-625, 630-646, 651, 656-659, 664-674, 679-706, 711-752, 757-773, 778-786, 791-799, 804-810, 815-837, 842-850, 855-880, 885-894, 899-921, 926-934, 951, 968, 983, 990, 1000, 1010-1160, 1165-1272, 1282-1368, 1378-1415, 1425-1440, 1445-1448, 1453-1456, 1461-1464, 1469-1472, 1477-1480, 1485-1488, 1493-1509, 1514-1516, 1521-1527, 1532-1533, 1538, 1543, 1549, 1554-1560, 1565-1569, 1575-1576, 1584-1600, 1606-1613, 1625, 1634, 1646-1649, 1655, 1665, 1676, 1689-1697, 1702-1715, 1720-1730, 1735-1738, 1743-1753, 1758-1761, 1767-1774, 1779, 1785-1791, 1797-1803, 1808-1811, 1816-1841, 1846-1852, 1859, 1866-1880, 1888-1889, 1895-1913, 1918-1930, 1937-1959, 1965-1974, 1982-1988, 1993-2020, 2025-2067, 2072-2105, 2109-2131, 2136-2159, 2172-2173, 2178-2186, 2191-2194, 2199-2202, 2207-2211, 2216-2221, 2226-2228, 2233-2248, 2253-2260, 2265-2267, 2272-2274, 2278-2285, 2290-2323, 2328-2366, 2371-2375, 2380-2396, 2401-2403, 2408-2409, 2414-2432, 2436-2446, 2451-2476, 2481-2528, 2538-2604, 2609-2615, 2620-2633, 2638-2639, 2644-2647, 2652-2655, 2660-2663, 2668-2676, 2681-2691, 2696-2701, 2706-2731, 2736-2748, 2753-2758, 2763-2778, 2783-2786, 2791-2809, 2814-2823, 2828-2889, 2895-2897, 2902-2911, 2916-2964, 2970, 3084-3131, 3134-3137, 3140-3214, 3228-3234, 3237-3245, 3248-3273, 3276-3433, 3436-3459, 3462-3492, 3495-3498, 3501-3504, 3509-3536, 3541-3547, 3551-3563, 3567-3574, 3578, 3582-3583, 3589-3724, 3739 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/cff.py 345 294 15% 11-12, 15-25, 30-48, 52-68, 73-89, 94-132, 137-144, 151-185, 190-195, 198-199, 202-203, 213-229, 236-240, 243-264, 267-269, 272-274, 277-278, 281-282, 285-286, 289-290, 293-295, 298-300, 303-317, 320-324, 327-353, 358-381, 385-388, 396, 401-464, 469-536 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/svg.py 141 114 19% 10-13, 36, 43, 49-57, 65-87, 95-105, 112-124, 132-156, 160-164, 169-174, 179-190, 195-251 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/util.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/__init__.py 50 36 28% 21-23, 54-119, 123 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/sfnt.py 378 307 19% 33-44, 47-96, 99, 104, 108-122, 125, 128, 137-146, 149-152, 185-194, 202-214, 224-263, 266-269, 273-299, 302, 308-373, 377-406, 409-412, 415, 487, 490, 493, 496, 499-502, 505-510, 513-517, 520, 523, 538-546, 549-557, 560-570, 578-596, 599-601, 615-625, 629-645, 649-657, 661-664 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/standardGlyphOrder.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/BitmapGlyphMetrics.py 28 15 46% 34-40, 43-52 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/C_F_F_.py 33 21 36% 8-10, 13-14, 17-19, 22-25, 28-33, 36, 41, 44-46 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/C_O_L_R_.py 110 93 15% 20-35, 38-53, 56-71, 74-83, 86-97, 100-125, 128-130, 133-138, 141, 146-147, 150-151, 154-158 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/C_P_A_L_.py 208 179 14% 19-23, 26-68, 76-82, 85-91, 94-144, 147-149, 152-162, 165-170, 173-178, 181-190, 193-239, 242-271, 276, 279, 282-283, 287-293, 297 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/D_S_I_G_.py 81 63 22% 43-65, 68-86, 89-102, 105-114, 125-132, 137, 140-145, 148-151 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/DefaultTable.py 38 26 32% 10-12, 15, 18, 21-30, 33-38, 41, 44-46, 49-50 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/E_B_D_T_.py 515 420 18% 49, 56-84, 88-135, 141-164, 167-200, 205-211, 214-223, 230-241, 245-254, 260-263, 274-282, 289-293, 297, 301-315, 319-332, 336-361, 365-384, 390-408, 412-414, 440-441, 449-455, 458-460, 464, 467-475, 478-488, 493, 497, 500-506, 510-512, 528, 531-539, 557-559, 562-608, 611-640, 645-647, 650-657, 660-664, 671-673, 676-677, 684-686, 689-690, 695, 698, 705-707, 710-711, 718-720, 723-724, 729-742, 745-761, 766-777, 780-787, 792-801, 804-810 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/E_B_L_C_.py 384 310 19% 76, 81-138, 142-239, 242-245, 248-262, 267-268, 271-281, 284-296, 304-307, 310-318, 323-340, 345-351, 354-360, 369-370, 378-383, 386-387, 393, 404-422, 428-440, 446, 450, 459, 466-472, 487-503, 508-547, 557-559, 562-572, 578-584, 595-604, 607-617, 629-646, 651-673, 678-695, 698-707 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/G_D_E_F_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/G_P_O_S_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/G_S_U_B_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/H_V_A_R_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/M_A_T_H_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/S_V_G_.py 127 98 23% 55-97, 102-153, 156-168, 171-181, 186-189, 192, 212, 215 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/TupleVariation.py 522 459 12% 39-40, 43-46, 49, 53-57, 65, 68-106, 109-128, 131-166, 169-177, 180-196, 200-205, 215-270, 275-321, 324-340, 359-371, 375-385, 389-415, 419-461, 466-488, 492-497, 503-510, 516-519, 529-530, 540-550, 553-577, 580-581, 584-614, 618-622, 628-638, 645-705, 718-744, 750-790, 804-808 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/V_O_R_G_.py 111 96 14% 16-47, 50-71, 74-96, 99-113, 116-123, 126-133, 136, 141-142, 145-152, 155-159 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/V_V_A_R_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/__init__.py 87 85 2% 8-90, 94-96 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_a_n_k_r.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_b_s_l_n.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_c_m_a_p.py 929 840 10% 16-23, 73-76, 113-117, 135-140, 143-184, 189-190, 193-215, 218-221, 224-236, 254, 260-261, 264-269, 276-279, 285-290, 293-305, 308-320, 332, 336, 342, 345-350, 353-369, 376-389, 392-403, 406-416, 424-428, 433-458, 463-577, 580-756, 759-770, 788-859, 866-928, 931-1039, 1042-1053, 1060-1081, 1084-1106, 1109-1120, 1125-1128, 1131-1145, 1150-1172, 1175-1243, 1256-1272, 1275-1290, 1298, 1301, 1304, 1312, 1315, 1318, 1322-1324, 1328-1330, 1335-1340, 1343-1397, 1400-1420, 1423-1454, 1457-1527, 1532-1543, 1546-1547, 1550-1551, 1556-1559, 1564-1567 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_f_v_a_r.py 150 122 19% 49-51, 54-74, 77-95, 98-101, 104-111, 116-121, 124, 127, 130-152, 155-161, 170-173, 176-182, 185-194, 197-236, 239-250 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_g_l_y_f.py 1502 1308 13% 91-122, 127-128, 131-179, 182-249, 252-270, 278, 285, 293, 300-301, 304, 307, 312-315, 318-320, 323-325, 328-329, 332-333, 347-362, 390-442, 458-499, 508-521, 527-529, 537-539, 545-547, 592-614, 618-620, 624, 632-638, 642-643, 690-694, 697-699, 702-722, 725-742, 745-783, 786-837, 840-857, 860-861, 864-879, 885-889, 892-945, 949-989, 992-1005, 1008, 1011-1030, 1035-1078, 1083-1142, 1152-1156, 1160-1163, 1167-1170, 1188-1245, 1253-1290, 1296-1392, 1396, 1408-1488, 1495-1521, 1524-1526, 1529-1530, 1547, 1563-1568, 1571-1623, 1626-1677, 1680-1704, 1707-1727, 1730-1732, 1735-1736, 1804-1805, 1809-1827, 1830-1891, 1894-1955, 1958-1980, 1983-2006, 2009-2033, 2037-2074, 2077-2114, 2117-2119, 2122-2123, 2134-2135, 2140, 2145-2147, 2151-2153, 2157, 2161-2167, 2171-2177, 2181-2183, 2186, 2189, 2192-2193, 2196-2198, 2201-2205, 2208-2216, 2222-2228, 2234-2240, 2246-2253, 2267-2269, 2283-2284, 2302, 2315-2319, 2322-2324, 2327, 2330, 2333, 2336, 2342, 2355-2366, 2379-2390, 2404-2415, 2425-2433, 2450, 2456-2458 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_g_v_a_r.py 185 150 19% 44-45, 48-52, 59-61, 64-95, 98-111, 114-147, 153, 157-176, 188-199, 202-216, 219-240, 244-255, 259-267, 271-275 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_h_d_m_x.py 99 82 17% 17-18, 21, 24, 27, 30, 35-47, 50-64, 67-93, 96-119 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_h_e_a_d.py 79 63 20% 44-66, 69-90, 93-111, 114-124 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_h_m_t_x.py 105 83 21% 23-68, 71-120, 123-134, 137-140, 146, 149, 152 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_k_e_r_n.py 192 160 17% 16-19, 22-62, 65-77, 80-83, 86-100, 109, 112-155, 160-206, 209-223, 226-244, 247, 250, 253, 258, 261, 264, 267-273, 276 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_l_c_a_r.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_m_a_x_p.py 100 87 13% 34-38, 41-52, 59-115, 118-122, 125-137, 140 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_n_a_m_e.py 289 241 17% 38-64, 67-88, 91-92, 95-101, 104-112, 115-133, 136-140, 146, 152, 157-171, 183-199, 205-218, 232-236, 259-300, 327-353, 369-381, 385-393, 405-414, 438-466, 476, 479, 482, 485, 509-551, 564, 569-591, 594-606, 609-643, 646 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_o_p_b_d.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_p_o_s_t.py 222 195 12% 31-43, 48-62, 69-73, 76, 79-107, 110-129, 134, 137-154, 157-184, 189-205, 208-248, 251-270, 277-301, 305-308 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_p_r_o_p.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_s_b_i_x.py 69 53 23% 32-37, 41-68, 71-87, 90-95, 98-112 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/_v_m_t_x.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/otBase.py 913 733 20% 21, 30-34, 37, 54, 57, 90-95, 120-186, 189-208, 211, 214-239, 242, 245-251, 254, 269-273, 276, 279, 282-284, 287-288, 291-295, 298-304, 307, 310, 313, 316, 319, 322, 325, 328, 331, 334, 337, 340, 343-347, 350, 353-358, 361-365, 368-370, 373, 376, 384-389, 395, 399, 402-404, 407, 410, 416-424, 428-450, 454-464, 468, 471-472, 475-477, 487-513, 524-574, 577-631, 641-664, 668-697, 702-711, 714, 717-720, 723-724, 727, 730-731, 734, 737, 740, 743-744, 747, 750-751, 754, 757, 760, 763-765, 768-769, 772-774, 777, 780-782, 785-786, 789, 792-822, 831-835, 838-843, 846, 849-852, 856, 860, 864-865, 869-870, 878-886, 889-897, 902-906, 910-922, 925, 928, 931-955, 958-997, 1000-1098, 1101, 1104, 1107-1116, 1122-1134, 1137-1149, 1152-1153, 1156-1162, 1182-1190, 1198, 1208, 1211-1220, 1223, 1226, 1229, 1232, 1235, 1240, 1243, 1255-1256, 1267-1283, 1331-1335, 1338, 1341-1360, 1363-1375, 1383-1393, 1396-1399, 1402-1406, 1409-1432, 1435-1449, 1452-1453, 1456-1458 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/otConverters.py 1304 923 29% 114-115, 120, 123-131, 134-140, 143-145, 183-202, 205-207, 211, 214-219, 223, 227, 231, 237-240, 246, 250, 253-254, 257, 264-267, 270-272, 278, 285, 288, 291, 294, 301, 304, 307, 310, 316, 327, 330, 333, 336, 343, 346, 349, 352, 359, 362, 365, 368, 375, 378, 381, 384, 391, 394, 399-401, 420, 423, 431, 436, 439, 442, 452-463, 468-477, 483, 490, 493, 503, 506, 510, 514, 518, 522, 547, 551, 556, 560, 573-574, 577-578, 582, 586, 590, 603-610, 613-621, 626, 629-631, 634, 637-647, 650-689, 692, 697-701, 704-719, 727, 730, 733-743, 746-754, 762, 765, 774, 777, 785-786, 789, 794-795, 798, 803-804, 809-810, 823-825, 828-829, 834, 837, 840, 843-846, 849-853, 871-883, 886-909, 919-926, 929-933, 940-942, 947-960, 967-979, 982-984, 991-1001, 1004-1008, 1015-1020, 1025-1027, 1030-1042, 1045-1062, 1065-1075, 1078-1081, 1084-1090, 1093-1100, 1112-1123, 1135-1158, 1161-1162, 1165-1166, 1186-1191, 1194-1218, 1221-1238, 1241-1270, 1273-1293, 1311-1355, 1358-1363, 1366-1368, 1375-1383, 1386-1392, 1395-1447, 1450-1473, 1476-1489, 1492-1497, 1500-1505, 1508-1545, 1548-1556, 1559-1578, 1581-1588, 1591-1595, 1598-1602, 1607-1612, 1615-1619, 1622-1626, 1629-1636, 1641-1655, 1658-1666, 1669-1673, 1676-1683, 1688-1708, 1711-1729, 1732-1733, 1736, 1741-1757, 1763-1779, 1789-1809, 1812-1828, 1831-1832, 1835, 1840-1856, 1863, 1867, 1871 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/otData.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/otTables.py 1522 1212 20% 34, 42-44, 49, 57, 60-66, 69-70, 98-103, 106-116, 119-126, 129-140, 143-155, 164-167, 170-179, 182-189, 192-202, 205-220, 225-235, 247-250, 253-267, 270-286, 290-313, 316-323, 326-339, 342-361, 364-376, 392-396, 399-426, 429-441, 446-449, 452-464, 467-480, 484-511, 516-522, 525, 545-546, 549-571, 574-615, 618-620, 623-627, 637-638, 641-642, 646-668, 671-678, 683-695, 698-705, 710-711, 714-718, 721-733, 736-743, 746-757, 769-772, 780-781, 784-803, 806-842, 845-848, 851-855, 860-861, 864-872, 875-885, 888-892, 895-924, 928-930, 935-936, 939-967, 970-992, 995-1023, 1026-1029, 1032-1036, 1041-1042, 1045-1055, 1058-1081, 1084-1092, 1095-1106, 1111-1112, 1115-1125, 1128-1169, 1172-1182, 1185-1201, 1211-1220, 1226-1227, 1233-1258, 1264-1270, 1273-1304, 1309-1312, 1317-1320, 1328, 1331, 1338, 1341, 1346-1347, 1350-1390, 1393-1400, 1405-1438, 1441-1465, 1468-1490, 1567, 1570-1575, 1582-1585, 1588-1597, 1600-1624, 1628, 1632-1639, 1642-1682, 1687-1706, 1824-1856, 1860-1882, 1886-1911, 1915-1937, 1941-2008, 2013-2068, 2100-2149, 2270-2274 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/otTraverse.py 40 30 25% 16-22, 59, 99, 120-161 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/sbixGlyph.py 78 67 14% 34-55, 58-83, 86-102, 105-130, 133-145 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/sbixStrike.py 96 82 15% 26-29, 32-85, 88-121, 124-136, 139-177 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/ttProgram.py 311 256 18% 160-164, 191, 194, 212-215, 220, 223-225, 228-235, 238-240, 243-245, 248-313, 316-322, 325-467, 470-532, 558, 565-567, 570-571, 580-586, 590-593 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/ttCollection.py 74 56 24% 23-47, 50, 53, 56-57, 64-87, 91-112, 115, 118, 121, 124, 127 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/ttFont.py 583 477 18% 110-178, 181, 184, 188-189, 202-238, 243-263, 274-276, 291-348, 351-377, 383-396, 401, 408-415, 421-430, 434-440, 443, 446-456, 459-488, 491, 494-499, 503-506, 514-518, 522-553, 568-621, 625-632, 636-637, 643-645, 653-656, 660-662, 666-674, 678-683, 687-689, 692-695, 701-721, 730-738, 764-773, 785-805, 842, 852, 855-863, 866-870, 882-890, 911-913, 918, 927-931, 938, 941-943, 951-954, 959-960, 972, 996, 1004, 1010-1026, 1034-1044, 1049-1054, 1088-1104, 1111-1124, 1131-1135, 1142-1146 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/ttGlyphSet.py 197 157 20% 21-42, 49-64, 68-73, 76, 79, 82, 88, 93-95, 98, 103-115, 118, 133-146, 154, 160-162, 170-181, 187-198, 202-223, 226-232, 235-272, 280, 285-317 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/ttVisitor.py 20 13 35% 9-11, 14-16, 25-32 /home/admin/.local/lib/python3.8/site-packages/fontTools/unicode.py 37 29 22% 2-12, 17-22, 25-28, 33-42, 50 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/__init__.py 669 605 10% 73-130, 140-218, 226-233, 240-329, 333-347, 353-419, 447, 451, 456-499, 512-597, 602-676, 681-692, 697-724, 728-759, 779-788, 792-806, 812-916, 944-965, 990-1022, 1039-1125, 1132-1147, 1162-1172, 1177, 1180-1190, 1195-1324, 1328-1334 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/builder.py 88 72 18% 8-10, 14-23, 27-33, 37, 41-90, 100, 104, 111-121, 125-130, 137-139, 143-146, 150-154 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/errors.py 113 68 40% 16-24, 28, 31-37, 41-49, 53-57, 60-70, 78-102, 114-115, 119-120, 128-129, 133-134, 161-163, 167-170, 185-191, 198-203, 210-215 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/featureVars.py 278 246 12% 48-74, 78-84, 137-202, 227-297, 311, 331-390, 400-428, 435-444, 449, 454-456, 463, 473-489, 494-498, 503-508, 513-521, 526-531, 536-541, 549-560, 565-576, 580-582, 586-588 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/iup.py 206 183 11% 12-16, 40-72, 81-124, 136-147, 164-172, 188-256, 275-301, 307-311, 315-318, 331-419, 435-448 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/merger.py 986 844 14% 47-49, 66, 81-93, 96-118, 121-128, 131-149, 152-160, 172-193, 197-207, 218-244, 251-252, 259-260, 264-278, 284-305, 310-359, 367-415, 419-448, 453-477, 481-504, 509-583, 587-624, 629-668, 672-754, 759-769, 774-784, 788-809, 813-834, 838-864, 871-885, 889-909, 914-1010, 1023-1026, 1031-1035, 1040-1046, 1051-1066, 1081-1083, 1090-1106, 1113-1137, 1144-1166, 1179-1181, 1184-1185, 1188-1211, 1215-1218, 1223-1230, 1235-1242, 1247-1258, 1263-1276, 1291-1306, 1309-1319, 1322-1349, 1352-1357, 1360-1367, 1370-1389, 1394-1418, 1421-1431, 1435-1452, 1468-1499, 1505-1517, 1524-1527, 1531-1536, 1543-1604, 1609-1645, 1650-1662, 1667-1671, 1678 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/models.py 303 270 11% 15, 19, 23-27, 31-38, 42-43, 56-74, 116-120, 157-201, 252-271, 274-281, 285-292, 296-345, 350-358, 361-414, 417-436, 439-447, 450-461, 464-465, 468, 477-487, 490-491, 494-495, 498-499, 503-519, 524-575, 579-584 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/mvar.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/stat.py 48 37 23% 32-42, 58-69, 90-91, 108-113, 119-142 /home/admin/.local/lib/python3.8/site-packages/fontTools/varLib/varStore.py 426 355 17% 19, 24-33, 36-37, 40-45, 48-53, 56-88, 91-93, 96-118, 122-132, 139, 150, 158-162, 165-166, 169, 172-177, 181-186, 189-195, 198-200, 215-269, 282-297, 307-322, 327-328, 332-333, 342-348, 352-353, 362-365, 368, 371, 376-377, 385-386, 389, 392, 395, 398, 404, 410-415, 418-425, 430-431, 434-435, 440-465, 474-602, 610-647, 651-657 /home/admin/.local/lib/python3.8/site-packages/google/api_core/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/google/api_core/client_options.py 24 19 21% 95-108, 111, 122-130 /home/admin/.local/lib/python3.8/site-packages/google/api_core/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/_credentials_base.py 11 3 73% 47, 63, 73 /home/admin/.local/lib/python3.8/site-packages/google/auth/_default.py 225 188 16% 73-76, 113-126, 165-170, 178-221, 226-245, 251-279, 286-306, 318-343, 378-413, 419-430, 434-444, 448-458, 462-508, 512-522, 527-529, 533-544, 644-697 /home/admin/.local/lib/python3.8/site-packages/google/auth/_exponential_backoff.py 63 39 38% 73-85, 90, 95, 98-99, 102-108, 117, 120-121, 124-136, 145, 148-149, 152-164 /home/admin/.local/lib/python3.8/site-packages/google/auth/_helpers.py 61 34 44% 55, 80-82, 98-100, 112, 130-134, 152-156, 186-203, 216, 228-231, 245-247, 264, 273 /home/admin/.local/lib/python3.8/site-packages/google/auth/_refresh_worker.py 36 22 39% 32-33, 48-60, 66-68, 72-74, 78-79, 96-99, 105-106 /home/admin/.local/lib/python3.8/site-packages/google/auth/_service_account_info.py 18 11 39% 45-61, 78-80 /home/admin/.local/lib/python3.8/site-packages/google/auth/credentials.py 144 74 49% 51-67, 80-85, 97, 104-119, 124, 129, 140, 156, 171, 181-198, 201-202, 205-217, 236-242, 245, 261, 264-267, 282, 297, 312, 317, 322, 332-333, 369-371, 376, 381, 387, 401-404, 449, 474-477, 495, 502, 509 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/__init__.py 17 7 59% 91-98 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/_cryptography_rsa.py 56 26 54% 48, 52-57, 74-85, 101-102, 107, 111-112, 132-136, 140-146, 150-151 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/base.py 32 10 69% 44, 53, 67, 87, 104-109, 124-127 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/es256.py 67 34 49% 48, 53-73, 90-101, 117-118, 123, 127-132, 156-160, 164-170, 174-175 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/rsa.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/environment_vars.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/exceptions.py 31 6 81% 22-24, 28, 58, 70 /home/admin/.local/lib/python3.8/site-packages/google/auth/iam.py 42 21 50% 84-86, 90-116, 126, 130-131 /home/admin/.local/lib/python3.8/site-packages/google/auth/jwt.py 237 160 32% 89-115, 120-127, 143-168, 184-185, 201-228, 257-316, 395-406, 424-426, 443-444, 458-461, 491-493, 514-517, 528, 543-560, 570, 574, 579, 584, 589, 637-648, 666-668, 685-686, 700-703, 729-731, 748-751, 763, 779, 790-806, 821-827, 841, 858-864, 868, 873, 878 /home/admin/.local/lib/python3.8/site-packages/google/auth/metrics.py 48 20 58% 46, 54, 62, 70, 80, 89, 98, 106, 114, 120, 126, 132-135, 149-154 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/__init__.py 20 4 80% 55, 60, 65, 103 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/_http_client.py 42 24 43% 36-38, 42, 46, 50, 80-113 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/_mtls_helper.py 134 107 20% 61-65, 81-88, 109-114, 134-144, 148-186, 190-193, 197-207, 211-222, 241-276, 310-337, 360-365, 401-407 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/mtls.py 35 29 17% 27-39, 53-67, 89-112 /home/admin/.local/lib/python3.8/site-packages/google/auth/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/oauth2/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/google/oauth2/_client.py 126 100 21% 57-69, 85-110, 123-133, 174-217, 259-271, 297-319, 344-367, 396-419, 439-450, 494-508 /home/admin/.local/lib/python3.8/site-packages/google/oauth2/service_account.py 249 165 34% 174-197, 215, 243-246, 260-263, 268, 273, 282, 285-299, 303-306, 321-330, 334-338, 350-352, 366-370, 374-376, 380-382, 393-415, 420, 423-425, 429-452, 461-490, 496, 501, 506, 510-516, 599-621, 639-643, 661-664, 678-681, 684-695, 709-711, 730-739, 743-745, 749-751, 762-782, 803-809, 820-828, 833, 837, 842, 847 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/any_pb2.py 16 4 75% 31-34 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor.py 404 261 35% 66-73, 80-82, 85, 88, 95-97, 138-143, 151-155, 163-181, 209-220, 232-238, 327-369, 376-379, 397, 406, 558, 575-607, 616-618, 635-638, 684-697, 706, 742-750, 781-790, 827-828, 833-845, 856, 865, 892-893, 903-913, 925-931, 977-980, 984, 991-1011, 1019, 1028-1029, 1034-1050, 1055-1058, 1063-1075, 1096-1179 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor_database.py 57 42 26% 50-51, 63-85, 104, 128-141, 145, 149, 152-158, 171-177 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor_pool.py 465 393 15% 75-80, 99, 104-107, 111, 140-155, 165-193, 202, 216-221, 227, 240-246, 252, 264-287, 293, 303-308, 314, 329-356, 361, 374-379, 392-394, 409-423, 439-450, 465-499, 514-517, 532-535, 549-552, 566-569, 583-599, 618-622, 637-646, 655-674, 688-691, 705-708, 722-731, 745-826, 843-920, 939-972, 995-1005, 1037-1056, 1067-1132, 1145, 1169-1188, 1204-1209, 1229-1234, 1246-1250, 1263-1272, 1276, 1285 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/api_implementation.py 31 9 71% 44-45, 48, 65-67, 86, 102, 107, 112 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/containers.py 328 229 30% 62-63, 67, 71, 76, 79, 82, 88-90, 93, 114-115, 119-121, 125-127, 132-146, 152-153, 157-158, 162-164, 168-174, 178, 182-186, 190-191, 195-196, 200-206, 231-232, 238-243, 247-252, 256-261, 268-276, 282, 286-287, 291-293, 297, 301-302, 306-307, 311-316, 340-344, 347-353, 358-359, 365-368, 371-374, 377-378, 381, 384, 387, 390-391, 396-398, 402-403, 406, 430-434, 437-446, 460, 466-469, 472-473, 476, 479-481, 484, 487, 490, 494-499, 506-508, 512-513, 516, 527-530, 534, 537-540, 548-550, 553-557, 562-564, 568-570, 574-576, 587, 590-599, 602, 605-608, 611-613, 616-617, 620-623, 626-636, 639-643 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/decoder.py 499 420 16% 108-120, 131-144, 173-179, 196-243, 257-258, 281-283, 309-328, 353-368, 375-508, 547-593, 600-634, 640-681, 687-731, 752-832, 839-876, 887-892, 897-900, 905-906, 912-916, 923-927, 933-944, 950-967, 973, 979-982, 988-989, 995, 1024-1025 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/encoder.py 475 391 18% 82-91, 96-106, 113, 129-148, 158-177, 187-203, 231-247, 253-269, 275-287, 293-308, 326-334, 346-364, 379-381, 394-402, 442-466, 476-500, 516-538, 559-566, 569-578, 582-615, 650-681, 687-705, 711-727, 733-748, 754-769, 787-801, 820-829 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/enum_type_wrapper.py 33 19 42% 53-64, 69-73, 83, 103, 108-114 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/type_checkers.py 121 59 51% 61, 71-76, 80, 92-101, 123-132, 142, 153-155, 157, 164, 172, 175-181, 184, 195-217, 220, 261-272, 275 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/well_known_types.py 447 337 25% 68-72, 76-80, 85, 89, 108-126, 139-190, 194, 198, 202, 207, 212, 216-217, 221-222, 226-227, 231-232, 236, 249-250, 267-288, 301-321, 326, 330-331, 335-336, 340, 344, 349, 355, 361-362, 366, 372, 378-382, 386-395, 406-411, 421-424, 428-433, 437-440, 444-446, 458-459, 463-467, 471-477, 492-493, 499-508, 513-516, 522-546, 551-561, 581-583, 587-588, 604-613, 617-618, 627-635, 639-643, 649, 658-660, 666-698, 703-711, 715-732, 736-750, 759, 762, 765, 768, 771, 774, 777, 780, 783, 787-790, 794-797, 800-801, 812, 815, 818-819, 823, 826, 829, 832-833, 837-840, 844-847 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/wire_format.py 105 49 53% 75, 77, 89, 97, 105-107, 112-114, 123, 127, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 194, 199, 209-221, 227, 237-248, 268 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/json_format.py 423 362 14% 121-127, 158-165, 169, 184-191, 194-195, 199-206, 210-270, 274-313, 317-334, 340, 344-356, 360, 365-369, 372, 377, 381-386, 391-400, 419-425, 444-446, 456-457, 469-476, 488-594, 598-619, 625-628, 632-645, 650-655, 659-667, 671-672, 685-697, 715-752, 767-777, 782-813, 829-839 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/message.py 90 46 49% 81-83, 87, 91, 94, 98, 102, 115, 126-129, 133, 143, 152, 191, 198-199, 215, 230, 261, 279, 293, 309, 327, 335, 343, 350, 360, 364, 368, 389, 393, 397-403, 406-413, 419-421 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/message_factory.py 56 39 30% 49, 78-85, 100-121, 137-157, 176-185 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/pyext/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/pyext/cpp_message.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/reflection.py 13 5 62% 75-78, 95 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/symbol_database.py 44 21 52% 95, 108, 118-120, 130, 148, 169-186 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/text_encoding.py 32 13 59% 73-80, 96-107 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/text_format.py 722 458 37% 79-89, 92, 95, 101, 104, 107, 110, 171-192, 198-202, 226-239, 257-263, 281-287, 302-312, 375-391, 395-407, 410-418, 426-457, 461-512, 516-541, 545-548, 553-560, 563-579, 590-629, 682, 755-759, 811-813, 852-875, 878-907, 912-915, 923-925, 929, 932, 941-943, 959-964, 970-971, 982-992, 1008, 1015, 1017, 1022-1027, 1033, 1041, 1045-1050, 1054-1055, 1083, 1086, 1089, 1091, 1093, 1095, 1099, 1103, 1108-1115, 1119-1124, 1127, 1146-1150, 1159-1173, 1183-1192, 1206-1214, 1259, 1314, 1317-1321, 1328-1337, 1340-1344, 1355-1359, 1362-1366, 1379, 1384-1388, 1399-1404, 1407-1411, 1422-1427, 1438-1443, 1446-1450, 1461-1465, 1476-1479, 1493-1505, 1510-1511, 1524, 1529, 1533, 1549, 1588, 1592-1596, 1615-1619, 1634, 1653-1654, 1700, 1703-1704, 1719-1736, 1751-1756, 1782-1793 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/wrappers_pb2.py 56 20 64% 95-114 /home/admin/.local/lib/python3.8/site-packages/google_auth_httplib2.py 88 46 48% 41-42, 47, 52, 57, 86, 111-126, 131, 176-185, 189, 203-263, 267, 272, 277, 282, 287, 292, 297, 302, 307 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/__init__.py 8 4 50% 20-24 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/_auth.py 64 44 31% 43-49, 56-69, 88-97, 112-124, 132-137, 142-144, 148-151, 158-167 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/_helpers.py 53 29 45% 113-130, 137-138, 153-163, 183-188, 204-207 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/channel.py 71 46 35% 104-107, 135-138, 200-207, 218-233, 245-248, 267-278, 299-308 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/discovery.py 586 487 17% 59, 136-139, 161-165, 179-189, 279-343, 360-369, 404-453, 457-460, 538-722, 748-763, 775-782, 799, 830-847, 878-888, 923-935, 952-956, 971-975, 1014-1025, 1038-1066, 1079-1331, 1351-1392, 1429-1442, 1451-1452, 1460-1464, 1472-1474, 1477, 1480, 1487, 1490-1492, 1496-1535, 1541-1573, 1581-1604, 1617-1627, 1640, 1659-1662 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/errors.py 86 43 50% 40-46, 51, 55-85, 88-102, 166-168, 171-174, 185, 195 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/http.py 672 543 19% 43-44, 90-147, 170-229, 243-244, 253-256, 269-270, 279-282, 318, 326, 334, 342, 355, 367, 376, 389-396, 405, 419-425, 469-478, 486, 494, 502, 510, 523-524, 536, 545, 549, 592-601, 606-607, 616, 620-621, 658-659, 695-713, 734-780, 801-804, 816-820, 852-874, 896-939, 951, 992-1093, 1109-1126, 1133-1141, 1146-1149, 1162, 1210-1247, 1259-1277, 1290-1296, 1313-1319, 1331-1367, 1379-1395, 1405-1408, 1440-1453, 1468-1525, 1544-1606, 1624-1630, 1638, 1681-1682, 1700-1720, 1732-1744, 1755-1759, 1762, 1794-1796, 1808-1826, 1848-1877, 1898-1930, 1944-1962 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/mimeparse.py 56 41 27% 45-56, 73-83, 95-120, 133, 147-149, 166-177, 181-183 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/model.py 170 103 39% 38, 51, 79, 94, 118-130, 152-182, 193-205, 209-215, 230-241, 252, 264, 284, 287-293, 296-307, 311, 327, 331, 347, 351, 375, 378, 381, 385, 409-429 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/sample_tools.py 31 22 29% 58-108 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/schema.py 110 86 22% 81-84, 99-114, 127, 142-145, 158, 167, 184-200, 208, 216, 225-232, 236, 240, 251-302, 316-317 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/httplib2/__init__.py 914 737 19% 59-61, 151-188, 192-194, 202-217, 228-229, 233-245, 258-274, 281, 290-292, 296-304, 342-386, 390-412, 416-429, 433-471, 475-478, 482, 498-502, 505-506, 510-511, 516, 526, 529, 532, 535, 538, 541, 544, 547, 552, 557, 567-579, 583-615, 618-630, 639-678, 682-689, 705-708, 721, 726-730, 740-765, 770, 791-794, 797-805, 808-811, 814-816, 824, 827, 830-832, 840, 843-845, 877-881, 900, 911, 914, 918-929, 932, 942-949, 955-979, 994-998, 1002-1066, 1093-1114, 1118-1202, 1272, 1313-1317, 1320-1327, 1330-1331, 1337-1341, 1346, 1351, 1356-1357, 1360-1431, 1439-1510, 1513, 1542-1751, 1775-1793, 1796-1799 /home/admin/.local/lib/python3.8/site-packages/httplib2/auth.py 40 17 58% 40-49, 54-69 /home/admin/.local/lib/python3.8/site-packages/httplib2/certs.py 29 9 69% 10-11, 17, 30-33, 35, 38, 42 /home/admin/.local/lib/python3.8/site-packages/httplib2/error.py 25 3 88% 10-12 /home/admin/.local/lib/python3.8/site-packages/httplib2/iri2uri.py 41 30 27% 49-57, 64-72, 76-124 /home/admin/.local/lib/python3.8/site-packages/httplib2/socks.py 244 201 18% 44, 127, 139-142, 155-162, 169-175, 181-183, 190-206, 209-210, 241, 256-352, 358, 364, 371, 378-422, 429-467, 477-518 /home/admin/.local/lib/python3.8/site-packages/imgaug/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/base.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/batches.py 266 116 56% 24, 168, 285-311, 374, 380, 386, 392, 398, 415, 431, 583-610, 624, 630, 717, 751, 771-785, 803-816, 853-888, 943-954, 979-981, 997, 1018-1029, 1051-1068, 1081-1088 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/bbs.py 484 365 25% 49-58, 73-76, 91, 106, 121, 136, 148, 160, 172, 184, 196, 214-218, 249-253, 282, 318-322, 355, 381-387, 406, 433-437, 459-464, 493-501, 521-523, 548-552, 581-585, 592, 614-628, 647, 673-677, 721-723, 784-795, 843-895, 946-957, 1003-1085, 1101-1103, 1122-1124, 1158-1173, 1201-1203, 1228-1244, 1277, 1319, 1332, 1345, 1348, 1351, 1382-1383, 1397, 1411, 1425, 1439, 1451, 1473-1481, 1500, 1529-1544, 1574-1580, 1601-1606, 1620, 1652-1670, 1694-1695, 1735-1748, 1773-1778, 1800, 1825, 1847, 1854, 1870-1876, 1887, 1913-1915, 1958-1960, 1974-1989, 2015-2028, 2042-2045, 2066-2072, 2094-2100, 2113, 2128, 2141, 2144, 2147, 2158-2165, 2169-2187, 2190, 2194-2195, 2203-2227, 2230-2246, 2251-2258, 2261-2271 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/heatmaps.py 143 116 19% 45-96, 113-125, 156-187, 221-254, 280-286, 323-333, 381-396, 413-414, 435-436, 446, 467-475, 494-496, 536-537, 580-584, 614-659, 671, 682 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/kps.py 364 260 29% 37-67, 100-102, 114, 126, 140, 154, 184-186, 213, 235-239, 265, 287-289, 308, 347-408, 456-487, 512-526, 554, 575, 601, 604, 647, 661, 673, 685, 718-726, 744, 785-790, 819, 845, 863, 879, 901-903, 922, 936, 975, 996-1006, 1077-1097, 1139-1184, 1214-1228, 1280-1334, 1351, 1374-1384, 1405-1411, 1452, 1467, 1480, 1483, 1486 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/lines.py 517 418 19% 48-67, 80-82, 94, 106, 122, 138, 153-155, 170-172, 192-198, 211-213, 240-262, 285-288, 313, 343-344, 371, 400-409, 431-433, 456-463, 495-502, 520-614, 634-671, 697-699, 742-744, 772-778, 812-823, 854-864, 907-923, 967-1052, 1097-1104, 1176-1210, 1266-1305, 1325-1327, 1356-1360, 1372-1373, 1385-1390, 1405-1406, 1443-1444, 1483-1484, 1520-1539, 1563-1565, 1587, 1609, 1624, 1637, 1640, 1643-1645, 1690-1698, 1712, 1726, 1738, 1760-1768, 1787, 1815-1818, 1837-1838, 1913-1923, 1948-1951, 1973, 1998, 2018, 2046-2050, 2076, 2102-2104, 2147-2149, 2163-2165, 2191-2216, 2232-2238, 2262-2277, 2302-2308, 2334-2340, 2353, 2368, 2381, 2384, 2387, 2392-2397, 2401-2414, 2418-2442 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/normalization.py 574 394 31% 14, 16-19, 29-32, 37-46, 64-65, 77-87, 98-99, 111-121, 132, 134-138, 149, 153, 171-187, 203-230, 245, 247-250, 256-257, 260-261, 264-301, 319-384, 433-516, 527-530, 532-565, 570-575, 583, 597-626, 638-669, 675-679, 681-686, 690-700, 703-713, 720-762, 773-861, 898-1040, 1193-1202, 1223-1252, 1262, 1265, 1273-1281, 1286-1288 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/polys.py 731 611 16% 49-70, 104-142, 157, 169, 181, 197, 213, 231-233, 245-248, 263-264, 279-280, 310-311, 338, 369-380, 402-405, 435-438, 459, 480, 511-536, 543, 575-650, 676-678, 721-723, 818-902, 928-957, 993-1008, 1031-1041, 1062-1069, 1089, 1101-1103, 1125, 1138-1142, 1157-1159, 1178-1181, 1208-1223, 1250, 1297-1306, 1340-1342, 1364, 1385, 1400, 1413, 1416, 1419-1423, 1455-1456, 1470, 1484, 1496, 1517-1525, 1543, 1637-1653, 1678-1682, 1702, 1725, 1747, 1777-1781, 1806, 1831-1833, 1876-1878, 1896-1898, 1916, 1930-1932, 1964-1988, 2003-2009, 2032-2047, 2072-2078, 2104-2110, 2123, 2138, 2151, 2154, 2157, 2164-2182, 2188-2230, 2233-2281, 2284-2299, 2303-2317, 2321-2328, 2331-2392, 2397-2407, 2413-2526, 2531-2573, 2579-2589, 2592-2760, 2777-2782, 2806-2829 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/segmaps.py 124 101 19% 21, 103-167, 195-205, 211, 236-255, 312-381, 418-421, 466-476, 482, 505-507, 536-540, 568-572 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/utils.py 114 88 23% 12, 25-37, 57-59, 90-111, 139-142, 167-173, 204-218, 252-270, 290-296, 323-334, 339-342, 348-361 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/__init__.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/arithmetic.py 791 668 16% 101-118, 130-161, 165-204, 247-261, 274-283, 298-325, 383-400, 411-442, 448-492, 548-565, 571-583, 587-628, 686, 754-781, 816-847, 877-893, 943-1004, 1038, 1132-1186, 1190-1193, 1199-1202, 1207-1211, 1236-1252, 1259-1261, 1268-1281, 1287-1309, 1335, 1369, 1407-1470, 1552-1559, 1564-1607, 1611, 1698-1705, 1710-1729, 1733, 1836-1845, 1959-1968, 2075-2081, 2167-2174, 2179-2221, 2225, 2311-2318, 2323-2353, 2357, 2365-2373, 2554-2570, 2576-2587, 2591-2627, 2631-2663, 2680-2698, 2703, 2792-2794, 2803-2834, 2993-3010, 3113-3128, 3132-3165, 3171-3209, 3214, 3301-3315, 3319-3353, 3357-3359, 3364-3366, 3371, 3483-3491, 3496-3537, 3541, 3612, 3675, 3816-3831, 3905-3913, 4035-4055, 4132-4139, 4259-4279, 4414-4431, 4436-4460, 4464-4485, 4496, 4505-4510, 4579, 4664-4665, 4742-4748, 4754-4765, 4769 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/artistic.py 105 84 20% 99-157, 164-170, 175-189, 194-198, 203-206, 211-221, 226-239, 244-246, 365-381, 385-396, 400-401, 413 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/base.py 14 7 50% 21-27, 43, 49 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/blend.py 584 447 23% 38-43, 111-211, 216-242, 247-258, 403-422, 426-472, 476, 481, 486, 491-498, 616-648, 652-695, 704-718, 724-785, 789, 794, 799, 804-810, 958-962, 970, 1160-1190, 1411-1442, 1581, 1704, 1833, 1962, 2064, 2196, 2325, 2407-2409, 2418-2423, 2429-2451, 2562-2578, 2586-2592, 2598, 2607-2626, 2667-2684, 2690-2694, 2701-2703, 2708-2735, 2740-2744, 2752-2762, 2773-2777, 2783, 2792-2801, 2842-2881, 2942, 2982, 3041, 3081, 3134-3142, 3153-3158, 3164-3175, 3205-3242, 3275, 3291, 3305, 3315-3320, 3348-3359, 3434-3450, 3462-3469, 3475-3490, 3518-3528, 3598-3614, 3626-3637, 3643-3658, 3686-3699, 3724-3725, 3734-3740, 3758, 3782, 3809, 3842 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/blur.py 259 182 30% 174-181, 186-215, 219, 221, 223, 225, 242, 263, 265, 329-367, 372, 376, 461, 473, 570-616, 622-686, 690, 768-781, 787-815, 819, 932-942, 949-975, 979, 1075-1088, 1098-1101, 1106-1134, 1213-1220, 1227-1236, 1240-1241, 1251 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/collections.py 50 33 34% 192-218, 233, 254-293, 340-341 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/color.py 642 462 28% 69-72, 240-310, 374-395, 405-407, 414, 436-454, 459-853, 888-950, 983, 994, 1063-1069, 1073-1093, 1096-1100, 1104, 1108, 1111, 1226-1234, 1238-1266, 1270-1277, 1282-1285, 1289-1293, 1298, 1303, 1307, 1396-1402, 1414, 1497, 1571, 1666-1675, 1679-1688, 1692-1712, 1716-1744, 1747-1751, 1755, 1759, 1762, 1891-1974, 2048, 2117, 2196-2204, 2380-2398, 2401-2438, 2442-2483, 2501-2511, 2516-2521, 2525, 2530-2543, 2547-2554, 2558-2564, 2572-2582, 2653, 2726, 2858-2905, 2908-2913, 2917-2939, 2943, 3021, 3076-3083, 3087-3095, 3100, 3115-3126, 3129-3139, 3143-3153, 3157-3210, 3218, 3228-3238, 3380, 3396, 3399, 3409, 3484-3526, 3663, 3679, 3682, 3824, 3836, 3858, 3889, 3957-3974, 3993-3995, 4015-4017, 4023, 4031, 4039-4053, 4081, 4133-4136, 4165 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/contrast.py 303 239 21% 37-45, 49-90, 94, 149-172, 233-258, 318-340, 388-421, 489-493, 589-599, 676-681, 754-761, 788-803, 806-898, 902, 1001-1012, 1017-1082, 1086, 1265-1275, 1280-1309, 1313-1315, 1388, 1394-1420, 1424, 1541-1549, 1554-1583, 1587-1588 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/convolutional.py 149 115 23% 126-143, 150-234, 238, 313-322, 330-331, 334-353, 424-433, 441-442, 445-464, 521-527, 535, 538-556, 654-664, 672-673, 676-717 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/debug.py 470 319 32% 42-77, 138-140, 145, 150, 154-159, 171, 176, 181-184, 188-191, 203-205, 210, 215, 219-250, 255-256, 271-272, 277, 282, 286-294, 305, 314, 323, 332, 340-341, 358-359, 367-373, 470-496, 502, 509, 515-527, 533-558, 564-593, 599-611, 617-671, 677-690, 696-708, 714-756, 762-825, 832-843, 849-850, 863, 868, 873, 878-882, 887, 892-894, 899-901, 906-908, 913-915, 920, 925-926, 931-932, 937, 942-943, 948-949, 953-957, 961-965, 970, 975, 980, 985-987, 992, 997, 1002, 1007-1010, 1051, 1055-1056, 1060-1061, 1071-1075, 1079-1080, 1086, 1123-1124, 1128-1130, 1170-1174, 1178-1193, 1256-1266, 1273-1274 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/edges.py 94 68 28% 93-101, 110-114, 117-159, 162, 327-375, 382-415, 419-468, 472, 476 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/flip.py 114 92 19% 722-724, 728, 734-761, 805, 811, 817, 872-875, 879-925, 929, 984-987, 991-1039, 1043 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/geometric.py 1751 1369 22% 92-96, 102, 106-120, 129, 139, 143-150, 158, 162, 169, 185-188, 215-244, 267, 269, 298-312, 315, 317, 323-351, 399-443, 475-508, 543-582, 607, 611, 639, 672, 681, 1178-1181, 1224-1229, 1253-1268, 1279-1284, 1309-1314, 1340, 1345, 1366-1371, 1392, 1414, 1417, 1422-1424, 1427, 1434-1473, 1479, 1489, 1503, 1513-1515, 1536, 1607, 1684, 1771-1774, 1862-1865, 1941, 2016, 2091, 2405-2571, 2576-2583, 2591-2676, 2679-2691, 2694-2707, 2711-2774, 2778, 2783, 2788, 2793, 2797-2841, 2849-2854, 2857-2860, 3009-3043, 3047-3081, 3085-3128, 3133-3169, 3174-3242, 3245-3271, 3287-3354, 3358, 3365-3369, 3551-3586, 3592-3621, 3632-3681, 3685-3753, 3758-3817, 3821-3847, 3852-3962, 3972-3988, 3993-4011, 4015, 4021-4026, 4239-4269, 4273-4275, 4281-4293, 4298-4304, 4310-4358, 4363-4379, 4385-4433, 4438-4496, 4501-4503, 4509-4511, 4516-4518, 4522, 4527-4554, 4672-4793, 4901-4911, 4914, 4919-4943, 4948-4966, 4971-4991, 4996-5029, 5033, 5165-5168, 5172-5204, 5209-5238, 5243-5278, 5283, 5288, 5293, 5298, 5304, 5310, 5316, 5321, 5326-5328, 5333-5335, 5340, 5345, 5350, 5355, 5360-5413, 5419-5476, 5481-5505, 5511-5532, 5537-5560, 5566-5589, 5594-5608, 5613-5629, 5635-5654, 5667-5707, 5712, 5717, 5721-5725, 5729-5733, 5849-5862, 5866-5935, 5939-5955, 5961-5970, 5976-5986, 5991-5996, 6001-6007, 6011, 6018-6021 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/imgcorruptlike.py 197 121 39% 96-112, 144-212, 243-257, 297, 329, 361, 393, 425, 457, 470-515, 550, 582, 614, 646, 678, 710, 742, 774, 806, 838, 870, 902, 934, 1009-1014, 1020-1029, 1033-1037, 1042, 1098, 1157, 1216, 1275, 1334, 1393, 1452, 1511, 1570, 1629, 1688, 1747, 1806, 1865, 1924, 1983, 2042, 2101, 2164, 2171-2177 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/meta.py 1062 774 27% 47, 53, 59-61, 68-72, 77-100, 108-117, 122-125, 130-142, 147-149, 153-157, 166-173, 207-210, 215, 219, 296, 299-304, 313-314, 320, 370-528, 550, 598-602, 610-616, 622-624, 626-632, 649, 654-659, 668-670, 713-737, 762-771, 821-822, 874, 901, 940, 966, 1010, 1071, 1116, 1180, 1246, 1315, 1364, 1406, 1449, 1479, 1524-1529, 1558, 1592-1596, 1631-1656, 1682-1687, 1933-1934, 1945-1957, 1984, 1995-2000, 2003, 2081-2082, 2139-2186, 2213-2214, 2252-2256, 2272-2287, 2296, 2357-2373, 2393-2397, 2443-2449, 2479-2487, 2552-2613, 2627, 2664, 2686-2696, 2740-2757, 2780, 2805-2813, 2871-2888, 2897, 2935-2947, 2958, 2975, 2978, 2981-2983, 3094, 3100, 3107, 3119, 3132-3137, 3141, 3152, 3156, 3159-3164, 3287-3315, 3319-3343, 3346-3355, 3358-3362, 3366-3374, 3378-3411, 3414-3419, 3423, 3434, 3438, 3441-3446, 3512, 3599-3607, 3612-3641, 3644-3653, 3657, 3661-3666, 3669-3673, 3746-3766, 3770-3811, 3816, 3824-3831, 3841-3849, 3859-3861, 3865-3869, 3873-3878, 3882-3898, 3901-3905, 3909, 3913, 3916-3920, 3980, 3986, 3991, 4035, 4222-4231, 4234-4236, 4239-4252, 4255-4269, 4273-4287, 4291-4311, 4316-4333, 4338-4363, 4367, 4497-4504, 4521-4522, 4526-4529, 4669-4677, 4697-4718, 4725-4733, 4741, 4746-4748, 4754, 4757-4759, 4765, 4768-4770, 4776, 4779-4781, 4787, 4791-4794, 4800, 4803-4805, 4811, 4815-4818, 4892-4906, 4910-4922, 4926, 4972-4992, 5088-5092, 5096-5103, 5108, 5176, 5182-5188, 5193 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/pillike.py 343 243 29% 80-90, 122, 151, 182, 211, 244-249, 294-313, 320-347, 352-362, 415-426, 432, 446-531, 536-554, 597, 641, 684, 727, 732-750, 787, 824, 861, 898, 936, 973, 1010, 1047, 1084, 1121, 1132-1189, 1278-1316, 1373, 1444, 1451-1454, 1459, 1533-1540, 1558-1562, 1568-1574, 1578, 1583, 1643, 1709, 1775, 1841, 1855-1858, 1862-1865, 1870, 1917, 1967, 2018, 2070, 2122, 2173, 2224, 2274, 2324, 2375, 2489-2503, 2507-2513, 2520-2544, 2549-2567, 2572 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/pooling.py 113 76 33% 26-36, 49-59, 66-76, 83-92, 96-111, 115, 120, 126-150, 154-168, 172-174, 179-181, 186-188, 193, 312, 318, 433, 441, 556, 564, 679, 687 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/segmentation.py 353 269 24% 56-66, 212-222, 226-283, 287-319, 323, 357-378, 383-388, 392-393, 398-416, 421-441, 598-611, 615-634, 637-654, 658, 776, 940, 1122, 1174-1187, 1245-1248, 1253-1257, 1260-1263, 1267-1279, 1283-1286, 1290-1313, 1316, 1319, 1377-1381, 1387-1391, 1396-1410, 1413, 1417, 1463-1468, 1474-1495, 1498-1505, 1508-1512, 1515-1518, 1522-1538, 1541, 1545, 1580, 1585-1596, 1600-1603, 1608-1620, 1623, 1626, 1664-1671, 1676-1682, 1688-1692, 1695, 1699 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/size.py 1205 1047 13% 48-66, 70-72, 77-90, 95, 102, 110-147, 154-166, 170-174, 179-219, 225-253, 257, 262-279, 285-330, 348-350, 425-553, 607-621, 680-695, 734-766, 807-838, 874-902, 937-965, 1007-1037, 1084-1115, 1123, 1267-1272, 1276-1359, 1363-1378, 1382-1410, 1414-1434, 1438-1461, 1465-1475, 1478-1490, 1494-1529, 1533, 1539-1548, 1553, 1558, 1804-1828, 1833-1847, 1852-1902, 1906-1964, 1968-1997, 2001-2019, 2024-2043, 2047-2056, 2059-2169, 2173, 2377-2399, 2563-2585, 2731-2756, 2764-2790, 2794-2812, 2816-2831, 2836-2860, 2863-2884, 2889-2904, 2908, 2972, 3109-3121, 3129-3153, 3157-3171, 3175-3189, 3193-3204, 3210-3223, 3226-3245, 3249, 3310, 3381-3386, 3390-3412, 3417, 3477, 3551-3557, 3561-3583, 3588, 3657, 3739-3744, 3748-3770, 3775, 3835, 3914-3920, 3924-3946, 3951, 4019, 4085-4089, 4093-4118, 4123, 4180, 4248-4253, 4257-4279, 4284, 4340, 4399, 4461, 4523, 4579, 4701-4737, 4742-4781, 4787-4807, 4813-4825, 4830-4838, 4843-4855, 4858-4901, 4904-4908, 4912, 4916, 4919-4927 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/weather.py 226 166 27% 140-150, 153-159, 163-186, 190, 353-366, 371-379, 383, 394-414, 422-451, 457-466, 471-478, 484-495, 563-594, 666, 855-876, 880-888, 892, 903-953, 957-958, 965-969, 975, 979-988, 997-1004, 1012-1017, 1022-1023, 1028-1029, 1204-1217, 1301, 1311, 1317-1319, 1328-1339, 1428-1441 /home/admin/.local/lib/python3.8/site-packages/imgaug/dtypes.py 148 109 26% 20, 35-49, 54-97, 104, 108-112, 116-135, 150-172, 177-183, 187-189, 196-197, 201-207, 217-253, 258-282, 319-345 /home/admin/.local/lib/python3.8/site-packages/imgaug/external/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/external/opensimplex.py 1412 1385 2% 14-15, 85, 100-113, 116-120, 123-129, 132-140, 148-244, 252-740, 748-1934 /home/admin/.local/lib/python3.8/site-packages/imgaug/imgaug.py 599 461 23% 14-15, 86-87, 106, 147, 165-187, 192, 365, 382, 400-402, 419, 440-448, 462, 491-506, 525-526, 541-542, 567-572, 586-587, 610-613, 632-633, 656-657, 670-671, 703-726, 761-802, 839-846, 866, 889-906, 928-957, 983-1003, 1029-1056, 1083-1106, 1142-1146, 1200-1216, 1273-1306, 1403-1572, 1576-1578, 1608-1619, 1691-1744, 1791, 1838, 1882, 1926, 1976-2034, 2061-2062, 2095-2123, 2142-2143, 2150-2151, 2153, 2180, 2227-2273, 2352-2355, 2369-2371, 2389-2391, 2402-2404, 2415-2417, 2455-2461 /home/admin/.local/lib/python3.8/site-packages/imgaug/parameters.py 1056 827 22% 37, 40-43, 51-62, 74-99, 112-153, 161-187, 197-247, 254-293, 299-301, 306-314, 320-336, 342, 387, 420, 423-425, 431-433, 439-441, 447-453, 459-461, 467-469, 475-477, 483-485, 491-493, 499-501, 507-513, 519-521, 527-529, 535-537, 551, 562, 594-628, 659, 663, 675, 678-682, 706-727, 731-739, 743, 747-750, 787-799, 802-856, 859, 862, 899-900, 903-907, 910, 913, 955-958, 962-968, 971, 974, 1014-1016, 1019-1022, 1025, 1028, 1068-1071, 1075-1080, 1083, 1086, 1143-1149, 1153-1170, 1173, 1176, 1220-1223, 1227-1232, 1235, 1238, 1273-1275, 1279-1281, 1284, 1287, 1323-1325, 1329-1331, 1334, 1337, 1379-1382, 1386-1392, 1395, 1398, 1438-1445, 1448-1452, 1455, 1458, 1538-1589, 1592-1653, 1656, 1659-1670, 1703-1715, 1718-1723, 1726, 1729-1737, 1766-1769, 1772-1791, 1794, 1797-1798, 1846-1850, 1853-1867, 1870, 1873, 1926-1930, 1934-1958, 1961, 1964, 2018-2022, 2025-2038, 2041, 2044, 2093-2097, 2100-2113, 2116, 2119, 2169-2173, 2176-2202, 2205, 2208, 2229-2233, 2236-2237, 2240, 2243-2244, 2269-2280, 2283-2300, 2303, 2306-2307, 2347-2364, 2367-2404, 2407, 2410-2411, 2449, 2491, 2570-2622, 2628-2659, 2662, 2665-2666, 2731-2746, 2771, 2774-2789, 2792, 2795-2796, 2870-2890, 2895-2906, 2909-2934, 2937-2977, 2980, 2983, 3072-3093, 3103-3114, 3117-3177, 3181-3188, 3191, 3194, 3201 /home/admin/.local/lib/python3.8/site-packages/imgaug/random.py 378 202 47% 67, 172, 185, 202-203, 224, 241, 256, 272, 293, 304-305, 318, 336, 348-352, 384, 396-398, 438, 455, 476, 489, 496, 500, 507, 512, 517, 521, 526, 530, 534, 538, 542, 546, 550, 554, 559, 563, 567, 571, 575, 580, 585, 590, 594, 599, 604, 608, 613, 617, 621, 633-643, 654-665, 676-686, 691, 695, 700, 705, 709, 714, 719, 738, 756, 770, 783-785, 798, 811-814, 831, 880-883, 891, 895, 940, 961, 970, 975-978, 985-992, 996-1003, 1022, 1032, 1065-1066, 1080-1082, 1087, 1091, 1154-1156, 1163-1166, 1170-1173, 1196-1198, 1219, 1237-1240, 1260, 1284-1286, 1314-1319, 1324-1325, 1346-1348, 1356, 1373-1376, 1384, 1405-1407, 1411-1431, 1435-1444, 1466, 1477-1478, 1515-1519, 1521, 1550-1561, 1586-1587, 1590-1592, 1595-1596 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_common.py 101 56 45% 35-46, 56, 68-72, 77, 82, 87, 95-104, 112-114, 129-141, 145, 156-158, 167, 176, 184-185, 194-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_compat.py 58 36 38% 13, 20-23, 28-29, 42, 46, 49-75, 101-103, 107, 117-126 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/abc.py 65 23 65% 26, 39, 47, 52, 79-80, 86-87, 109-124, 130, 161, 164, 167, 170 /home/admin/.local/lib/python3.8/site-packages/jmespath/__init__.py 12 4 67% 10-12, 19, 23 /home/admin/.local/lib/python3.8/site-packages/jmespath/ast.py 44 22 50% 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46, 50, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90 /home/admin/.local/lib/python3.8/site-packages/jmespath/compat.py 40 21 48% 16-48 /home/admin/.local/lib/python3.8/site-packages/jmespath/exceptions.py 68 36 47% 13-19, 23-24, 34-37, 41-42, 50-57, 60-61, 68-71, 74, 82-85, 91, 103-106, 109, 117 /home/admin/.local/lib/python3.8/site-packages/jmespath/functions.py 228 141 38% 73-81, 84-91, 94-97, 104-120, 124-134, 137-161, 166, 170-173, 177-179, 183-186, 190-193, 198-211, 215, 219, 223, 227, 231-234, 238, 242, 246, 250-253, 257-260, 264-267, 271-274, 278, 282, 288, 292, 296-307, 311-327, 331-337, 341-347, 350-359, 362 /home/admin/.local/lib/python3.8/site-packages/jmespath/lexer.py 139 120 14% 27-110, 114-118, 121-127, 130-135, 140-156, 159-176, 180-188, 193-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/jmespath/parser.py 313 244 22% 79-82, 85-92, 95-105, 108-116, 119-135, 138, 141, 144-152, 155-160, 163, 166, 169-171, 174-177, 180-181, 184-198, 205-213, 219-237, 240, 243-244, 247-259, 262-263, 266-267, 270-271, 274-291, 295-301, 304, 307, 310, 313, 316, 319, 322-325, 328-344, 347-353, 356-357, 360-369, 372-389, 393-406, 417-434, 437-440, 443, 447-451, 455-458, 461, 464, 467, 470, 473-476, 480-488, 492-493, 498, 504-505, 508-510, 522-524, 527 /home/admin/.local/lib/python3.8/site-packages/jmespath/visitor.py 212 161 24% 9-12, 32-35, 43, 54-56, 70-71, 76-77, 80, 85, 88-94, 97, 113-123, 126, 129-132, 135-138, 142-158, 161, 164, 167-171, 174-184, 187-197, 200, 205-210, 213-216, 219-222, 225, 228, 231-236, 239-244, 247-250, 253-256, 259-264, 267-270, 273-281, 284-294, 300, 304, 309-311, 314-319, 322-328 /home/admin/.local/lib/python3.8/site-packages/lxml/__init__.py 11 9 18% 12-22 /home/admin/.local/lib/python3.8/site-packages/matplotlib/__init__.py 517 265 49% 165-178, 190-191, 223, 240-243, 276-277, 356-460, 465-480, 505, 511, 514-515, 521-537, 608-609, 617, 702-706, 708-709, 711-714, 717-718, 721-722, 724-725, 731-734, 737-740, 745-748, 758-764, 767, 775, 788-789, 803, 808-814, 821-828, 863-865, 872, 875-878, 889-902, 927-945, 977, 1033-1052, 1074-1077, 1090-1092, 1115-1119, 1168-1177, 1233-1240, 1252, 1263, 1270, 1288-1293, 1308-1316, 1320-1325, 1348, 1366, 1368, 1448-1472 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_afm.py 242 190 21% 54, 61-65, 69, 73-74, 78, 82-85, 105-168, 206-237, 252-269, 306-323, 339-355, 362-364, 367-369, 376-394, 398-424, 428, 432-434, 440-442, 446, 450-452, 458-459, 466, 470, 474, 478-481, 485-493, 498, 502, 506, 510, 514, 518, 525, 532 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/__init__.py 126 30 76% 47, 58, 83, 89-93, 124, 128-131, 158-168, 187, 191-192, 256, 270, 281, 336, 341, 357-359, 382 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/deprecation.py 173 33 81% 28-29, 142-143, 156-159, 162-164, 167-169, 292-296, 310, 370-373, 387, 392, 400-403, 449, 486-503 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_blocking_input.py 8 7 12% 21-30 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm.py 141 12 91% 59-64, 145-152 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm_listed.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_color_data.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_constrained_layout.py 373 352 6% 102-149, 162-194, 202-240, 247-260, 264-297, 303-335, 347-440, 447-479, 507-576, 583-596, 615-624, 632-665, 689-751, 761-768, 772-783 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_docstring.py 39 4 90% 35, 53, 59-60 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_enums.py 57 36 37% 24, 89-111, 161-177 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_fontconfig_pattern.py 46 7 85% 89-91, 97, 101-105, 114-118 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_layoutgrid.py 208 174 16% 40-103, 106-118, 126-128, 132-137, 144-162, 166, 173-206, 213-245, 266-267, 287-288, 303-304, 322-323, 339-347, 352, 359-367, 374-391, 398-411, 418-429, 436-448, 455-466, 473-484, 490, 497, 502-547 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext.py 1244 988 21% 57-66, 100-102, 105-111, 116-147, 170-171, 180, 218-219, 227-228, 234, 240, 247, 255, 264, 274-282, 285-295, 298-300, 304-323, 334-343, 349, 353-358, 380-387, 392-405, 464, 489-519, 524, 527-586, 589-592, 599-617, 621-632, 699-704, 709-753, 757-773, 912-919, 926, 929, 932, 939, 949-952, 955-959, 962, 969, 976, 993-1002, 1005, 1008-1015, 1018, 1027-1034, 1037, 1042-1047, 1057-1061, 1064-1065, 1068, 1077-1083, 1086, 1093-1104, 1108-1113, 1120-1123, 1133-1148, 1187-1226, 1233-1234, 1258-1305, 1320-1321, 1324, 1331-1334, 1341-1342, 1368-1375, 1378-1381, 1391, 1401, 1419-1420, 1423, 1426-1428, 1441-1467, 1480-1495, 1508-1637, 1646-1649, 1664-1668, 1671, 1675, 1679-1681, 1685, 1701-1711, 1800-1955, 1964-1977, 1981, 1985, 1989, 1992, 1995, 1998-2000, 2003-2009, 2019-2028, 2046-2048, 2051, 2054-2096, 2099, 2127-2141, 2148-2150, 2153-2185, 2188-2192, 2195-2196, 2199, 2204-2205, 2208-2209, 2212-2216, 2219-2221, 2224-2226, 2229, 2232-2391, 2394-2429, 2432, 2435, 2441, 2446, 2451, 2456-2483, 2488-2525, 2528-2544, 2547-2566, 2569 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext_data.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_pylab_helpers.py 67 27 60% 41, 55-67, 72-75, 80-83, 88, 93, 98, 115, 130-132 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_text_helpers.py 23 1 96% 34 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_bbox.py 47 44 6% 18-70, 80-84 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_layout.py 133 125 6% 48-157, 170-191, 226-301 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_type1font.py 396 320 19% 56-58, 61, 65, 69, 73, 77, 81, 84, 91, 94, 101, 108, 115, 118, 137-141, 145-151, 158, 165, 168-171, 190-270, 294-315, 364-373, 377-402, 415-441, 457-463, 482-489, 498-592, 595-626, 630-653, 660-680, 684-692, 714-770 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_version.py 11 2 82% 5-6 /home/admin/.local/lib/python3.8/site-packages/matplotlib/artist.py 664 260 61% 36-37, 58-61, 66-67, 70, 75, 77, 81-82, 97-98, 113, 145, 217-221, 241-259, 304, 317, 327, 350, 367-376, 405, 415, 457, 462, 483-485, 504-508, 518, 531-555, 590, 602, 616, 630, 641, 713-717, 727-728, 754, 813-814, 816, 822-823, 825-826, 828-829, 832, 864, 879-881, 892, 896, 908-910, 959-964, 985-986, 1003-1005, 1017, 1020, 1036, 1040-1046, 1076-1078, 1127, 1178, 1193, 1197, 1271-1286, 1317, 1337-1373, 1397-1403, 1414-1417, 1435-1437, 1441, 1485, 1493, 1515-1519, 1596-1600, 1614, 1616-1617, 1635-1666, 1683-1700, 1704-1715, 1746-1751, 1816-1838 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/__init__.py 9 1 89% 10 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py 2254 1885 16% 98-102, 158, 172, 179, 193-195, 317, 323, 381-398, 461-511, 547-550, 585-591, 617-623, 704, 761-776, 829-844, 849-851, 904-926, 966-974, 1022-1031, 1072-1111, 1152-1191, 1304-1437, 1772-1776, 1821-1829, 1872-1876, 1919-1923, 1996, 2073-2105, 2174-2176, 2190-2228, 2340-2525, 2640-2643, 2697-2810, 2856-2878, 2972-3063, 3185, 3188, 3194-3195, 3199, 3201, 3203, 3207-3210, 3216, 3260-3261, 3277-3280, 3292, 3299, 3310-3333, 3475-3704, 3903-4014, 4116-4301, 4366, 4373-4374, 4379, 4384, 4397, 4404-4430, 4434-4442, 4449-4451, 4578, 4587, 4594, 4601-4602, 4612, 4621, 4628-4661, 4678-4681, 4688-4689, 4700-4703, 4867-5099, 5130-5138, 5142-5144, 5148-5152, 5160-5164, 5172-5176, 5224-5230, 5321-5421, 5425, 5439, 5653-5676, 5685-5792, 5941-6026, 6213-6253, 6367-6435, 6448-6451, 6464-6467, 6487, 6685-6956, 7001-7033, 7128-7140, 7225-7251, 7331-7353, 7419-7439, 7497-7508, 7566-7577, 7630-7641, 7743-7797, 7878-7936, 7974-7987, 8079-8090, 8182-8259, 8281-8284 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_base.py 1737 837 52% 63, 87, 109-110, 116, 143-208, 229, 232-233, 246, 249, 254-294, 299, 309-310, 317, 331-333, 344-345, 356-404, 453-454, 457, 465, 472-490, 496, 504, 507, 517-518, 522, 524, 529-533, 542, 571, 631-636, 639, 641, 646, 663, 693, 695, 737, 749-756, 760-767, 770-784, 788, 797, 879, 943, 1023, 1101-1104, 1129-1132, 1142, 1158-1160, 1170-1171, 1187, 1232-1241, 1251-1260, 1289, 1291, 1324, 1369-1370, 1372-1373, 1380, 1393, 1401-1404, 1445, 1448, 1453-1454, 1457, 1462-1466, 1469-1473, 1477, 1483, 1488, 1496, 1500, 1504, 1508, 1518-1520, 1597-1606, 1669, 1672, 1685, 1700, 1734, 1744, 1764, 1793-1797, 1849-1860, 1922, 1931-2005, 2067, 2069, 2072, 2081, 2083-2084, 2088-2125, 2127, 2132, 2136, 2140, 2151, 2162, 2183-2184, 2186, 2192, 2202, 2219-2225, 2237-2243, 2282-2289, 2292-2293, 2329, 2335-2353, 2356, 2362-2365, 2396, 2413, 2416-2419, 2439-2444, 2455, 2477-2483, 2505, 2545-2546, 2553, 2558-2559, 2561-2566, 2575, 2584, 2618, 2637-2641, 2659-2663, 2717-2738, 2757-2758, 2762, 2786-2807, 2864, 2866, 2916, 2921, 2923, 2943-2944, 2951-2952, 2963, 2967, 2974-2976, 2980-2981, 2983-2990, 3003, 3005, 3035, 3046-3051, 3057, 3062, 3074, 3080-3084, 3088, 3094, 3119, 3243-3270, 3306-3312, 3418-3419, 3449, 3452-3453, 3482, 3500, 3522, 3526, 3528, 3569, 3572, 3645-3647, 3649-3651, 3669-3670, 3700, 3703-3704, 3733, 3751, 3773, 3777, 3779, 3877-3879, 3881-3883, 3907, 3917, 3922, 3934-3944, 3948-3949, 3957, 3963, 3969, 3985, 4011-4013, 4027-4029, 4040-4109, 4147-4154, 4171, 4187, 4197-4249, 4268-4271, 4287-4290, 4297, 4308-4328, 4374-4415, 4420-4436, 4458-4466, 4488-4495, 4499, 4503, 4513-4514, 4518-4535, 4539-4556, 4597-4613 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_secondary_axes.py 113 92 19% 22-55, 68-74, 92-115, 120-121, 125-128, 147-159, 170-173, 180-202, 209-221, 228, 238-247 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axis.py 1252 436 65% 89-96, 194, 198-199, 201, 221, 233-235, 245, 254-257, 267-268, 272, 296-297, 337-340, 349, 360-375, 378, 382, 385-387, 400, 403, 406, 467, 528, 556, 567, 585, 652, 719, 727, 743, 762-768, 781, 816-830, 852, 856-857, 862-863, 949-955, 971, 1022-1027, 1072, 1082-1084, 1088, 1102, 1117, 1121, 1135, 1156-1158, 1202-1206, 1211-1219, 1221, 1241-1244, 1251, 1257-1267, 1287-1289, 1294, 1302-1305, 1315, 1332, 1334, 1358-1363, 1373-1378, 1385, 1407-1408, 1413, 1417, 1421, 1433-1437, 1458-1465, 1467, 1472-1477, 1481-1486, 1490-1492, 1533, 1549-1553, 1559, 1580, 1589, 1597, 1601, 1605, 1649, 1653-1655, 1681-1687, 1692-1694, 1715, 1728, 1733, 1740-1741, 1748-1749, 1751-1752, 1771-1772, 1798, 1807, 1825, 1872, 1877, 1884, 1939, 1949, 2007-2057, 2073, 2080-2081, 2121, 2125, 2140, 2159, 2166, 2180-2183, 2188, 2212-2225, 2241, 2244, 2267, 2313-2327, 2337-2341, 2349, 2360-2362, 2370-2379, 2399-2404, 2413-2428, 2443-2468, 2474-2480, 2486-2492, 2519-2520, 2524-2533, 2573-2587, 2597-2602, 2610, 2620-2622, 2630-2639, 2651, 2665-2670, 2674-2689, 2704-2725, 2731-2738, 2744-2751, 2757, 2773-2780, 2791 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backend_bases.py 1287 740 43% 97-115, 131-134, 142-148, 195, 250-266, 278-285, 305, 323, 345, 366, 406, 426, 431, 438, 442, 455, 487, 497, 504, 527, 566, 588-606, 629-633, 641-663, 671, 675, 679-681, 685, 747-754, 812, 816, 826, 834-841, 851, 858, 866, 878, 889, 954-959, 977, 1004, 1019, 1030, 1034, 1038, 1042, 1079, 1123-1126, 1130, 1142-1144, 1148, 1151, 1154, 1159, 1165-1167, 1172, 1176-1177, 1187-1188, 1200-1211, 1225-1236, 1309-1310, 1340-1367, 1432-1444, 1447, 1494-1499, 1535-1536, 1542-1545, 1551-1571, 1585-1603, 1610, 1622, 1672, 1681, 1709, 1714-1729, 1746-1750, 1761-1762, 1776-1779, 1786-1788, 1796-1799, 1807-1812, 1825-1829, 1837-1841, 1855-1859, 1874-1881, 1896-1900, 1923-1926, 1950-1954, 1970-1972, 1990-1992, 2009-2016, 2025-2027, 2037, 2080-2082, 2095, 2124-2133, 2156, 2162, 2172-2176, 2204-2208, 2216, 2218, 2220, 2235, 2297-2304, 2308, 2326, 2336-2342, 2345-2356, 2375, 2388, 2395-2400, 2411-2413, 2490, 2519, 2546-2554, 2563, 2586-2724, 2734-2743, 2832, 2835, 2840-2842, 2848, 2889-2905, 2915-2921, 2926, 2929, 2939, 2958, 2962, 3020-3036, 3059-3061, 3071-3073, 3083-3085, 3091-3102, 3115-3124, 3128-3146, 3149-3150, 3153-3162, 3170-3180, 3186-3199, 3204-3208, 3212-3221, 3224-3235, 3241-3259, 3265-3280, 3284-3321, 3325-3332, 3339-3350, 3353-3373, 3377, 3381-3382, 3406-3410, 3420, 3435-3445, 3449-3460, 3471, 3501, 3514, 3529, 3540, 3589-3591, 3602-3620, 3641, 3655 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backend_managers.py 152 120 21% 7-10, 16-17, 27-29, 48-60, 65-67, 72, 76, 88-96, 126, 138, 142-147, 152, 169-170, 173-174, 187-196, 208-224, 249-281, 297-324, 341-355, 358-364, 369, 390-398 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backend_tools.py 501 318 37% 54-57, 62-66, 107-109, 124, 128, 139, 156, 165, 197-198, 202-206, 214, 229, 234, 237-252, 263-272, 275-279, 283-287, 291-292, 297-299, 302-311, 321-322, 325-329, 334-339, 346-351, 359, 367, 377, 387, 397-402, 412-417, 427, 434-436, 439-440, 443-444, 454, 464, 482-485, 490-497, 501-505, 515-536, 542-550, 567, 576-580, 584-585, 589-590, 594-595, 604-607, 655-663, 667-672, 677-681, 684-688, 692-716, 729-730, 733-740, 747-774, 777-778, 781-782, 787-796, 802-837, 850-851, 854-858, 861-878, 882-896, 899-903, 917, 921-922, 925, 930-932, 935-939, 951-952, 992-993, 1011-1013 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backends/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/backends/_backend_pdf_ps.py 77 27 65% 18-19, 37-57, 79, 98, 112, 114-115, 117-123, 136-138 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py 239 176 26% 44-56, 79-90, 95, 98, 101-107, 111-187, 191-198, 202-219, 224-240, 245-257, 261, 267-271, 275, 278, 281, 284, 287, 295, 299, 315-334, 340-343, 362-374, 385-386, 389-390, 394-403, 407-415, 424, 433, 442, 445-448, 457-458, 509, 512-514, 525-526, 531, 536 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backends/backend_mixed.py 41 19 54% 77-85, 94-119 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backends/backend_pdf.py 1429 573 60% 98, 157-158, 179-182, 198, 201, 205, 216-231, 239-248, 257-271, 279-293, 317, 324, 367, 374, 388-390, 404, 432, 435, 438, 441, 449, 460, 463, 466, 545, 583, 673, 716-723, 826-831, 862, 887, 890, 930-935, 959-980, 985-987, 993-994, 1004-1013, 1016-1071, 1076-1134, 1289-1429, 1447, 1451, 1455, 1478-1479, 1509-1544, 1557-1559, 1563-1577, 1583-1614, 1635-1638, 1643-1681, 1687-1694, 1702-1717, 1724-1746, 1755-1809, 1813-1819, 1836-1848, 1860-1870, 1886, 1945, 1994, 2012, 2017-2039, 2065-2066, 2071, 2074, 2077, 2081, 2094, 2135-2159, 2162, 2166-2201, 2209-2213, 2217-2267, 2271-2350, 2355, 2364, 2369-2370, 2377-2379, 2385-2388, 2483-2486, 2507, 2534-2538, 2551-2553, 2558, 2599-2600, 2729, 2732, 2743, 2752, 2766-2772, 2792, 2802, 2813, 2823, 2828, 2831-2832 /home/admin/.local/lib/python3.8/site-packages/matplotlib/bezier.py 222 150 32% 42-62, 72-81, 91-92, 100-110, 151-178, 222, 232, 267, 330-337, 348-401, 413-418, 424-429, 451-459, 474-533, 541-543, 554-594 /home/admin/.local/lib/python3.8/site-packages/matplotlib/category.py 85 10 88% 49, 107, 113, 131, 135, 151, 162, 164, 193-195 /home/admin/.local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py 901 563 38% 64-98, 102-105, 207, 220-229, 260, 269, 281-299, 317-321, 336-346, 371-373, 376-380, 386-395, 404-418, 435, 443, 451-456, 489, 492, 496-497, 501-505, 508, 513-514, 536-557, 584-588, 603-604, 607-609, 620-621, 625-628, 631, 634, 638-639, 643-645, 653-655, 663-666, 670, 674-675, 686-698, 709-715, 723, 748-798, 846-847, 853-865, 869-870, 874-877, 885-888, 901, 942-945, 981-1023, 1058, 1060, 1070, 1073-1076, 1081, 1084-1087, 1171-1289, 1303-1321, 1360, 1376-1420, 1476-1518, 1549-1556, 1587-1592, 1623-1630, 1661-1672, 1697, 1711-1715, 1720, 1722, 1766, 1770, 1784, 1808-1828, 1841, 1895-1897, 1945-1949, 1976-2000, 2034, 2053, 2056, 2059, 2062-2063, 2066, 2077-2088, 2095-2108, 2119-2126, 2131, 2145-2166, 2185-2188, 2197, 2205-2215, 2229-2243, 2269, 2280, 2295-2297, 2307, 2309-2313, 2338-2341 /home/admin/.local/lib/python3.8/site-packages/matplotlib/cm.py 213 91 57% 91, 102, 133-146, 178-181, 200-210, 254-257, 285-293, 341-343, 364-371, 415-425, 464, 469-472, 475, 479-483, 486-495, 509-518, 527, 531, 537, 561, 563, 573, 587, 598-606, 610, 615, 620, 643-647, 654-658, 665-666, 719 /home/admin/.local/lib/python3.8/site-packages/matplotlib/collections.py 835 494 41% 182, 187, 195, 205, 208, 216, 219, 232, 257, 263, 266, 277-280, 300, 305, 328-330, 332-334, 346, 358-359, 362, 365-366, 384-391, 394, 397, 400-405, 434, 443-471, 494, 535, 545-553, 612-617, 635, 638, 649, 652, 677, 680-684, 722-723, 727, 731, 749, 758, 764, 769-775, 777-779, 799, 822, 825, 847-853, 869, 873-887, 890, 894, 901, 906-925, 943, 958-959, 1069-1144, 1170-1173, 1189-1215, 1221-1226, 1238-1244, 1264-1271, 1315-1320, 1323, 1326, 1330-1337, 1408-1412, 1415-1421, 1434-1448, 1451, 1454, 1457, 1460, 1473, 1478, 1541-1549, 1555-1556, 1560-1571, 1575-1580, 1585, 1591, 1598-1603, 1613-1618, 1622, 1626-1635, 1639, 1643-1652, 1656, 1659, 1663, 1680-1683, 1711-1718, 1723-1760, 1764-1765, 1807-1821, 1824-1826, 1836-1846, 1849-1851, 1854, 1864-1866, 1870-1889, 1928-1940, 1943-1945, 1948-1949, 1973-1986, 1989, 1999, 2009-2020, 2028-2058, 2062-2107, 2110-2113 /home/admin/.local/lib/python3.8/site-packages/matplotlib/colorbar.py 698 608 13% 120, 125-127, 133, 136-138, 141-143, 151-152, 155-186, 190, 301-440, 446, 450-451, 456, 460-461, 466, 470-471, 476, 480-481, 485-489, 506-519, 527, 534-579, 584-601, 604-625, 628-644, 653-738, 765-818, 825-828, 840-876, 894-901, 912-915, 950, 956-957, 961-962, 985-989, 998, 1026, 1035-1063, 1071-1116, 1125-1148, 1152-1160, 1164-1165, 1173-1199, 1206-1219, 1228-1233, 1240-1271, 1280-1296, 1300-1301, 1305-1306, 1310-1312, 1316-1318, 1323, 1328, 1334-1339, 1343-1348, 1355-1371, 1375, 1381, 1409-1488, 1524-1594 /home/admin/.local/lib/python3.8/site-packages/matplotlib/colors.py 1035 651 37% 66-67, 70-71, 93, 137-140, 143, 146, 149, 172-184, 194-197, 206-210, 243, 252-262, 320, 344, 350-354, 358-362, 370, 378, 381, 395, 432, 438-441, 445-446, 450, 452, 454, 464, 471, 474, 483, 487, 490, 515-518, 608-644, 706-760, 767, 771-779, 783-785, 789-791, 795-797, 801-803, 807-809, 813-815, 822-827, 835-837, 840-848, 852, 856-858, 863-872, 891, 895-907, 911-923, 1009-1020, 1024-1025, 1047, 1068-1073, 1078, 1147-1148, 1151, 1155-1161, 1165-1168, 1172-1178, 1243, 1247-1250, 1254, 1258-1261, 1265, 1269-1271, 1278, 1301-1313, 1333-1359, 1362-1371, 1375-1380, 1384-1388, 1392, 1428-1434, 1439, 1443-1445, 1451-1455, 1461-1473, 1476-1483, 1524-1527, 1533-1534, 1539-1541, 1545, 1549-1553, 1557, 1561-1565, 1569, 1573-1577, 1581-1583, 1587-1592, 1664-1677, 1683-1690, 1697-1715, 1718-1732, 1736-1739, 1754, 1758, 1831, 1835, 1863, 1867, 1876-1877, 1880-1906, 1909-1918, 1970-1991, 2001-2036, 2046, 2055, 2058, 2076-2110, 2127-2192, 2199-2202, 2240-2245, 2252-2254, 2299-2311, 2339-2357, 2420-2433, 2483-2507, 2549-2579, 2598, 2616-2618, 2644-2675 /home/admin/.local/lib/python3.8/site-packages/matplotlib/container.py 44 25 43% 14, 18, 21-23, 26-31, 34, 71-75, 104-107, 138-142 /home/admin/.local/lib/python3.8/site-packages/matplotlib/contour.py 700 623 11% 43-45, 49-74, 175-233, 238, 244, 249, 253, 258-259, 264-266, 274-277, 281-290, 296-324, 346-414, 418-432, 436-441, 465-505, 509-511, 515-557, 560-561, 570, 595-606, 723-882, 889-894, 897-902, 926-962, 970-996, 1000-1015, 1021-1030, 1043-1046, 1050-1075, 1091-1118, 1124-1143, 1157-1180, 1205-1223, 1228-1246, 1249-1270, 1274, 1281-1282, 1326-1361, 1364-1366, 1384-1436, 1439-1461, 1468-1504, 1519-1545 /home/admin/.local/lib/python3.8/site-packages/matplotlib/dates.py 654 512 22% 222-234, 273, 300-302, 316-318, 332-342, 356-381, 404-415, 440-467, 486-491, 509-514, 543-544, 567, 590-608, 612-621, 645-647, 651-652, 655, 735-785, 789-791, 794-870, 873, 876, 958-965, 976, 979-996, 1016-1019, 1023-1025, 1028-1056, 1060-1063, 1068-1101, 1104-1114, 1117, 1136, 1147, 1151-1155, 1159-1162, 1169, 1175, 1182-1193, 1200-1201, 1205-1210, 1213-1217, 1222-1241, 1245-1246, 1250-1266, 1269, 1337-1372, 1377-1379, 1382, 1387-1396, 1399-1402, 1406-1502, 1531-1534, 1539-1552, 1574-1579, 1604-1606, 1627-1634, 1654-1659, 1679-1684, 1704-1708, 1743-1745, 1748-1749, 1753-1758, 1761-1771, 1775, 1779, 1790-1823, 1835-1836, 1845-1853, 1864, 1872-1884, 1892-1897, 1901-1910, 1923-1927, 1930, 1933, 1936 /home/admin/.local/lib/python3.8/site-packages/matplotlib/dviread.py 535 387 28% 78, 83-89, 94, 104, 121-122, 135, 143, 151-153, 160, 167, 175, 225-227, 264-268, 272, 278, 296-297, 301-302, 309-345, 371-391, 398-404, 408-409, 413-414, 418-419, 423, 426-438, 445, 448-449, 453, 457-461, 465-466, 470, 474, 478, 482-484, 488-490, 494, 498-500, 504-506, 510, 514, 518-519, 526, 529-538, 542-556, 560, 566, 570, 611-621, 625, 629, 632, 636-640, 644-661, 689-695, 698, 705-749, 752-757, 760-763, 766-772, 779, 806-825, 882-894, 897-905, 940-1005, 1024-1030, 1037-1039, 1042, 1048-1053, 1079-1110, 1118-1124, 1132, 1140-1165 /home/admin/.local/lib/python3.8/site-packages/matplotlib/figure.py 1041 613 41% 97, 110, 116-118, 163, 166, 168, 170, 172, 174, 176, 178, 220-224, 236-237, 262-282, 304-308, 314, 363-364, 379, 385-388, 411-414, 421-425, 429, 433, 441, 451, 457, 467, 477, 489-490, 516-527, 615-641, 747, 752-755, 765, 906-909, 911-914, 930-933, 938-941, 954, 973, 1009, 1128-1150, 1277-1315, 1346-1357, 1400-1418, 1460-1478, 1501-1502, 1587-1614, 1639-1641, 1680-1693, 1706-1709, 1713-1718, 1722-1726, 1732-1737, 1766-1808, 1812-1827, 1831-1837, 1949-2148, 2219-2252, 2256, 2260, 2266, 2276-2277, 2280, 2292-2308, 2316, 2332, 2335, 2350, 2358-2373, 2399, 2402, 2509-2517, 2519-2526, 2528-2532, 2566, 2569, 2600-2601, 2612-2620, 2654, 2656, 2661, 2663, 2665, 2669, 2675-2678, 2684, 2698-2700, 2736-2744, 2763-2766, 2793, 2814-2819, 2827, 2851-2856, 2889-2890, 2909-2923, 3007-3024, 3057, 3060, 3065, 3091, 3095, 3099, 3109-3110, 3127, 3144, 3153, 3162, 3168-3171, 3179, 3192-3194, 3200, 3203-3220, 3223-3247, 3372-3375, 3429-3474, 3484-3494, 3507-3509, 3539-3549, 3600-3629 /home/admin/.local/lib/python3.8/site-packages/matplotlib/font_manager.py 563 282 50% 135-136, 177, 190-191, 207-212, 217-244, 250-258, 269-291, 295-301, 305-307, 347-456, 474-524, 608, 626-631, 648, 664, 715, 773-781, 799-807, 827-828, 834-836, 853-857, 888-891, 907-920, 938, 958-962, 991-1024, 1037-1047, 1053, 1060, 1073, 1078, 1095, 1097, 1107-1110, 1127, 1139, 1172, 1191-1199, 1264, 1269, 1331-1339, 1344-1356, 1369, 1372, 1383, 1396, 1399-1417, 1427-1442, 1454-1458, 1539-1540, 1545-1548 /home/admin/.local/lib/python3.8/site-packages/matplotlib/gridspec.py 277 100 64% 49, 52, 59-63, 83, 97-99, 110-111, 121, 132-133, 143, 170-175, 214-224, 238, 242, 245-249, 255-256, 276, 304-305, 307-308, 316, 400-410, 426-428, 443, 467-474, 501-505, 511-521, 529, 558, 574-581, 587, 591, 593-597, 600, 629-630, 635-636, 641-645, 648, 651, 654, 657, 679-683, 691, 697, 739 /home/admin/.local/lib/python3.8/site-packages/matplotlib/hatch.py 143 101 29% 16-17, 20-28, 33-34, 37-45, 50-55, 58-64, 69-75, 78-84, 91-97, 102-121, 126-129, 136-137, 144-145, 153-154, 162-168, 185-189, 205-225 /home/admin/.local/lib/python3.8/site-packages/matplotlib/image.py 760 617 19% 83-110, 134-157, 171-213, 221-227, 259-274, 277-281, 285, 289-292, 302-306, 318, 325-326, 358-587, 607, 615, 620-646, 650-677, 681-683, 695-731, 743, 754, 771-776, 788-792, 796-797, 811-814, 818, 830-831, 835, 846-850, 854, 920-922, 936-938, 942-949, 954, 977-1002, 1006-1014, 1025-1041, 1058-1059, 1063, 1067-1133, 1148-1165, 1168, 1177-1180, 1183-1185, 1188, 1191, 1194-1196, 1199-1201, 1245-1248, 1252-1281, 1284, 1304-1338, 1341, 1345-1354, 1379-1389, 1393-1394, 1399-1410, 1416-1417, 1440-1451, 1454-1462, 1466-1476, 1480-1486, 1538, 1541-1553, 1558, 1621, 1627-1633, 1641, 1648, 1657, 1665-1666, 1679-1686, 1711-1724, 1734-1754, 1796-1818 /home/admin/.local/lib/python3.8/site-packages/matplotlib/layout_engine.py 69 39 43% 63-64, 70, 78-80, 88-90, 96, 103, 122-124, 130, 158-162, 181-189, 207-209, 249-259, 269-274, 303-305 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend.py 470 175 63% 69-74, 77-80, 83-90, 93-94, 343, 423, 428-430, 460, 469-470, 477, 483, 497-501, 509-511, 518-528, 533, 538, 556, 591, 596-598, 600, 623, 625-647, 649-650, 655-657, 684, 695, 702-704, 712, 721-722, 731, 769, 774, 845-853, 921-941, 945, 949, 953, 957, 963, 977-979, 983, 1016, 1020-1022, 1026, 1030, 1040-1041, 1076, 1080-1081, 1121-1158, 1161-1164, 1190-1191, 1196, 1202, 1217-1218, 1232-1238, 1243-1250, 1304, 1309, 1320-1346 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend_handler.py 343 231 33% 41-43, 82, 164, 189-192, 195-206, 231-236, 249-273, 290-312, 347, 369, 375-384, 389-396, 404-407, 410-415, 420-428, 440-443, 447-464, 468-473, 477, 487-502, 510, 521, 538-545, 551-629, 659-664, 670-712, 719-720, 748-773, 782-807, 813-817 /home/admin/.local/lib/python3.8/site-packages/matplotlib/lines.py 679 268 61% 43-52, 56-58, 65, 78-106, 118-201, 262-271, 314, 316, 326, 366, 370, 400, 440-484, 492, 506-508, 518, 537-538, 616-618, 622-624, 627-635, 654, 661, 666, 680-688, 709-710, 739-744, 749-750, 765-766, 774, 778-791, 818, 821-822, 829-833, 843, 859, 869-871, 882, 890, 906, 914, 922, 930, 940-946, 956, 960, 973, 981, 989, 997, 1006-1010, 1019-1023, 1035-1037, 1090, 1117, 1132, 1172, 1175, 1203-1206, 1279-1284, 1300-1305, 1329-1332, 1395, 1403, 1443, 1451, 1472-1481, 1484-1521, 1525-1526, 1566-1575, 1590, 1594-1599 /home/admin/.local/lib/python3.8/site-packages/matplotlib/markers.py 427 260 39% 260-261, 267-268, 342, 344, 346, 349, 358-362, 386, 402-405, 412-413, 424-429, 445-458, 474-480, 486-488, 491, 494, 497-515, 523-541, 552-556, 559, 562-573, 584-611, 614, 617, 620, 623, 626-641, 644-655, 658-659, 662-682, 685-704, 707-728, 731-754, 757-775, 780-783, 786-787, 825-828, 831-832, 835-836, 839-840, 845-849, 852-853, 856-857, 860-861, 866-867, 870-871, 874-875, 878-879, 887-890, 898-901, 911-922, 932-943 /home/admin/.local/lib/python3.8/site-packages/matplotlib/mathtext.py 114 67 41% 55-57, 61-63, 70, 76, 83, 90, 100-105, 108, 114-116, 119-125, 130-139, 142-144, 147-148, 161-163, 166-167, 170, 173, 225-226, 230-252, 278-287 /home/admin/.local/lib/python3.8/site-packages/matplotlib/mlab.py 275 235 15% 69, 80, 108-127, 152-157, 179, 198-213, 246-250, 255-288, 298-446, 455-472, 584-587, 638-651, 772-790, 829-840, 888-925, 929, 932, 959-985 /home/admin/.local/lib/python3.8/site-packages/matplotlib/offsetbox.py 659 353 46% 66-67, 73, 131-154, 196-197, 199-200, 271-278, 325-326, 336-337, 362, 387-388, 393-394, 399, 403-404, 481-483, 514, 552-564, 568-569, 573-583, 586-589, 593-594, 631, 635-636, 665, 678, 680, 683, 702, 748-749, 753, 764-765, 771, 794, 807-809, 841-846, 850-852, 859, 877-880, 884, 888-898, 902-905, 971-990, 1001-1004, 1008, 1012, 1016-1018, 1022-1029, 1040-1054, 1059-1065, 1068-1070, 1074-1086, 1131-1140, 1161-1178, 1181-1183, 1186, 1189-1190, 1193, 1197, 1200, 1203-1208, 1212-1214, 1229, 1311-1341, 1345, 1349-1350, 1354, 1358-1359, 1362-1367, 1371-1374, 1377-1380, 1388-1392, 1396, 1400-1403, 1408-1411, 1419-1458, 1462-1475, 1508-1514, 1531-1541, 1544-1556, 1559-1563, 1566-1570, 1574-1575, 1578, 1581, 1584, 1589-1590, 1593-1597, 1600-1601, 1604-1609, 1614-1615, 1618-1619, 1622-1623 /home/admin/.local/lib/python3.8/site-packages/matplotlib/patches.py 1704 1027 40% 79, 109-114, 117-126, 136-156, 203-204, 232-233, 260, 286, 290, 298, 302, 322, 425, 427, 449, 474, 494, 531, 565-566, 569, 572-573, 586, 601, 608-610, 615, 685-687, 749-750, 754, 776, 789, 796, 801, 813, 831-832, 842-843, 847-848, 852-853, 866-874, 888-889, 916-922, 925, 928, 940-941, 952-953, 956, 959, 999-1004, 1007-1040, 1044-1045, 1058-1067, 1074-1078, 1093-1095, 1099, 1103, 1114-1118, 1129, 1147-1162, 1172-1175, 1200-1201, 1212-1215, 1225-1227, 1230-1232, 1235-1237, 1240-1242, 1245-1247, 1251, 1260, 1297-1298, 1306, 1309, 1320, 1364-1376, 1401-1416, 1419-1475, 1490-1491, 1508, 1516-1519, 1542-1555, 1566-1570, 1578, 1581-1582, 1592-1593, 1597, 1609-1610, 1616, 1628-1629, 1633, 1645-1646, 1650, 1661, 1695-1701, 1704-1709, 1720-1722, 1726, 1740-1746, 1750, 1762-1764, 1768, 1780-1782, 1792-1794, 1808-1816, 1820, 1825, 1833-1844, 1847-1849, 1857-1859, 1873-1874, 1884-1885, 1889, 1902-1906, 1945-1955, 2003-2098, 2102-2108, 2113-2138, 2150-2161, 2170-2174, 2211-2212, 2216-2217, 2224, 2253-2256, 2310, 2313-2319, 2333, 2336-2340, 2358, 2361-2369, 2382, 2386-2397, 2408-2411, 2425, 2429-2441, 2475, 2521-2522, 2527-2555, 2570-2571, 2576-2611, 2614-2616, 2623-2631, 2690, 2697, 2708-2718, 2726-2737, 2756, 2759-2775, 2797-2798, 2801-2815, 2841-2844, 2847-2879, 2911-2916, 2919-2975, 3003-3006, 3009-3047, 3055-3059, 3137-3142, 3156, 3165-3181, 3216-3255, 3267-3297, 3302-3323, 3327-3408, 3417, 3466, 3485, 3506, 3524, 3547, 3569, 3588-3590, 3594-3648, 3667-3669, 3673-3736, 3756-3758, 3762-3780, 3796-3797, 3885, 3918-3919, 3940, 3944, 3948, 3952, 3962-3963, 3973-3974, 3984-3985, 3995-3996, 4017, 4026, 4045-4049, 4124-4153, 4165-4169, 4179-4180, 4190-4191, 4226-4231, 4235, 4269-4274, 4278, 4288-4289, 4299, 4309-4310, 4314, 4321-4324, 4328-4349, 4352-4367, 4377, 4462-4483, 4487-4540, 4555-4556, 4564, 4568-4582, 4587-4607, 4610-4614 /home/admin/.local/lib/python3.8/site-packages/matplotlib/path.py 381 147 61% 135, 140, 186-188, 220-223, 239-242, 254, 265, 272, 279, 308-319, 328-345, 348, 395, 446, 453, 460-463, 537-546, 587-590, 599-601, 642, 651, 662, 671-682, 704-725, 749-762, 772-788, 796, 889-922, 949, 956, 977-984, 1019, 1029-1030, 1043-1045, 1077-1083 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/__init__.py 28 3 89% 95, 109-110 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/geo.py 273 183 33% 24, 27-28, 33-34, 41-57, 61-106, 111-114, 119-120, 123, 126, 129-130, 133, 136, 139, 142, 145-146, 152, 159-162, 170-172, 179-181, 187-188, 195, 205, 213, 216, 219, 222, 236-237, 240, 244-245, 256-267, 271, 278, 282, 285-288, 291, 302-309, 313, 319-323, 327, 330-333, 336, 347-377, 381, 387-393, 397, 400-403, 406, 421-423, 427-442, 446, 454-456, 460-473, 477, 483-488, 492-493, 496, 502 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/polar.py 719 577 20% 50-54, 63, 68-77, 81-131, 135, 165-169, 175-184, 207-210, 219-231, 235, 246-253, 259, 262, 265, 268, 271, 274, 277, 290-291, 294-295, 298-302, 305-306, 324-332, 337-344, 347-352, 355-396, 413-416, 420-422, 425-433, 437-445, 458-459, 462, 466-471, 478-479, 483-487, 490-494, 514-518, 523-544, 559-561, 566-615, 618-695, 710-711, 714-716, 720-722, 725-726, 735, 744, 761-765, 771-800, 815-821, 825-844, 848-849, 857-951, 955-956, 959, 962, 965-970, 973-982, 985-991, 994-1037, 1040, 1043-1053, 1057, 1061, 1065, 1069, 1087-1098, 1104-1106, 1112, 1129-1138, 1150-1159, 1171, 1181, 1190, 1200, 1209, 1219, 1227, 1230, 1244-1256, 1266, 1277, 1280-1281, 1285, 1288, 1340-1349, 1402-1415, 1419-1441, 1453, 1463, 1473, 1476-1486, 1496, 1499-1523 /home/admin/.local/lib/python3.8/site-packages/matplotlib/pyplot.py 860 424 51% 119-120, 136, 145-157, 163-167, 175, 180-182, 188-191, 208, 234-267, 276-279, 304-322, 337, 352, 362-373, 375, 445-446, 476, 512-516, 552-556, 576-584, 589, 594, 599-601, 609, 614, 619, 658-686, 804-807, 814-825, 831, 846, 849-853, 867, 889-890, 906, 911, 921-923, 940, 945, 950, 969-991, 1017, 1022-1025, 1032, 1118-1126, 1133-1135, 1142-1143, 1149, 1290-1352, 1613-1621, 1664-1683, 1696-1699, 1712-1715, 1726-1734, 1753-1756, 1791-1795, 1830, 1881-1883, 1893, 1944-1946, 1954-1956, 2020-2029, 2088-2097, 2105-2108, 2116-2118, 2130-2138, 2155-2159, 2165, 2184-2190, 2195, 2242-2252, 2267-2274, 2284, 2295, 2303, 2315, 2324, 2335, 2343, 2349, 2355, 2361, 2370, 2380, 2389, 2395, 2401, 2407, 2419, 2425, 2431, 2439, 2447, 2457, 2467, 2483, 2500, 2508, 2517, 2527-2531, 2537-2541, 2550, 2564, 2578, 2588, 2598, 2608, 2627-2635, 2645, 2658, 2669-2674, 2682, 2695-2704, 2716, 2722, 2730, 2739, 2745, 2751, 2759-2764, 2773-2778, 2786, 2822, 2833, 2843-2847, 2853, 2874, 2880, 2890-2897, 2905-2909, 2917, 2929, 2940, 2953-2963, 2991, 2999, 3008-3010, 3016-3018, 3026-3030, 3036, 3045, 3058, 3069, 3107, 3113, 3124, 3135, 3146, 3157, 3168, 3179, 3190, 3201, 3212, 3223, 3234, 3245, 3256, 3267, 3278, 3289, 3300, 3311, 3322 /home/admin/.local/lib/python3.8/site-packages/matplotlib/quiver.py 390 338 13% 291-314, 318, 322-345, 348, 357-362, 365, 373-374, 377-385, 407-437, 441-443, 477-506, 516-527, 530-536, 540-544, 549-571, 575-577, 592-596, 599-605, 608-663, 670-723, 897-941, 967-973, 1024-1117, 1122-1162, 1173-1180 /home/admin/.local/lib/python3.8/site-packages/matplotlib/rcsetup.py 414 127 69% 68-69, 75-82, 99, 110, 127, 135-136, 159, 169-170, 185, 188-189, 218, 230-234, 238, 260, 282, 288, 290, 292, 294, 296-300, 304, 344-347, 354, 366-367, 381-384, 395-398, 411, 415, 427-428, 438, 457-483, 506-524, 534, 537-541, 549-552, 560, 568, 583-589, 683, 686, 689-692, 695-698, 705, 716-718, 738-739, 746, 750, 758, 761, 783, 786-792 /home/admin/.local/lib/python3.8/site-packages/matplotlib/scale.py 274 146 47% 69, 76, 111, 145-150, 153, 156, 180-182, 186, 190-198, 205-209, 213, 218-236, 239, 246-247, 250, 253, 256, 281-282, 288-291, 297, 301-304, 333-335, 339, 343, 350-361, 364-371, 374, 382-388, 391-398, 401, 440-441, 449-453, 457, 465-469, 472, 475, 483-484, 487, 490, 551-557, 562, 565-574, 581-584, 588-593, 596, 599, 606-607, 611, 614, 617, 646-648, 652, 657-665, 677-679, 726 /home/admin/.local/lib/python3.8/site-packages/matplotlib/spines.py 315 142 55% 33, 90-99, 103-109, 113-114, 126-131, 137-138, 156, 171-176, 189-194, 231, 234, 240, 243-270, 282, 314, 317, 319, 329-330, 338-341, 351, 357-386, 408-419, 423, 438, 448-451, 456-460, 476-477, 491, 494-505, 508-512, 546, 549, 554-555, 560-563, 566, 568-571, 578, 582 /home/admin/.local/lib/python3.8/site-packages/matplotlib/stackplot.py 42 37 12% 71-127 /home/admin/.local/lib/python3.8/site-packages/matplotlib/streamplot.py 370 328 11% 91-241, 247-248, 274-284, 288, 291, 294, 297, 300-301, 304-305, 308-311, 314, 321-362, 366, 372, 386-396, 399, 403-404, 408-409, 417-426, 443-502, 535-602, 607-624, 633-667, 678-707 /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/core.py 92 45 51% 22, 127-180, 220-224, 242, 256, 262-266 /home/admin/.local/lib/python3.8/site-packages/matplotlib/table.py 335 92 73% 101, 118, 143, 161, 188-189, 202, 207, 211-217, 302, 354-355, 365, 402, 404, 407, 431-444, 448, 452-457, 500-508, 512-516, 520-521, 526, 532, 543-545, 568-570, 585, 598-605, 614, 616, 618, 620, 627, 629, 631, 633, 650, 738, 744-746, 752, 757, 760, 774-777, 780-781, 787-790, 796, 821-827 /home/admin/.local/lib/python3.8/site-packages/matplotlib/texmanager.py 151 103 32% 48-49, 105-106, 110-115, 120-130, 134-171, 178-187, 194-195, 200, 205-207, 246-249, 253-275, 284-305, 314-329, 334-344, 357-361, 366-373 /home/admin/.local/lib/python3.8/site-packages/matplotlib/text.py 812 357 56% 41-49, 67-90, 130, 228, 233, 236-239, 246-268, 279, 292-313, 318, 390, 479, 486-489, 492, 494, 496, 498, 531-552, 559, 571-582, 589, 633-652, 659-675, 681-685, 697-736, 761-762, 768-769, 785, 789-790, 796, 814, 824, 834, 844, 864, 874, 884, 916, 941, 952, 954, 977-983, 1026-1028, 1065-1066, 1080-1081, 1095-1096, 1126, 1148, 1181-1182, 1234, 1246-1247, 1297-1299, 1301, 1303, 1332, 1371, 1395-1397, 1407-1408, 1412, 1415-1419, 1436-1454, 1470-1478, 1483-1488, 1490-1496, 1498-1499, 1501, 1503, 1505, 1508, 1510-1513, 1517, 1524, 1526, 1537-1538, 1545-1546, 1548, 1550-1552, 1557, 1562, 1569, 1606-1607, 1612, 1616-1617, 1639-1654, 1673, 1856, 1872-1880, 1888-1895, 1908, 1918, 1922, 1938, 1946, 1960-2016, 2024, 2030-2032, 2041-2058, 2062-2064 /home/admin/.local/lib/python3.8/site-packages/matplotlib/textpath.py 192 152 21% 34-37, 40, 46, 49-70, 112-134, 142-164, 173-215, 221-223, 230-280, 287-298, 354-369, 373-374, 378, 385-386, 393, 402-408 /home/admin/.local/lib/python3.8/site-packages/matplotlib/ticker.py 1228 803 35% 165-167, 170, 173, 176, 179, 182, 186, 196-197, 213, 225, 233, 269, 283-284, 294-297, 300, 303, 316-317, 325, 328, 331, 346, 354, 365, 374, 452, 485-486, 498, 512, 531, 555-556, 559-564, 573, 588, 620-622, 626-650, 654-666, 676-692, 712-713, 736, 749-750, 753-754, 761-762, 764, 768, 772, 774, 782, 789, 792, 795, 805, 809, 874-883, 893, 902, 914, 925, 933-984, 987-993, 997-1016, 1019-1020, 1024, 1028-1046, 1054-1062, 1072, 1076-1110, 1120-1125, 1167-1172, 1184, 1193, 1205, 1218, 1221-1260, 1263-1286, 1289-1292, 1295-1313, 1318-1322, 1388-1392, 1395, 1398-1401, 1406, 1409-1412, 1417-1421, 1438-1473, 1503-1506, 1510-1512, 1536-1556, 1559, 1570-1579, 1583, 1616, 1623, 1631, 1645, 1685-1686, 1690-1693, 1697-1698, 1701, 1723-1724, 1741-1747, 1791-1795, 1800, 1804, 1808-1811, 1815-1816, 1819-1830, 1835-1850, 1860, 1864-1865, 1869-1870, 1873-1879, 1886-1896, 1905, 1928, 1935-1937, 1946, 1953, 2013, 2017, 2020, 2022, 2053, 2065, 2072, 2086-2088, 2097-2098, 2104-2109, 2117, 2140-2141, 2148, 2150, 2152, 2157-2158, 2164, 2171-2176, 2181-2189, 2198, 2209, 2220-2225, 2234-2239, 2244, 2248, 2284-2292, 2296-2303, 2308, 2315, 2321-2334, 2340-2341, 2344-2429, 2433-2441, 2444-2462, 2489-2502, 2506-2509, 2514-2515, 2518-2604, 2608-2620, 2659-2664, 2669-2676, 2679-2685, 2690-2732, 2752-2753, 2757-2759, 2763, 2767, 2771-2844, 2847-2880, 2895-2896, 2916, 2920-2957, 2960 /home/admin/.local/lib/python3.8/site-packages/matplotlib/transforms.py 1162 372 68% 127-129, 146-155, 219, 238-244, 251, 336, 341, 391, 397-398, 404-405, 411, 421-431, 437-438, 444-445, 451, 462-472, 512, 517, 530-531, 545, 547, 551, 563-566, 574-577, 589-593, 604, 612-617, 635-636, 643-647, 653, 666-670, 764, 774-782, 828, 832, 837, 840, 878, 908-909, 928-929, 952, 980-981, 1000-1004, 1054-1055, 1067, 1095, 1098, 1125, 1129, 1140-1145, 1149, 1153, 1183-1192, 1198-1204, 1207-1212, 1219-1222, 1226-1228, 1235-1238, 1242-1244, 1251-1254, 1258-1260, 1267-1270, 1274-1276, 1401, 1415, 1452, 1458-1462, 1467, 1473, 1502-1503, 1508, 1536, 1561, 1570, 1574, 1592-1594, 1650-1673, 1685, 1720, 1734, 1783, 1791, 1796, 1809, 1848-1849, 1854-1855, 1859-1870, 1878, 1909, 1926, 1954-1955, 1962-1964, 1975, 2027, 2038-2039, 2088-2101, 2114, 2140, 2144, 2148, 2152, 2172, 2176, 2179, 2215, 2220, 2228, 2234-2256, 2260, 2304, 2365, 2377-2382, 2394, 2429-2432, 2438-2441, 2447, 2454, 2475, 2477, 2490, 2493-2496, 2550-2558, 2564-2578, 2595, 2610, 2627-2637, 2649, 2664, 2720-2721, 2726-2729, 2796-2797, 2817-2818, 2821-2827, 2863-2864, 2872-2873, 2876-2881, 2884, 2904-2907, 2934, 2955-2956, 2979-2988 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triangulation.py 98 80 18% 43-91, 101, 113-115, 122-131, 137-140, 154-168, 172-191, 199-203, 216-218, 228-247 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tricontour.py 54 38 30% 29, 35-51, 54-79, 245-246, 271-272 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trifinder.py 26 15 42% 20-21, 38-42, 55-63, 79, 86, 93 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triinterpolate.py 535 450 16% 34-56, 157-207, 228, 258-261, 265, 270, 275-283, 381-418, 421, 426, 431-446, 466-476, 497-515, 539-543, 561-571, 689-706, 727-762, 783-787, 803-828, 846-879, 896-909, 935-978, 996-1004, 1007, 1013-1017, 1043-1058, 1065-1068, 1084-1105, 1112-1127, 1135-1153, 1163-1164, 1172-1210, 1224-1227, 1234-1235, 1243-1248, 1254-1259, 1265-1269, 1272, 1277-1280, 1312-1350, 1406-1426, 1440-1472, 1479, 1486, 1494-1514, 1531-1544, 1556-1574 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tripcolor.py 62 56 10% 61-154 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triplot.py 28 23 18% 38-86 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trirefine.py 93 81 13% 43-44, 62, 94-131, 157-169, 191-307 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tritools.py 77 65 16% 29-30, 44-47, 79-115, 165-190, 220-238, 260-263 /home/admin/.local/lib/python3.8/site-packages/matplotlib/units.py 61 10 84% 66, 117, 122, 132, 150-156, 176 /home/admin/.local/lib/python3.8/site-packages/matplotlib/widgets.py 1888 1585 16% 43-45, 49-51, 55, 59, 63, 76, 80, 91, 107, 133-135, 144-145, 149-150, 194-215, 218-221, 224-228, 231-241, 249, 253, 265-301, 305-315, 326, 330-331, 430-503, 507-527, 531-552, 556-561, 571-586, 603, 703-803, 814-824, 828-835, 839-846, 850, 854-865, 869-906, 910-920, 930, 940, 950-969, 986, 990-991, 1053-1107, 1111-1118, 1121-1143, 1156-1159, 1173-1176, 1190-1196, 1214-1246, 1256-1260, 1268, 1277, 1281, 1287-1305, 1311-1335, 1383-1420, 1424, 1435-1458, 1461-1465, 1468-1501, 1504-1511, 1515-1532, 1536-1548, 1551-1563, 1566, 1569-1575, 1583, 1592, 1596, 1659-1721, 1725-1731, 1734-1753, 1766-1769, 1783-1791, 1797, 1801-1808, 1816-1841, 1849, 1853, 1859-1870, 1888-1919, 1922-1926, 1929-1942, 1976-1992, 1996-1999, 2003-2024, 2027-2035, 2080-2107, 2116-2117, 2124-2127, 2131-2141, 2144-2155, 2158-2172, 2179-2207, 2212-2214, 2224, 2232-2252, 2256-2262, 2266-2286, 2291-2308, 2312-2316, 2326-2332, 2336-2347, 2354-2362, 2369-2373, 2380-2381, 2388-2405, 2412-2419, 2426-2428, 2432, 2436, 2440-2441, 2445-2446, 2449-2450, 2455-2456, 2463-2468, 2476-2485, 2488-2492, 2511-2512, 2531-2532, 2640-2687, 2691-2714, 2718-2722, 2729-2732, 2736-2743, 2747-2749, 2753-2781, 2786, 2791-2802, 2806-2835, 2839-2850, 2856-2895, 2898-2905, 2910-2931, 2935, 2941-2942, 2949-2955, 2960-2967, 2993-3006, 3010, 3015-3016, 3021, 3033-3035, 3039-3040, 3044-3045, 3049-3050, 3067-3078, 3103-3109, 3113, 3117, 3121, 3125-3128, 3131, 3134, 3138-3144, 3272-3331, 3335, 3339, 3346-3370, 3374-3418, 3430-3565, 3569, 3572-3577, 3580-3581, 3593-3598, 3606-3613, 3618-3619, 3627-3630, 3635-3643, 3651, 3657-3660, 3663-3678, 3683-3706, 3710, 3720-3725, 3747, 3750-3760, 3764-3767, 3813-3823, 3826-3827, 3830-3835, 3838-3843, 3933-3967, 3970, 3973-3987, 3990-3992, 3996-4000, 4009-4028, 4032, 4036-4052, 4057-4064, 4069-4088, 4096-4100, 4105-4138, 4144-4148, 4154-4166, 4170-4182, 4187, 4197-4202, 4232-4244, 4247-4255, 4258-4275 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/axes_divider.py 276 217 21% 46-55, 58, 61, 72, 76, 92-96, 100, 109, 113, 122, 126, 134, 138, 141, 144, 147-150, 156-157, 163, 180-212, 228, 234-245, 261-264, 286-299, 303-306, 314, 339-343, 347, 351, 355-356, 372-382, 387-390, 401-423, 433-455, 480-489, 492-499, 502-506, 509-512, 515, 523-551, 576, 580-590, 611, 615-625, 629-633, 646-651 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/axes_grid.py 274 240 12% 14-17, 22-23, 26-31, 34-35, 38-40, 115-171, 175-200, 203-208, 212, 215, 221, 232-233, 244, 249, 253, 270-300, 308, 311, 314, 384-416, 421-576 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/axes_size.py 158 102 35% 20, 23-26, 31-32, 35-37, 44, 47-49, 58-59, 62-64, 74, 77-79, 85-88, 98-102, 105-114, 124-128, 131-141, 151-153, 156, 159-164, 173, 182, 193-195, 198-204, 215-216, 219-222, 234-239, 245, 248-254, 267-269, 272-276, 292-294, 297-304 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py 89 63 29% 8, 11-12, 15-16, 23-24, 27-38, 41, 45, 49-52, 61-72, 76-77, 82-83, 88, 91-94, 97, 101-128 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py 131 100 24% 12-16, 19-24, 31-37, 42-50, 53-54, 57, 60-69, 81-82, 111-120, 123-139, 142-144, 147-151, 160-164, 173-177, 186-193, 201-206, 209-216, 220-226, 247-253 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/art3d.py 488 390 20% 28-31, 36-39, 62-73, 97-98, 102, 116-119, 129-130, 144-146, 150-159, 164, 179-180, 208-209, 224-229, 248-254, 265, 269-273, 289-290, 296-300, 306-314, 320-329, 337-344, 354-355, 361-362, 368-377, 382-384, 404-405, 421-422, 426, 429-434, 455-456, 472-473, 476-481, 486-489, 494-496, 501-506, 529-531, 534, 546-547, 551-552, 570-580, 583-592, 595-602, 605, 611-613, 636-640, 643-645, 649-650, 668-695, 698-700, 703-705, 708, 720-721, 724-754, 758-768, 771-778, 781, 787-789, 809-816, 871-896, 914-916, 920-928, 944-947, 953-955, 960-966, 970-971, 977-1040, 1044-1045, 1049-1050, 1054-1065, 1070-1073, 1078-1081, 1097-1101, 1111-1118, 1127-1132, 1141-1146, 1173-1189, 1198-1227 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py 1305 1135 13% 122-180, 183-184, 187-188, 195, 200-207, 211-213, 217, 231, 234-235, 246, 249-253, 257, 260-275, 325-360, 372-381, 407-419, 422-436, 440-492, 495-500, 506, 527-531, 541-564, 577-602, 607-616, 628-678, 682-685, 696-706, 714, 718, 722, 767, 818-835, 853-865, 869, 875-936, 951-957, 961, 967, 973, 983-992, 996-1001, 1004-1009, 1012-1017, 1021, 1026-1030, 1038-1043, 1052-1083, 1093-1144, 1150-1180, 1189-1202, 1212-1250, 1273-1288, 1306-1319, 1325-1327, 1333-1334, 1340, 1350-1351, 1364-1367, 1386-1395, 1403-1404, 1412-1416, 1425-1434, 1446-1448, 1471-1492, 1566-1689, 1729-1807, 1862-1904, 1911-1951, 1955-1962, 1965, 1974-1990, 2021-2028, 2064-2083, 2088-2093, 2118-2125, 2157-2176, 2191-2209, 2261-2283, 2313-2340, 2404-2501, 2505-2508, 2553-2649, 2724-2860, 2969-3184, 3188-3200, 3268-3317, 3324-3335 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axis3d.py 301 250 17% 22, 30-31, 37, 43-50, 71, 74, 77-154, 161-179, 183, 186-191, 194-199, 204, 207-210, 223-228, 235-236, 239-242, 245-283, 290-302, 315-322, 332-346, 350-528, 534-576 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/proj3d.py 101 81 20% 20-29, 39-48, 58-68, 95-107, 125-130, 148-150, 154-162, 167-173, 177-181, 185-192, 199-206, 210, 217-218, 230-231, 235, 239-240, 244-249 /home/admin/.local/lib/python3.8/site-packages/nacl/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_aead.py 132 104 21% 105-163, 184-242, 263-322, 343-401, 422-481, 502-559 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_box.py 107 85 21% 40-46, 69-80, 97-112, 128-147, 160-171, 184-196, 211-227, 244-265, 282-324 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_core.py 73 52 29% 48-61, 80-102, 121-143, 163-182, 199-217, 235-253, 273-294, 314-335, 355-376, 393-412 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_generichash.py 68 42 38% 45-87, 125-147, 161-164, 171, 176-180, 212-228, 241-256, 269-281 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_hash.py 21 12 43% 34-37, 47-50, 60-63 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_kx.py 40 27 32% 47-52, 69-81, 103-139, 161-197 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_pwhash.py 187 99 47% 194-226, 238-262, 295-322, 348-366, 382-399, 404-456, 486-527, 552-570, 585-597 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_scalarmult.py 51 34 33% 44-49, 61-66, 83-103, 120-140, 163-191, 212-240 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_secretbox.py 31 20 35% 41-54, 69-86 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_secretstream.py 76 54 29% 58-63, 76-82, 100-126, 154-193, 214-246, 270-331, 352-357 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_shorthash.py 26 13 50% 45-53, 67-81 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_sign.py 89 50 44% 38-44, 58, 106, 122-133, 147-158, 172-175, 189-192, 203-209, 224-235, 253-276, 296-327 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/randombytes.py 13 8 38% 30-32, 44-51 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/sodium_core.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/utils.py 54 46 15% 24-38, 53-64, 78-85, 101-110, 128-141 /home/admin/.local/lib/python3.8/site-packages/nacl/encoding.py 57 10 82% 56, 60, 66, 70, 76, 80, 86, 90, 96, 100 /home/admin/.local/lib/python3.8/site-packages/nacl/exceptions.py 26 2 92% 84, 88 /home/admin/.local/lib/python3.8/site-packages/nacl/public.py 113 69 39% 41-46, 53, 56, 59-61, 64, 93-108, 132-144, 147, 150, 153-155, 158, 167, 197-203, 209, 219-224, 246-263, 287-305, 319, 347-360, 365, 387-391, 411-423 /home/admin/.local/lib/python3.8/site-packages/nacl/signing.py 86 25 71% 56, 74, 77, 88, 91-93, 96, 121, 127, 135, 146-147, 177, 183, 195, 198, 201-203, 206, 215, 248-250 /home/admin/.local/lib/python3.8/site-packages/nacl/utils.py 30 11 63% 41-44, 51, 58, 63, 67, 71, 86-88 /home/admin/.local/lib/python3.8/site-packages/numpy/__config__.py 30 16 47% 12-16, 27-28, 69-78 /home/admin/.local/lib/python3.8/site-packages/numpy/__init__.py 142 39 73% 124, 128-132, 284-289, 309-313, 317-325, 351-358, 368-374, 378-391, 410-417 /home/admin/.local/lib/python3.8/site-packages/numpy/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/_globals.py 19 2 89% 26, 85 /home/admin/.local/lib/python3.8/site-packages/numpy/_pytesttester.py 51 43 16% 38-44, 128-201 /home/admin/.local/lib/python3.8/site-packages/numpy/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/compat/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/compat/_inspect.py 67 17 75% 75, 86, 107, 122-123, 126-129, 136, 182-191 /home/admin/.local/lib/python3.8/site-packages/numpy/compat/py3k.py 59 24 59% 39-41, 44-46, 49-51, 57, 60, 65, 68-71, 74-77, 85, 103, 106, 109, 134-135 /home/admin/.local/lib/python3.8/site-packages/numpy/core/__init__.py 85 14 84% 23-48, 62-68, 125-126, 135, 142, 150-151 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs.py 261 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs_scalars.py 48 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_asarray.py 34 12 65% 19, 95, 109, 114-115, 121, 123-124, 126-127, 133-134 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype.py 157 125 20% 27-28, 35-42, 46-49, 60, 65, 95-100, 104-156, 163-175, 180-186, 191-230, 245-253, 257-279, 286-296, 300-301, 313, 316, 326, 330, 340 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype_ctypes.py 54 36 33% 33, 37-68, 84-93, 106, 108, 110, 112, 116 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_exceptions.py 98 57 42% 11-14, 35, 42-44, 47, 58-59, 62, 75-78, 85-86, 90-91, 103-104, 108-109, 128-138, 145-146, 150-153, 160-190, 193-194 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_internal.py 430 234 46% 16-17, 24, 27-51, 57-76, 89-133, 141, 158-203, 207, 209, 211, 213, 215, 220, 222-223, 227, 230-233, 242, 246, 251-265, 282-284, 291-293, 300-302, 320, 332, 343, 352, 361-363, 370-372, 379-381, 388-392, 400-416, 431-434, 457-466, 490-495, 579, 627-630, 637-638, 643, 655, 662-663, 668-674, 690-696, 703, 717, 751-757, 761-779, 782-785, 798-803, 810-811, 830, 871, 877-878 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_methods.py 155 50 68% 52, 58, 64, 82-84, 95, 98-99, 109, 114-123, 127, 135-136, 138-139, 141, 153, 156, 169, 176-177, 184, 187, 191, 202, 207, 220, 225, 235-242, 251, 256, 262-272, 282-287, 290 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_string_helpers.py 15 5 67% 68-69, 97-100 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_type_aliases.py 122 13 89% 47-53, 108, 224-230 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_ufunc_config.py 87 23 74% 192-203, 217, 302-310, 356, 432, 437 /home/admin/.local/lib/python3.8/site-packages/numpy/core/arrayprint.py 550 326 41% 29-30, 69, 73, 77, 80, 85-88, 93-96, 252-265, 295, 325-330, 355-359, 362, 365, 392, 395-412, 424, 427, 430-452, 472, 492, 520, 680-684, 687, 696, 709-710, 723-740, 767, 787, 793-796, 799-804, 814, 833, 861-865, 872-894, 898-978, 981-1001, 1081-1087, 1169-1178, 1190, 1201, 1204, 1212-1224, 1230-1237, 1242-1253, 1257, 1260-1263, 1270-1284, 1287-1289, 1292, 1300, 1305, 1308-1310, 1322, 1330-1336, 1339-1346, 1355, 1360, 1362, 1390-1398, 1411-1423, 1430-1470, 1475, 1523, 1530, 1540, 1556, 1595, 1658-1664 /home/admin/.local/lib/python3.8/site-packages/numpy/core/defchararray.py 438 243 45% 57-61, 68, 79-84, 92-94, 98, 124, 150, 177, 203, 229, 255, 259, 283, 307-311, 315, 339-344, 349, 376, 415-416, 420, 451-456, 461, 507, 511, 556, 592, 597, 640, 645, 680, 716, 745, 772, 798, 824, 851, 878, 904, 931, 935, 960, 966, 996-1001, 1036-1037, 1041, 1095-1096, 1100, 1135, 1140, 1171, 1205, 1235, 1266-1271, 1307, 1312, 1349, 1354, 1398-1399, 1433, 1438, 1467, 1472, 1503, 1548-1549, 1586-1587, 1626-1627, 1631, 1662-1667, 1702-1703, 1707, 1734-1737, 1768-1770, 1800-1802, 1950-1979, 1983-1984, 1987-1996, 2011, 2021, 2031, 2041, 2051, 2061, 2072, 2083, 2094, 2105, 2117, 2120, 2140, 2153, 2164, 2176, 2187, 2198, 2210, 2222, 2234, 2245, 2258, 2271, 2283, 2296, 2309, 2321, 2334, 2346, 2358, 2370, 2382, 2392, 2404, 2417, 2429, 2441, 2451, 2463, 2475, 2487, 2499, 2511, 2523, 2535, 2548, 2562, 2574, 2586, 2598, 2610, 2675-2743, 2794 /home/admin/.local/lib/python3.8/site-packages/numpy/core/einsumfunc.py 408 385 6% 49-54, 79-82, 128-142, 176-213, 248-270, 291-310, 348-410, 457-520, 550-693, 703, 818-986, 992-993, 1353-1431 /home/admin/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py 363 129 64% 45-47, 58-66, 82, 302-304, 429, 433, 479, 483, 537-543, 664, 749-756, 760, 839, 994-995, 1199, 1276, 1280, 1350, 1354, 1416-1434, 1503-1504, 1508, 1512, 1638-1642, 1647, 1707-1711, 1820, 1969-1970, 1975, 2039, 2248-2257, 2622, 2624-2629, 2884, 2919-2925, 3056, 3120, 3166, 3204-3213, 3429, 3431, 3433-3438, 3446, 3568-3581, 3587, 3708-3723, 3763, 3777, 3789 /home/admin/.local/lib/python3.8/site-packages/numpy/core/function_base.py 117 56 52% 122, 144-148, 153-159, 167, 170, 173, 180, 275-278, 283, 389-440, 453, 456, 463, 527-529 /home/admin/.local/lib/python3.8/site-packages/numpy/core/getlimits.py 199 26 87% 262, 277-281, 287-288, 385-387, 398, 401, 403-406, 409, 440-451, 454-457, 517-518, 523, 553-560, 563 /home/admin/.local/lib/python3.8/site-packages/numpy/core/machar.py 188 178 5% 113-114, 117-325, 328-338, 342 /home/admin/.local/lib/python3.8/site-packages/numpy/core/memmap.py 91 73 20% 211-286, 289-298, 315-316, 319-331, 334-337 /home/admin/.local/lib/python3.8/site-packages/numpy/core/multiarray.py 104 18 83% 244-245, 338, 492-495, 610, 661, 880, 957, 1148, 1206, 1290, 1365, 1460, 1554, 1622, 1690 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numeric.py 495 235 53% 146, 202, 286, 338, 422, 485-496, 564, 568, 614-618, 622, 663, 667, 741, 745, 837-844, 848, 934-936, 940, 1072-1133, 1137, 1211-1238, 1242, 1317-1332, 1382-1383, 1387-1390, 1450-1452, 1457, 1471, 1593, 1606, 1632-1646, 1663-1671, 1768, 1776, 1783, 1842-1846, 1855, 2011-2051, 2093-2108, 2116-2123, 2127, 2164, 2360-2378, 2441-2442, 2448-2453, 2457, 2496-2505 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numerictypes.py 150 64 57% 173-181, 219-227, 274, 278-279, 355, 420, 500-506, 566-572, 576-588, 651-672 /home/admin/.local/lib/python3.8/site-packages/numpy/core/overrides.py 58 9 84% 102, 107, 175-181 /home/admin/.local/lib/python3.8/site-packages/numpy/core/records.py 356 310 13% 81, 149-151, 156-172, 178-208, 211-221, 234-236, 239-241, 244-265, 269-279, 283-290, 295-299, 423-434, 437-440, 446-470, 480-507, 510-524, 528-557, 560-574, 578-586, 637-680, 725-763, 821-838, 841-846, 893-945, 1031-1092 /home/admin/.local/lib/python3.8/site-packages/numpy/core/shape_base.py 192 89 54% 123, 130, 193, 195, 199, 202, 209-214, 278, 281, 336, 340, 352-353, 418, 422, 426, 448-449, 483-525, 531, 535, 576-594, 624-647, 659-666, 673-677, 830-849, 861-870, 874-889, 893-900 /home/admin/.local/lib/python3.8/site-packages/numpy/core/umath.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ctypeslib.py 213 175 18% 63-64, 67-82, 118-154, 158-161, 166-171, 177-192, 208, 217-220, 280-341, 347-351, 373-387, 391-393, 398-443, 451-456, 497, 509-518, 524-539 /home/admin/.local/lib/python3.8/site-packages/numpy/dual.py 43 10 77% 66, 72-79, 82-83 /home/admin/.local/lib/python3.8/site-packages/numpy/fft/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/fft/_pocketfft.py 164 120 27% 50-75, 79-88, 93-102, 111-114, 119, 211-216, 312-317, 405-410, 509-514, 607-612, 674-679, 683-698, 702-708, 712, 815, 918, 1014, 1107, 1200-1205, 1257, 1362-1367, 1424 /home/admin/.local/lib/python3.8/site-packages/numpy/fft/helper.py 46 33 28% 16, 64-73, 111-120, 160-169, 216-221 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/__init__.py 39 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_datasource.py 177 73 59% 59-66, 110-111, 116-117, 123-126, 252-254, 259-261, 274-278, 289, 325-342, 364-366, 371, 373, 411, 468-485, 522, 529, 533, 578-579, 582, 586-591, 595, 618, 652, 683, 700-704 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_iotools.py 352 295 16% 31-33, 44-45, 53-57, 81-84, 120-131, 168, 172-197, 201-206, 209-216, 219-224, 227, 288-310, 339-380, 383, 413-419, 505, 524, 529, 539-541, 568-582, 587-596, 601-669, 672-675, 678-700, 703, 707-723, 746-751, 754-763, 796-820, 861-898 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_version.py 75 61 19% 56-76, 80-97, 101-112, 115-134, 137, 140, 143, 146, 149, 152, 155 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraypad.py 218 200 8% 29-30, 55, 81-83, 109-126, 146-151, 175-183, 208-227, 257-293, 321-378, 401-451, 482-518, 522, 736-876 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py 197 117 41% 34, 82-122, 130, 276-317, 330-331, 338-344, 350, 352-355, 357-358, 435-436, 441-442, 446-447, 455-461, 467, 500-510, 589, 600-602, 610-631, 738, 775, 779, 819-824 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arrayterator.py 71 58 18% 85-90, 93, 101-125, 132-134, 161-162, 172, 177-219 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/format.py 266 178 33% 193-194, 212-216, 232-233, 238-245, 270-281, 307-336, 352-364, 371-389, 396-413, 430-440, 452, 468, 499, 532, 563, 579, 599-601, 603-604, 607-609, 614-615, 617-618, 621-623, 663-696, 735, 742-751, 770-780, 784-785, 842-890, 912-913, 915-916 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/function_base.py 1153 803 30% 116, 121, 125, 131, 133, 143, 232, 234, 383-412, 415-417, 488, 494-497, 588-618, 622-623, 665-719, 810-811, 989-1157, 1250, 1252, 1258, 1263-1268, 1273-1278, 1281, 1298, 1406-1439, 1443, 1485-1496, 1500, 1569-1596, 1600, 1627-1637, 1686, 1689-1693, 1698, 1750, 1754, 1794-1798, 1833-1840, 1866-1869, 1887-1905, 1926-1934, 1939, 1945-1948, 2113, 2118, 2122, 2131, 2148-2161, 2169, 2186, 2197, 2208-2214, 2219, 2237, 2239, 2251, 2257-2316, 2321, 2448-2543, 2548, 2679-2701, 2796-2801, 2905-2910, 3009-3014, 3109-3114, 3182-3190, 3194, 3198, 3202, 3256-3262, 3388-3392, 3396, 3475-3477, 3481, 3508-3510, 3542-3560, 3570, 3655-3660, 3666-3716, 3866, 3873, 3976-3979, 3990, 3998-4000, 4003, 4030-4033, 4038, 4047, 4053, 4055, 4057, 4059, 4063, 4075-4084, 4095-4098, 4116-4120, 4126, 4215-4240, 4356, 4379, 4447-4560, 4564, 4656-4755, 4814, 4821, 4915-4932 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/histograms.py 287 254 11% 29, 49-50, 72-73, 96-97, 118-119, 146-161, 182-196, 224-226, 263-270, 285-301, 309-331, 342-357, 382-451, 460, 467, 668-670, 675, 791-929, 934-940, 1014-1129 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/index_tricks.py 258 182 29% 32, 93-107, 149-207, 325-420, 423, 593, 607, 610, 657-661, 665, 678-681, 695-696, 775, 891-909, 977-978, 982, 1006-1013 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/mixins.py 59 12 80% 10-13, 19-21, 29-31, 39, 54 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/nanfunctions.py 279 219 22% 61-66, 96-110, 135-139, 164-180, 209-221, 225, 313-336, 340, 428-451, 455, 494-500, 504, 544-550, 554, 647-648, 652, 717-718, 722, 787-788, 792, 854-855, 859, 937-957, 965-974, 984-1000, 1010-1020, 1025, 1113-1124, 1129, 1245-1249, 1255, 1358-1362, 1371-1381, 1391-1405, 1413-1418, 1424, 1518-1567, 1572, 1670-1676 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/npyio.py 854 664 22% 34-37, 83, 86-89, 97, 108-112, 189-202, 205, 208, 215-221, 224, 228, 231, 242-260, 269-273, 277-281, 408, 414-415, 431-434, 438, 444-450, 455, 519-529, 534-535, 618, 622-623, 689, 695-726, 733-736, 740, 742, 744, 747-758, 768, 904, 927-944, 950, 954, 990, 992, 994-995, 1005-1006, 1016, 1028, 1039-1054, 1065, 1072-1075, 1081, 1085-1086, 1091, 1100-1104, 1113, 1122-1138, 1152-1156, 1162, 1167, 1176-1179, 1182-1186, 1199, 1326-1447, 1510-1541, 1557, 1751-2284, 2311-2317, 2339-2345, 2369-2377, 2403-2415 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/polynomial.py 438 319 27% 41, 138-165, 231, 238, 248, 257, 265, 342-366, 370, 432-446, 450, 620-689, 693, 764-772, 776, 830-844, 884-898, 956-961, 965, 1020-1038, 1042-1065, 1180, 1185-1186, 1191, 1197, 1202, 1208, 1220-1231, 1233, 1236, 1239, 1246-1249, 1252-1254, 1257, 1260-1314, 1317, 1320, 1323, 1326-1330, 1333-1337, 1340-1341, 1344-1345, 1348-1353, 1356-1357, 1360-1361, 1364-1368, 1373-1377, 1382-1386, 1389-1391, 1395-1400, 1403-1411, 1414, 1427, 1440 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/scimath.py 70 38 46% 106-110, 135-138, 162-165, 189-192, 196, 239-240, 287-288, 337-338, 342, 376-378, 425-426, 430, 473-475, 519-520, 565-566, 616-617 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/shape_base.py 263 148 44% 31-49, 53, 161-170, 174, 251-260, 264, 359-414, 418, 487-505, 509, 588-602, 648, 718, 722, 727-732, 778, 868-872, 878, 937-942, 989-991, 1034-1036, 1043-1048, 1055-1060, 1064, 1136-1164, 1252 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py 91 46 49% 21-22, 29-34, 97-114, 119, 301-335, 343, 345, 363, 411, 426-427, 470-471 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/twodim_base.py 177 100 44% 34-40, 95-98, 153, 158, 211, 216, 220, 295, 300-303, 346-363, 367, 412-424, 433, 469-472, 498-501, 505, 582-597, 602-615, 741-752, 821-823, 904-906, 911, 938-940, 1023-1025, 1057-1059 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/type_check.py 138 78 43% 70-77, 81, 112-114, 159-160, 202-203, 240-244, 339-340, 390, 395-397, 401, 498-521, 526, 574-583, 588-590, 618, 696, 713, 753-769 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/ufunclike.py 57 30 47% 24-36, 50-53, 65, 70, 117-124, 188-196, 260-268 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/utils.py 454 363 20% 38-46, 83-84, 96-97, 116, 123-124, 221, 260-277, 329-379, 391-407, 415-431, 452-482, 537-628, 672-677, 737-812, 835-947, 950-956, 1028-1043, 1056-1070 /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py 678 507 25% 88, 91, 94, 97, 100, 129, 133, 142, 146-149, 153-154, 165-174, 177-185, 188-190, 196, 203, 208, 212, 230, 235, 286-306, 310, 378-395, 399, 456-467, 550, 618-666, 756-764, 770, 890-982, 1075, 1081, 1159-1176, 1179-1181, 1314-1333, 1453-1473, 1624-1639, 1652-1657, 1666-1674, 1678, 1761-1797, 1801, 1898-1906, 1912, 1995-2011, 2092-2101, 2153-2160, 2166, 2266-2328, 2354-2356, 2360, 2514-2611, 2617-2618, 2707-2739, 2750-2760, 2780-2801, 2806-2812 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ma/core.py 2405 1672 30% 102-113, 122, 124, 204-212, 217-222, 270-277, 282-291, 342, 393, 414-425, 441, 443-451, 456-457, 463-468, 532-534, 543-547, 575-579, 628, 633-636, 646-663, 713, 768-776, 804, 812-813, 831-832, 848-853, 868-869, 884-885, 895, 928-969, 1011-1050, 1057-1080, 1087-1105, 1112-1116, 1155-1192, 1284-1291, 1295-1297, 1305, 1534-1537, 1547, 1621-1636, 1683, 1726-1753, 1788-1809, 1814-1817, 1924-1943, 1969, 1995, 2021, 2047, 2073, 2101-2103, 2139-2143, 2179-2183, 2244-2251, 2323-2331, 2370, 2400, 2407, 2414, 2421, 2424, 2439-2447, 2489-2495, 2525-2550, 2581-2590, 2647-2653, 2656, 2659-2670, 2674-2676, 2696-2703, 2834, 2840, 2845-2847, 2857-2861, 2869-2871, 2874-2875, 2879-2882, 2887, 2889, 2895-2896, 2900-2909, 2915-2932, 2938, 2943, 2956, 3012, 3033, 3037-3040, 3047, 3054-3058, 3062, 3065, 3074-3121, 3180-3183, 3190-3194, 3204-3210, 3236-3251, 3261-3267, 3276, 3283-3286, 3291, 3301-3330, 3347-3402, 3411-3419, 3427-3431, 3438-3500, 3516, 3532-3535, 3539, 3554-3555, 3570-3571, 3576, 3591-3594, 3599, 3635, 3660, 3664-3665, 3697-3706, 3710-3725, 3778, 3781, 3786, 3789-3790, 3794-3809, 3833-3836, 3898-3908, 3915-3939, 3942, 3949-4026, 4031-4040, 4053-4104, 4117, 4130, 4137-4139, 4148, 4155-4157, 4164, 4168-4170, 4179, 4186-4188, 4195-4197, 4204, 4211-4213, 4220, 4227-4229, 4236, 4243-4253, 4260-4269, 4276-4285, 4292-4303, 4310-4321, 4328-4339, 4346-4360, 4367-4373, 4380-4385, 4407-4409, 4434-4436, 4497-4538, 4588, 4652-4658, 4673-4676, 4739-4761, 4785-4787, 4815, 4841-4855, 4871-4885, 4983, 4990-4997, 5037, 5078-5099, 5133-5140, 5160-5181, 5206-5213, 5241-5260, 5293-5300, 5317-5363, 5380-5388, 5401-5413, 5479-5493, 5535-5538, 5572-5575, 5648-5658, 5692-5724, 5788-5792, 5826-5859, 5936-5947, 5950-5953, 5956-5959, 5964-5985, 6024-6046, 6057-6061, 6101, 6116, 6163-6175, 6184-6186, 6200-6203, 6209, 6214-6221, 6229-6231, 6241-6257, 6262, 6269-6284, 6287-6291, 6294-6302, 6308-6316, 6319, 6343, 6356-6366, 6462-6469, 6472, 6475, 6478, 6481-6485, 6491-6500, 6505, 6510, 6524, 6527, 6530, 6536-6542, 6559, 6614-6616, 6640-6647, 6651-6679, 6683-6695, 6698-6705, 6710-6717, 6723-6729, 6767-6768, 6775, 6812-6813, 6833-6865, 6872-6882, 6898-6908, 6923, 6967-6983, 6998-7001, 7016-7022, 7037-7043, 7059-7062, 7083-7101, 7136-7139, 7156-7158, 7216-7222, 7230, 7237, 7243, 7308-7340, 7388-7415, 7442-7448, 7527-7540, 7605-7625, 7636-7642, 7650-7660, 7670-7682, 7710, 7738, 7783-7796, 7871-7904, 8001, 8011, 8017, 8082, 8116-8127, 8186 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/extras.py 560 464 17% 48, 101-102, 152-154, 207-209, 259, 262, 272-280, 306-318, 333-341, 364-369, 376-451, 459-477, 587-631, 700-714, 719-799, 823-842, 894-896, 911-914, 928-931, 975-981, 1024-1030, 1049-1063, 1078-1087, 1113-1119, 1133-1146, 1168-1188, 1209-1210, 1225, 1248-1253, 1267-1301, 1358-1374, 1425-1461, 1486-1487, 1491-1494, 1569-1576, 1621-1626, 1674-1684, 1744-1760, 1769-1789, 1825-1828, 1864-1867, 1880-1884, 1894-1921 /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py 238 158 34% 15-33, 69, 123-128, 131-139, 142, 149, 151, 153, 157, 160, 173-187, 190-213, 216-221, 224, 227-228, 231, 234-235, 238, 244-251, 257-260, 284, 319, 372, 411, 445, 479, 513, 546, 569, 609, 644, 683, 718, 757, 790, 830-835, 865, 894, 933, 966, 998-1001, 1011-1032, 1089-1111 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/__init__.py 18 7 61% 171-180 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/_polybase.py 419 296 29% 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 193-198, 216, 234, 252, 280-288, 291-304, 307-311, 314-324, 327-329, 337-367, 375-380, 389-394, 398-403, 409, 413-462, 469-473, 476, 481-483, 486, 489, 494, 497, 500-505, 508-513, 516-521, 527-532, 535-538, 541-544, 547-556, 559-561, 564-568, 571-575, 578-582, 586, 591, 594-597, 600-603, 606-614, 617-622, 625, 640, 653, 678, 700-701, 723-730, 761-767, 796, 823-829, 849-851, 865-866, 894-898, 971-986, 1014-1027, 1054-1060, 1091-1099, 1137-1141 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/chebyshev.py 357 294 18% 152-155, 177-180, 207, 243-274, 302-306, 333-340, 389-394, 441-455, 508-511, 566, 608, 652, 686-698, 742-747, 797-814, 855-872, 935-964, 1052-1091, 1153-1175, 1224, 1277, 1328, 1384, 1422-1437, 1490, 1544, 1670, 1700-1715, 1766-1776, 1827-1843, 1881-1888, 1915-1916, 1946-1953, 1979-1986, 2065-2069 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite.py 267 214 20% 134-139, 180-197, 251-254, 310, 350, 390, 432-443, 485-509, 557, 594, 652-677, 763-799, 871-895, 944, 997, 1048, 1104, 1151-1165, 1218, 1272, 1403, 1433-1448, 1502-1512, 1544-1555, 1594-1622, 1649-1650 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite_e.py 264 211 20% 135-140, 181-197, 250-253, 309, 349, 389, 427-438, 480-504, 550, 587, 645-670, 756-792, 864-887, 936, 989, 1040, 1096, 1143-1156, 1209, 1263, 1395, 1426-1441, 1495-1505, 1537-1548, 1587-1615, 1641-1642 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/laguerre.py 252 200 21% 134-138, 179-193, 245-248, 304, 345, 385, 427-439, 481-505, 551, 588, 646-674, 761-798, 870-893, 942, 995, 1046, 1102, 1149-1162, 1215, 1269, 1400, 1429-1444, 1498-1508, 1547-1572, 1598-1599 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/legendre.py 261 209 20% 140-145, 193-207, 261-264, 319, 361, 405, 447-461, 505-529, 578, 609, 672-701, 789-829, 891-914, 963, 1016, 1067, 1123, 1161-1176, 1229, 1283, 1411, 1441-1455, 1506-1516, 1555-1584, 1611-1612 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polynomial.py 221 166 25% 145-148, 212, 248, 285, 317-325, 361-363, 400-421, 460, 515-542, 623-661, 745-757, 835-845, 895, 948, 999, 1055, 1096-1109, 1157, 1211, 1361, 1390-1401, 1454-1464, 1514, 1518, 1522-1529 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polyutils.py 229 204 11% 71-77, 130-153, 200-208, 248-254, 297-301, 366-368, 372-374, 422-443, 452-453, 469-483, 497-513, 527-529, 547-565, 571-578, 584-592, 606-680, 697-713, 732-750 /home/admin/.local/lib/python3.8/site-packages/numpy/random/__init__.py 17 1 94% 210 /home/admin/.local/lib/python3.8/site-packages/numpy/random/_pickle.py 22 12 45% 31-37, 54-60, 77-83 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/decorators.py 74 61 18% 61-65, 100-105, 143-186, 226-251, 282-304, 323-329 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/nosetester.py 174 157 10% 36-58, 96-109, 164-193, 212-230, 233-250, 259-260, 276-324, 397-463, 523-536, 540-544 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/utils.py 873 732 16% 59-75, 89-95, 109-113, 127-132, 146-151, 156-186, 196-208, 220-244, 249-272, 325-432, 463-473, 545-599, 660-698, 716, 741-745, 749, 753, 758-763, 778-781, 784-787, 790, 795-796, 806-852, 933-934, 1015-1046, 1127-1128, 1135, 1165-1206, 1231-1252, 1289-1290, 1300, 1329-1330, 1353-1354, 1380-1401, 1437-1447, 1455-1473, 1583-1594, 1639-1646, 1681-1709, 1717-1724, 1730-1738, 1743-1749, 1797-1803, 1808-1814, 1844-1850, 1878-1918, 1935-1939, 1956-1961, 2009-2011, 2014-2019, 2022-2027, 2105-2112, 2115-2124, 2127-2149, 2171, 2201, 2205-2234, 2237-2242, 2246-2293, 2300-2305, 2310-2342, 2386-2392, 2403-2409, 2414-2431, 2439-2460, 2465-2476, 2481-2500, 2509-2520 /home/admin/.local/lib/python3.8/site-packages/numpy/version.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/oauth2client/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/oauth2client/_helpers.py 98 60 39% 119-133, 139-140, 156-159, 174-179, 194-202, 222-227, 243-246, 250-255, 272-274, 278, 302-307, 323-328, 333-334, 339-341 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_openssl_crypt.py 40 38 5% 18-135 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pkce.py 14 8 43% 41-49, 66-67 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pure_python_crypt.py 63 39 38% 55-62, 73, 88-92, 113-125, 136, 147-148, 166-184 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pycrypto_crypt.py 38 22 42% 34, 48-49, 64-75, 87, 98-99, 116-124 /home/admin/.local/lib/python3.8/site-packages/oauth2client/client.py 708 520 27% 147-148, 184-187, 213, 222, 231, 239, 255-274, 283, 298-314, 328, 351, 358-359, 367-368, 378, 388, 395, 405-409, 419-423, 434-438, 489-506, 535-536, 545, 554, 562, 580-581, 595-596, 610-633, 641-652, 660-664, 677, 689-697, 701, 705-707, 711-712, 716-722, 726-733, 748-763, 774-819, 827, 841-863, 871, 885-902, 944, 956-960, 971, 980, 996-1005, 1014-1030, 1039-1045, 1101, 1111, 1118, 1124-1147, 1152, 1171-1174, 1187-1190, 1207-1230, 1249-1261, 1271, 1287-1298, 1310-1315, 1331-1340, 1344-1351, 1362-1379, 1385-1415, 1420, 1427, 1433-1435, 1439-1441, 1472-1481, 1484-1491, 1495, 1503, 1515, 1525-1526, 1552-1561, 1575-1584, 1601-1614, 1668-1680, 1733-1737, 1754-1775, 1780-1802, 1877-1893, 1913-1941, 1951-1989, 2014-2089, 2133-2169 /home/admin/.local/lib/python3.8/site-packages/oauth2client/clientsecrets.py 48 35 27% 78-106, 110-111, 115-116, 120-126, 162-173 /home/admin/.local/lib/python3.8/site-packages/oauth2client/crypt.py 83 57 31% 41, 46-48, 64-65, 87-102, 117-123, 142-150, 178-203, 224-250 /home/admin/.local/lib/python3.8/site-packages/oauth2client/file.py 38 27 29% 32-33, 44-59, 67-72, 83-87, 95 /home/admin/.local/lib/python3.8/site-packages/oauth2client/service_account.py 191 133 30% 106-116, 135-140, 169-191, 219-221, 251, 286-296, 329-331, 367-368, 375-384, 400, 409, 414, 435-475, 478, 481-493, 506-520, 541, 547-549, 572-574, 599-600, 612-620, 625, 629, 634-649, 660, 668, 671-685 /home/admin/.local/lib/python3.8/site-packages/oauth2client/tools.py 89 55 38% 125-135, 193-251, 256 /home/admin/.local/lib/python3.8/site-packages/oauth2client/transport.py 89 66 26% 39, 42, 45, 58, 73, 86, 101-107, 123-134, 150-201, 217-251, 279-280 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/__init__.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/cupy.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/dispatch.py 55 33 40% 37-44, 64-69, 79-88, 97-106, 132, 139, 145 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/jax.py 13 8 38% 17-27 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/object_arrays.py 24 20 17% 33-60 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/tensorflow.py 63 50 21% 17-34, 41-56, 65-76, 83-94, 103-106, 113, 120-122, 126-128 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/theano.py 25 18 28% 16-24, 30-42, 47-53 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/torch.py 61 47 23% 22-28, 34, 42-45, 51-95, 100-105, 127-128 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/blas.py 77 72 6% 55-120, 168-243 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/contract.py 315 279 11% 30-44, 48-71, 75-87, 196-330, 337-353, 358, 365-366, 373-374, 470-507, 511, 518-527, 536-600, 611-626, 634-647, 657-667, 674-678, 681-686, 691-693, 710-719, 735-771, 774-778, 781-787, 797, 861-882 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/helpers.py 70 58 17% 42-50, 76-79, 125-134, 168-173, 228-283 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/parser.py 124 106 15% 32, 46, 64-66, 77-84, 97-99, 113-119, 137-138, 154, 183-186, 199-206, 212-243, 272-356 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/path_random.py 165 131 21% 18-25, 87-101, 107, 111, 116-139, 144-155, 158-160, 163, 166-204, 208-209, 247-283, 289-304, 310-319, 355-359, 367-370, 376-378, 384-385 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/paths.py 440 368 16% 51-56, 60, 71-77, 88-97, 130-138, 146-149, 183-236, 243, 247, 257, 267, 274, 312-319, 323, 352-448, 452-453, 462-472, 476-482, 486-496, 502-505, 514-615, 657-661, 700-719, 749-767, 778, 786-794, 809-816, 825-832, 842, 853-865, 903-916, 955-1053, 1057-1058, 1076-1077, 1092-1095, 1117, 1125-1129 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/sharing.py 96 58 40% 26, 32, 36, 40-43, 68-74, 81, 88-90, 98-103, 112-119, 130-139, 150-168, 185-190, 196-201 /home/admin/.local/lib/python3.8/site-packages/packaging/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/packaging/_structures.py 36 16 56% 8, 11, 14, 17, 20, 23, 26, 29, 37, 40, 43, 46, 49, 52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/packaging/version.py 163 65 60% 69, 76, 81-84, 87-90, 93-96, 99-102, 105-108, 198, 228, 236-261, 272-273, 289-290, 305-306, 317, 328, 340, 355, 371-380, 397, 408, 419, 428, 439, 450, 461, 470, 472, 474, 476, 482-484, 497, 527, 533, 547, 560 /home/admin/.local/lib/python3.8/site-packages/pandas/__init__.py 33 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_config/__init__.py 11 2 82% 39-40 /home/admin/.local/lib/python3.8/site-packages/pandas/_config/config.py 313 149 52% 119-121, 123, 144-171, 175-184, 188-201, 205-206, 217-226, 229-240, 243, 266-268, 433-438, 441-444, 447-449, 489, 491, 502, 504, 511, 517, 566-571, 589-593, 606-607, 623, 634, 644, 659-675, 681-700, 705-734, 812, 834, 838, 849-855, 881-882, 907-909 /home/admin/.local/lib/python3.8/site-packages/pandas/_config/dates.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_config/display.py 24 7 71% 27-28, 32-38, 42 /home/admin/.local/lib/python3.8/site-packages/pandas/_config/localization.py 46 33 28% 41-51, 71-78, 98, 137-169 /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/tslibs/__init__.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/window/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/__init__.py 396 256 35% 121-125, 253-255, 265, 276, 292-317, 325-330, 338, 343, 350-351, 358-359, 363-367, 371-385, 389-392, 396-399, 403, 407-410, 416-418, 424, 428-429, 433-437, 441-450, 462-468, 473-477, 481, 485, 489-492, 496-497, 501-503, 509-511, 515, 519, 524-525, 529-530, 534-543, 547, 551-552, 585-664, 739-771, 775-798, 802-805, 818, 822, 830, 834, 840, 859-872, 891-892, 909-911, 934-942, 959-967, 975, 979, 983, 987, 991, 995, 1005-1047 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_io.py 130 98 25% 30, 76-79, 102-111, 209-248, 267-278, 303-308, 329-337, 358-366, 388-418, 426-435 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_random.py 9 3 67% 14-19, 29 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_warnings.py 61 48 21% 86-102, 112-115, 126-150, 163-188, 196-199, 205-216 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/asserters.py 402 358 11% 92-142, 163-170, 176-177, 234-343, 352-377, 394-416, 420-435, 440-443, 471-504, 525-535, 539-542, 548-554, 560-564, 570-599, 633-679, 732-782, 877-1032, 1152-1224, 1253-1280, 1292-1313, 1317-1318, 1330-1336, 1350, 1358-1364, 1373-1378 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/compat.py 10 6 40% 10-14, 22-24 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/contexts.py 86 61 29% 46-47, 72-89, 114-134, 145-150, 173-184, 189-202, 206-213 /home/admin/.local/lib/python3.8/site-packages/pandas/_typing.py 149 33 78% 34-84, 204, 209, 213, 217, 223, 229, 233, 238, 243, 249, 253, 257, 262, 314 /home/admin/.local/lib/python3.8/site-packages/pandas/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/extensions/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/indexers/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/interchange/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/types/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/arrays/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/compat/__init__.py 33 14 58% 41-44, 56, 68, 80, 92, 104, 118, 131, 148-154 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/_constants.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/compat/_optional.py 49 29 41% 71-88, 145, 149-173 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/compressors.py 27 12 56% 16-17, 30-41, 54, 69 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/numpy/__init__.py 18 2 89% 19, 25 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/numpy/function.py 161 57 65% 68-86, 99-103, 113-115, 125-127, 158-164, 175, 180, 192-201, 221-226, 325-330, 345-351, 365-371, 388-391 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/pickle_compat.py 93 66 29% 27-57, 147-149, 157-174, 181-190, 195-196, 209-220, 233-234, 244-249 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/pyarrow.py 17 6 65% 17-22 /home/admin/.local/lib/python3.8/site-packages/pandas/core/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/_numba/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/_numba/executor.py 18 10 44% 41-59 /home/admin/.local/lib/python3.8/site-packages/pandas/core/accessor.py 84 27 68% 28, 34, 44-46, 55, 58, 61, 96, 99, 112, 224-230, 306-319, 324-326, 331-333, 338-340 /home/admin/.local/lib/python3.8/site-packages/pandas/core/algorithms.py 440 387 12% 87-97, 128-181, 200-214, 221-230, 262-266, 281-288, 390, 408-413, 418-437, 457-531, 570-593, 747-797, 830-904, 923-936, 960-965, 985-1006, 1040-1065, 1103-1157, 1246, 1252-1253, 1315-1348, 1378-1461, 1519-1599, 1604-1614, 1624-1629, 1655-1672 /home/admin/.local/lib/python3.8/site-packages/pandas/core/api.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/apply.py 624 504 19% 60-67, 83-90, 112-139, 143, 154-174, 191-241, 247-265, 271-287, 297-372, 382-468, 479-496, 507, 519-551, 560-578, 593, 597, 609, 614, 619, 625, 631, 635, 639, 643, 648-678, 681-701, 710-741, 746-767, 770-795, 798-801, 804-820, 823-841, 846-851, 859, 863, 867, 874-908, 915-916, 920-942, 946, 950, 959-971, 975-984, 999-1001, 1011-1025, 1028-1053, 1056-1057, 1063-1087, 1100-1102, 1112, 1115, 1129, 1139, 1142, 1184-1203, 1227, 1258-1288, 1303, 1338-1382, 1410-1422, 1450-1466, 1492-1502 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/datetimelike_accumulations.py 26 17 35% 34-55, 59, 63, 67 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/masked_accumulations.py 30 20 33% 45-76, 80, 84, 88, 92 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/masked_reductions.py 48 32 33% 49-60, 71, 84, 112-124, 134, 144, 154-156, 167-172, 185-190 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/putmask.py 50 35 30% 26, 42-59, 75-101, 110-115, 122-129, 141-152 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/quantile.py 46 37 20% 34-39, 77-106, 135-143, 179-216 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/replace.py 48 37 23% 33-40, 63-106, 128-150 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/take.py 196 115 41% 32-33, 44, 55, 96, 98-102, 107-114, 128-129, 155, 204-224, 237-284, 298, 310-322, 343-345, 356-369, 375-384, 520-532, 544-561, 574-575, 583 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/transforms.py 21 17 19% 13-42 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arraylike.py 220 143 35% 36, 40, 44, 48, 52, 56, 60, 66, 70, 74, 78, 82, 86, 90, 96, 186, 190, 194, 198, 202, 206, 210, 214, 218, 222, 226, 230, 234, 238, 242, 246, 261-412, 422-427, 437-462, 469-473, 484-489, 496-527 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/__init__.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/_mixins.py 197 129 35% 69-74, 85-92, 114, 118, 126-153, 163-173, 178-182, 186-187, 190, 193, 198-201, 206-209, 212-213, 222-228, 237-238, 242-245, 248-250, 253, 257, 264, 270-286, 292-293, 299-331, 337-339, 358-360, 378-381, 402-413, 433-451, 460-467, 475, 494-496 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/_ranges.py 75 67 11% 49-90, 121-157, 167-207 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/_arrow_utils.py 24 16 33% 17-20, 43-61 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/array.py 1003 805 20% 105-109, 117-120, 142-147, 154-159, 168-181, 234-245, 252-285, 294-348, 374-417, 423-429, 433, 437, 440, 443, 446, 449, 454-456, 459-460, 463-487, 490-536, 539, 542, 545-549, 556, 563, 573, 577-585, 589, 597, 655, 713, 723-732, 735-748, 751, 754, 766, 776, 785-834, 838-844, 860-861, 868-895, 898, 927, 936-944, 1006-1042, 1051-1081, 1091-1104, 1123-1151, 1168-1175, 1207-1225, 1251-1345, 1369-1424, 1438-1491, 1508-1530, 1548-1571, 1575-1588, 1613-1632, 1658-1685, 1689, 1698-1700, 1708-1718, 1723-1733, 1736-1739, 1742-1745, 1756-1764, 1767-1774, 1779-1781, 1786-1788, 1791-1804, 1807-1824, 1827, 1830-1832, 1835-1837, 1842-1846, 1853-1857, 1860, 1863, 1866, 1869, 1872, 1875, 1878, 1881, 1884, 1887, 1890, 1893, 1896, 1899, 1902, 1905-1909, 1912-1916, 1919-1923, 1931-1940, 1943-1946, 1949-1951, 1954-1956, 1959, 1964-1967, 1970-1981, 1984-1986, 1989-1991, 1994-1996, 1999-2001, 2010-2016, 2019-2021, 2024-2026, 2029-2033, 2037, 2041, 2045, 2052, 2058, 2061, 2065, 2069, 2073, 2077, 2081, 2085, 2089, 2093, 2097-2102, 2106, 2109, 2118-2146, 2154, 2162, 2170, 2173-2181, 2189-2206 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/dtype.py 146 97 34% 36, 89, 91, 98, 105-150, 162-168, 172-175, 180, 191-193, 207, 212-236, 247-265, 273, 284, 290-304, 310-312 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/base.py 385 253 34% 92, 261, 285, 304, 311, 315, 350, 395, 405, 414-415, 424-434, 447, 454, 485-490, 501, 508, 517, 524, 533, 541, 545, 549, 571-592, 614, 624, 651, 689-692, 723-726, 751-754, 790-811, 822, 857-872, 882-883, 934-937, 957-971, 987, 1011, 1059-1066, 1117-1119, 1218, 1228, 1248-1250, 1257-1269, 1272-1285, 1311-1313, 1326, 1330, 1349, 1371, 1380, 1412, 1438-1444, 1467-1469, 1472-1473, 1497-1501, 1523-1528, 1545-1553, 1565-1571, 1585-1588, 1610-1618, 1635-1640, 1660, 1663-1686, 1691, 1694, 1710, 1714-1731, 1735, 1739-1744, 1748, 1752-1757, 1826-1865, 1869, 1873 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/boolean.py 169 122 28% 36-38, 71, 75, 79, 90, 93, 97, 101, 109-142, 162-229, 297-303, 307, 319-333, 341-343, 346-378, 383-392 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/categorical.py 725 567 22% 112, 128-188, 224-243, 368-457, 464, 470-471, 477, 481, 485, 489, 503-550, 556, 582-625, 669-692, 724, 731, 749-751, 774-788, 803-804, 815-818, 829, 840, 891-908, 971-980, 1016-1023, 1068-1092, 1135-1148, 1184-1197, 1270-1280, 1293-1297, 1317-1326, 1342-1348, 1352-1374, 1381-1391, 1395, 1420, 1439, 1461, 1484-1503, 1522-1529, 1544-1548, 1552-1553, 1610, 1620, 1626, 1701-1711, 1725-1728, 1749-1767, 1774, 1777-1779, 1784-1786, 1794-1797, 1804-1807, 1814, 1821-1829, 1835-1855, 1861-1884, 1887-1888, 1893-1899, 1905-1914, 1920-1949, 1977-1983, 2003-2018, 2035-2050, 2053-2062, 2096, 2100-2101, 2115-2120, 2126-2150, 2166-2169, 2184, 2195-2205, 2248-2258, 2261-2294, 2304-2309, 2313-2315, 2445-2449, 2453-2454, 2457, 2460, 2467-2469, 2472-2477, 2489-2495, 2523-2538, 2557-2579, 2599-2604 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/datetimelike.py 925 699 24% 152, 171-182, 207, 212, 223, 243, 265, 285, 293, 299, 302-305, 318, 333, 337, 344-346, 350, 357, 369-378, 384-405, 421-430, 435, 442-489, 493, 497, 501, 505, 511, 522-541, 545-547, 554-587, 613-646, 663-673, 676-724, 727-732, 739-745, 759-761, 776-818, 824, 831, 838, 859-865, 875-877, 886-891, 895-901, 909, 916, 920, 924, 930-985, 1009-1019, 1028-1035, 1039-1071, 1075-1081, 1085-1098, 1102-1111, 1115-1132, 1136-1144, 1147, 1157-1167, 1179-1185, 1189-1199, 1206-1217, 1230-1237, 1243-1264, 1282-1300, 1303-1309, 1315-1365, 1369, 1373-1423, 1426-1458, 1461-1467, 1470-1476, 1487, 1501-1505, 1519-1523, 1549-1560, 1564-1570, 1573-1580, 1637-1638, 1799-1866, 1870, 1877, 1881-1888, 1903-1928, 1937, 1943, 1950, 1953-1967, 1974-1980, 1985-1993, 1997-2017, 2026, 2035, 2044, 2051, 2056, 2062, 2077-2091, 2100-2109, 2117-2147, 2152, 2157, 2178-2184, 2210-2221, 2241-2249, 2265-2267 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/datetimes.py 617 485 21% 90-91, 109-112, 117-148, 198, 263-265, 275-287, 291, 309-370, 389-502, 508-514, 517, 520-522, 529-531, 556, 569, 574, 584, 591, 595, 601-605, 615-634, 641-701, 709-713, 722-729, 733-748, 756-782, 794-797, 863-873, 1021-1058, 1071, 1111-1118, 1164-1190, 1244-1250, 1301-1307, 1319-1321, 1330, 1343-1345, 1381-1390, 1911-1917, 1971-1979, 2022-2134, 2172-2202, 2229-2256, 2281-2290, 2315-2344, 2370-2397, 2422-2440, 2446-2453, 2478-2486, 2521-2595 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/floating.py 34 3 91% 36, 40, 51 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/integer.py 70 9 87% 36, 40, 50-57 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/interval.py 638 486 24% 104, 220, 238-275, 288-293, 305-379, 389, 395-400, 458-460, 536-548, 608-631, 645-661, 674-677, 684, 688, 693, 699, 702, 706, 710, 715-731, 734-738, 742-817, 821, 825, 829, 833, 837, 841, 851-859, 864-878, 881-895, 926-935, 956-996, 999-1002, 1023-1033, 1043-1046, 1049, 1052-1077, 1133-1146, 1150-1168, 1171-1182, 1185-1205, 1225, 1233-1260, 1266-1270, 1273-1274, 1284-1286, 1293-1295, 1302, 1309-1313, 1372-1385, 1396, 1438-1444, 1466-1474, 1487-1498, 1504-1550, 1573-1577, 1582-1591, 1608-1613, 1616-1624, 1632-1635, 1683-1686, 1691-1720, 1724-1730, 1736-1748, 1753-1757, 1776-1796 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/masked.py 572 451 21% 88-90, 121-134, 140-141, 145, 149, 153, 158-167, 173-198, 204, 214-231, 234-248, 251-264, 267, 271, 275, 278-280, 283-285, 288-290, 294-296, 300, 326-330, 336, 339, 342, 345, 415-439, 443-446, 450, 454, 458, 461-504, 513, 521-592, 598-600, 609, 614-625, 628-730, 735-773, 782-821, 824, 828, 832, 840-842, 854-876, 881-898, 901-904, 914-915, 924-932, 939-971, 975, 994-1021, 1025-1037, 1050-1078, 1084-1097, 1100-1109, 1119-1135, 1147-1155, 1160-1167, 1174-1182, 1189-1197, 1202-1203, 1211-1212, 1281-1298, 1362-1380, 1385-1391 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/numeric.py 152 112 26% 41, 52, 56, 60, 64, 72-114, 118, 125-136, 145, 149-235, 248-263, 267-268, 274-280, 286-289 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/numpy_.py 186 130 30% 80-95, 101-120, 123, 130, 136, 143-188, 194-202, 205, 208-211, 214-218, 231-233, 243-245, 250-254, 259-263, 273-277, 287-291, 302-304, 315-319, 331-335, 347-351, 363-367, 378-382, 393-397, 408-420, 426, 429, 432, 435, 438-459, 466-472 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/period.py 420 311 26% 83-92, 105-107, 179, 215-236, 247-249, 259-274, 280, 297-298, 302-319, 331-338, 341, 344-346, 353, 361, 364-370, 376-397, 479, 497-536, 541, 589-609, 615-617, 626-643, 650-664, 672-677, 680-688, 708-712, 715-718, 731-740, 754-781, 803-818, 837-847, 912-943, 948, 953, 977-993, 1018-1033, 1037-1081, 1094-1128, 1132-1143 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/accessor.py 110 78 29% 20, 30-31, 34, 46-47, 50, 53-58, 102-108, 185-190, 216-218, 232-234, 265-287, 309-312, 334-355, 362-363, 367-386 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/array.py 786 648 18% 107-123, 155-158, 180-266, 275-287, 378-497, 506-510, 538-554, 557-579, 585-586, 590, 594, 604, 618, 622, 631, 635, 642-645, 649-651, 654, 658, 661-664, 668, 682, 696, 701-706, 746-772, 775-799, 809-820, 823-834, 838, 849-853, 868-893, 900, 907, 913-1001, 1004-1012, 1017-1030, 1035-1097, 1100-1120, 1128-1133, 1136-1137, 1143-1195, 1254-1274, 1316-1326, 1336, 1341-1344, 1351-1361, 1364-1367, 1374-1384, 1398-1405, 1419-1426, 1454-1470, 1490-1498, 1512-1521, 1538-1539, 1556-1557, 1572-1592, 1595-1618, 1621-1624, 1627-1630, 1639-1702, 1709-1739, 1742-1765, 1774-1782, 1785, 1788, 1791, 1794, 1800-1803, 1808, 1832-1870, 1875, 1880, 1885-1892 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/dtype.py 143 94 34% 39, 85-99, 104, 109-140, 156, 159-160, 178, 182, 186, 193, 197, 201, 205, 208, 219-221, 250-273, 297-307, 311-317, 357-370, 396-398, 403-426 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/string_.py 257 193 25% 58-65, 105, 110-120, 124, 154-165, 180-185, 193-215, 225-227, 315-320, 324-336, 340-365, 371, 375-377, 383-390, 393-396, 399-429, 435, 438-468, 473-476, 479-483, 486-490, 493-497, 500-503, 512-517, 520-547, 560-608 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/string_arrow.py 200 142 29% 53-55, 112-116, 128, 132-152, 158, 165, 168-170, 174-185, 188-201, 204-216, 231-283, 288-306, 309-310, 313-314, 325-331, 336-338, 343-345, 348-349, 352-353, 356-357, 360-361, 364-365, 368-369, 372-373, 376-377, 380-381, 384-385, 388, 391, 394-398, 401-405, 408-412 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/timedeltas.py 440 333 24% 78, 83-96, 142, 164-167, 186, 197-199, 207-214, 218-227, 242-271, 279-319, 325-331, 334, 338, 348-368, 371-384, 400-407, 419-426, 432-441, 447-449, 454-460, 466-467, 473-500, 509-550, 553-559, 567-581, 586-610, 615-631, 635-660, 664-678, 683-685, 690-692, 697-702, 707-712, 715-718, 721, 725, 784-785, 795, 826-853, 896-966, 985-1006, 1039-1042, 1046-1062 /home/admin/.local/lib/python3.8/site-packages/pandas/core/base.py 322 197 39% 75-82, 113, 120, 126-131, 138-144, 163, 172-178, 196-200, 204-207, 212, 217-230, 233-246, 261, 264, 283, 288, 299-300, 320, 324, 331, 348-350, 357, 364, 429, 526-561, 566, 608-610, 666-678, 722-724, 730-742, 763, 780-784, 799, 802, 818-823, 849-926, 1015, 1025-1031, 1068-1071, 1082, 1093-1095, 1106-1108, 1135-1144, 1164-1177, 1293, 1302, 1311-1323, 1331-1333, 1337, 1340-1350, 1357 /home/admin/.local/lib/python3.8/site-packages/pandas/core/common.py 195 126 35% 57, 77-81, 85-92, 126-138, 141-145, 164, 176, 183, 190, 197, 204, 211, 221, 226, 230-258, 276-287, 291-293, 301, 310, 322, 334, 342, 352-364, 379, 405-413, 418, 425, 453-478, 510-518, 527-533, 543-548, 563-568, 576, 626, 634, 653 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/align.py 99 76 23% 32-35, 42-51, 57, 64, 71-80, 87-142, 149-165, 188-213 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/api.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/check.py 8 1 88% 8 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/common.py 29 23 21% 14-16, 24-48 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/engines.py 50 23 54% 23, 37-42, 53-55, 63, 77-82, 88, 113-121, 134, 137 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/eval.py 111 90 19% 27, 51-72, 88-89, 95-99, 119-120, 147-149, 153-167, 299-413 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/expr.py 361 213 41% 65-66, 84-91, 114-117, 165-166, 262, 314, 397-401, 404-415, 418-421, 424, 428-451, 454-459, 462-482, 491, 504-532, 535-537, 540, 543-545, 548, 551, 554, 557, 560-561, 564-565, 571, 574-591, 595-605, 617-635, 638-655, 658-706, 709, 712-730, 733-735, 738-746, 768, 776, 804-809, 813, 816, 819, 822, 828, 835-837 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py 107 70 35% 24, 46, 58-61, 68-70, 75-89, 93-130, 171, 176-188, 196-199, 211-220, 235-240, 255-256, 267-268, 272-273, 281-283 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/ops.py 293 182 38% 72-75, 81-87, 91, 94, 97, 100, 103-117, 129-135, 139, 143-152, 158, 162-167, 171, 175, 179, 183, 188, 191, 195, 200, 214-216, 219, 226-227, 232-234, 238-240, 244, 248, 252-257, 265-273, 281-289, 346-355, 359, 374-387, 405-408, 427-457, 464-490, 493-511, 515, 529-539, 565-571, 577-579, 582, 586-593, 598-599, 603-605, 608-609, 614-617, 620 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/parsing.py 45 33 27% 35-67, 90-93, 125-130, 159-164, 181-195 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/pytables.py 352 257 27% 49-50, 57-61, 64, 68-78, 83, 88-89, 92, 103-106, 109, 112-151, 155-159, 164, 172, 177, 182, 187, 191-192, 200-256, 259, 266-268, 272-278, 282, 285-310, 313-316, 321, 324, 329, 336, 342, 345-368, 373-374, 379-393, 401-404, 411-416, 419, 422-425, 430-444, 449-470, 473, 476, 497-503, 545-585, 588-590, 594-609, 616-619, 623-631, 636-641 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/scope.py 125 84 33% 36-40, 49-53, 60, 76-82, 88-89, 118-120, 153-188, 191-193, 207, 226-246, 261-271, 285-294, 304-314, 330-338, 343, 356-357 /home/admin/.local/lib/python3.8/site-packages/pandas/core/config_init.py 197 20 90% 40-42, 54-56, 68-70, 290-292, 307-310, 345, 421-423, 641-643, 664-672 /home/admin/.local/lib/python3.8/site-packages/pandas/core/construction.py 218 142 35% 67-71, 290-379, 386, 393, 442-450, 453, 464-466, 469-471, 480-491, 520, 524, 530-532, 535-536, 539-542, 548-553, 557-559, 564, 572-576, 580-581, 596, 599, 622-633, 641, 656, 662-679, 696-699, 710, 734-767 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/api.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/astype.py 114 92 19% 40, 50, 57, 83-140, 149-159, 177-193, 220-251, 266-306 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/base.py 140 59 58% 33-36, 104, 124-133, 138, 141, 152, 164, 180, 189, 199, 210, 226-227, 275-284, 310-327, 338, 356, 380-384, 391, 404, 410-412, 416, 420, 472, 478, 482, 486, 492, 509, 517 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/cast.py 756 615 19% 102-103, 134, 151, 172-179, 194-210, 222-236, 245-251, 256, 261, 269-317, 324, 331, 350-422, 437-445, 475-492, 512-527, 532, 537, 545-551, 553, 556, 609-611, 635-640, 649-652, 658, 662-739, 760-764, 779-781, 795-873, 888, 926-944, 972-978, 990-995, 1000-1007, 1051-1168, 1199, 1227-1254, 1273-1294, 1326-1348, 1369-1385, 1407-1410, 1416, 1421, 1426, 1447, 1454, 1460-1465, 1469, 1471, 1477-1479, 1487-1509, 1531-1560, 1566-1570, 1636-1707, 1723-1747, 1767-1910, 1919-1921 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/common.py 323 197 39% 82-89, 105-109, 128-139, 186, 231-234, 265-272, 306, 341-348, 383, 413-419, 451-457, 489-495, 537-548, 579-587, 589, 595-598, 646, 760, 809, 864, 900-906, 944-953, 987, 1025, 1060-1070, 1112, 1115-1124, 1165, 1202, 1286, 1289-1290, 1293, 1298, 1300, 1310-1317, 1382, 1386, 1432, 1451, 1454-1455, 1479, 1488-1491, 1509, 1514-1535, 1557-1602, 1620-1625, 1651-1653, 1674, 1676, 1692-1694, 1706, 1719-1728 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/concat.py 101 87 14% 30, 55-121, 223-286, 291-293, 310-323 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/dtypes.py 621 400 36% 56-64, 102, 105, 109, 114, 187, 193-195, 201-207, 277-306, 329, 337, 340-347, 353-354, 359-365, 380-426, 429-437, 441-477, 488-490, 508-509, 526-545, 560-578, 585, 592, 596-598, 601-628, 679, 684, 687-716, 723, 730, 737, 748-750, 770, 777-785, 789, 794, 799, 802-807, 817-818, 865-885, 888, 895, 899-909, 924-927, 931, 935, 939, 943, 947, 950-963, 966, 972, 980-990, 1001-1003, 1011-1034, 1081-1141, 1145-1153, 1157, 1164, 1175-1177, 1186, 1191, 1203, 1206-1211, 1215, 1218-1230, 1236-1239, 1247-1255, 1263-1287, 1290-1302, 1325-1328, 1331, 1338, 1345, 1352, 1357, 1361, 1365-1373, 1384-1386, 1393, 1400, 1414, 1419, 1423, 1428, 1439, 1446-1459, 1464-1478 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/generic.py 37 4 89% 11-31, 51 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/inference.py 60 20 67% 71, 96, 129-132, 157, 181-186, 259, 292-293, 361-362, 392-393, 430-431 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/missing.py 249 151 39% 57-66, 80, 87, 92, 98, 103, 208, 210, 213-232, 256-263, 286-292, 297, 300, 312, 318-319, 326, 333, 338, 344, 349, 429-432, 449-452, 496-542, 546, 550, 554-581, 588-593, 602-613, 620-622, 653, 655, 660, 663-665, 673-676, 693, 695-699, 701, 707, 711, 717-727, 734-759 /home/admin/.local/lib/python3.8/site-packages/pandas/core/flags.py 34 13 62% 90, 93-94, 99-102, 105-107, 110, 113-115 /home/admin/.local/lib/python3.8/site-packages/pandas/core/frame.py 2171 1666 23% 239-244, 650, 653-657, 661, 673, 675, 688, 692, 697-700, 703, 711-723, 733-758, 772, 777, 798-843, 887-889, 930, 963-968, 975-983, 990-1010, 1019-1020, 1033-1073, 1079-1080, 1088-1094, 1102-1138, 1163, 1188, 1250-1271, 1291-1293, 1347-1352, 1396-1403, 1469-1487, 1497, 1501, 1580-1614, 1620, 1624, 1630, 1636-1643, 1738-1774, 1836-1842, 1851-1864, 1872, 1876, 1986-1988, 2086-2088, 2184-2319, 2404-2479, 2514-2529, 2645-2686, 2712-2714, 2750-2763, 2776, 2789, 2887-2889, 2981-2983, 3014, 3043, 3106-3129, 3300-3342, 3354-3358, 3454-3464, 3563-3605, 3634, 3681, 3691-3692, 3700-3711, 3731-3784, 3792-3811, 3815-3845, 3866-3883, 3909-3919, 3922-3950, 3956-3957, 3961-3997, 4006-4041, 4046-4058, 4061-4099, 4105-4106, 4109-4121, 4124-4131, 4143-4156, 4174-4199, 4209-4224, 4241, 4246-4247, 4266, 4272-4280, 4287, 4291, 4295, 4434-4453, 4457, 4461, 4555-4566, 4651-4709, 4763-4777, 4841-4845, 4860-4871, 4875, 4886-4900, 4912-4915, 4932-4935, 4949-4964, 4984, 5034, 5055, 5080, 5094, 5108, 5258, 5281, 5296, 5311, 5432, 5454, 5467, 5480, 5493, 5543, 5556, 5569, 5582, 5609-5623, 5633-5702, 5716, 5728, 5828-5932, 5946, 5960, 5974, 6149-6220, 6227-6228, 6235, 6239, 6246, 6259, 6272, 6382-6433, 6516-6530, 6626-6673, 6691, 6706, 6735, 6740-6751, 6764, 6767, 6773-6776, 6779-6786, 6793, 6799, 6817, 6833, 6849, 6950, 7078-7096, 7203, 7301, 7366-7379, 7422-7434, 7440-7446, 7449-7458, 7478-7533, 7540-7555, 7569-7574, 7578-7580, 7584-7586, 7713, 7832-7897, 7946-7972, 8090-8126, 8246-8252, 8412-8414, 8577-8579, 8756-8766, 8859-8896, 8959-8963, 8975, 9062-9082, 9104-9110, 9189-9210, 9225, 9237, 9249, 9259-9264, 9412-9423, 9495-9508, 9520-9564, 9729, 9749-9822, 9841-9843, 9938-9975, 10051-10092, 10207-10223, 10297-10359, 10428-10451, 10464-10530, 10539-10558, 10598, 10604-10623, 10629-10648, 10654-10659, 10741-10751, 10761, 10771, 10781, 10874-10960, 10971, 10994, 11066-11076, 11123-11133, 11204-11241, 11277-11281, 11371, 11382, 11393, 11403, 11414, 11425, 11436, 11446, 11457, 11471, 11492, 11504, 11516, 11527, 11545, 11557, 11569, 11580, 11595-11599, 11605-11620 /home/admin/.local/lib/python3.8/site-packages/pandas/core/generic.py 2254 1609 29% 193-200, 279, 292-311, 331-333, 352, 357, 448-451, 457-467, 478, 488, 503-507, 514-515, 520-521, 538, 543-569, 573-579, 590-595, 601, 605, 612, 621, 644, 670, 708, 714-721, 729-731, 744-780, 848-850, 853-856, 962-963, 987-1042, 1055, 1068, 1081, 1216-1256, 1313-1324, 1331, 1414-1417, 1424-1438, 1442-1453, 1457-1462, 1466, 1507-1518, 1588-1589, 1593, 1597, 1629-1631, 1689, 1723-1735, 1775-1778, 1782-1792, 1825-1872, 1891, 1905, 1917-1918, 1922, 1927, 1987, 1997-2010, 2016, 2023-2024, 2035-2067, 2075-2076, 2084-2087, 2095-2100, 2237-2252, 2522-2532, 2678-2682, 2876-2878, 2953-2955, 3032-3034, 3113-3118, 3145, 3172, 3356-3459, 3515-3533, 3560, 3587, 3761-3772, 3798, 3817-3824, 3827, 3907-3909, 3922-3938, 3948-3952, 4064, 4067-4078, 4083-4131, 4134, 4142-4151, 4158-4159, 4172-4174, 4202-4249, 4255-4286, 4293-4294, 4353-4356, 4362, 4466-4474, 4488, 4502, 4516, 4529-4555, 4582-4642, 4657-4660, 4721-4734, 4795-4807, 4821, 4835, 4849, 5003, 5019, 5035, 5051, 5066-5104, 5321-5360, 5368-5389, 5393, 5406, 5418-5451, 5523-5557, 5632, 5707-5709, 5841-5864, 5924-5926, 5953, 5958-5959, 5962-5971, 5988, 6013-6030, 6038-6041, 6052-6058, 6064-6067, 6079-6081, 6085-6093, 6098-6108, 6112, 6116, 6123, 6128, 6158-6159, 6269-6340, 6452-6454, 6458, 6468, 6515-6516, 6661-6691, 6707, 6720, 6733, 6858-6995, 7006, 7017, 7028, 7047, 7072-7078, 7089, 7100, 7111, 7130, 7155-7161, 7174, 7187, 7200, 7218-7401, 7613-7699, 7801-7873, 7939, 7943, 8006, 8010, 8014-8036, 8040-8069, 8178-8229, 8345-8347, 8403-8413, 8483-8498, 8885-8888, 8955-8974, 9028-9038, 9149-9195, 9206-9268, 9394-9449, 9477-9522, 9540-9605, 9623-9741, 9753, 9765, 9777, 9932-9933, 9945, 9957, 9969, 9988-9995, 10109-10149, 10276-10309, 10362-10390, 10534-10572, 10819, 10949-10965, 10977-11001, 11017, 11028, 11042-11067, 11070, 11075, 11080, 11083, 11096-11101, 11113, 11125, 11137, 11151-11158, 11169, 11185, 11201, 11212, 11223, 11234, 11251-11263, 11280, 11292, 11329, 11356, 11379, 11401, 11424, 11440, 11456, 11472, 11488, 11512, 11534, 11556, 11577, 11601, 11623, 11646, 11669, 11687-11703, 11724-11725, 11742-11743, 11765-11788, 11792, 11796, 11800, 11804, 11810, 11816, 11820, 11824, 11828, 11832, 11851-11854, 11871, 11876, 12594-12604 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/base.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/categorical.py 25 20 20% 49-87 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/generic.py 708 575 19% 98-99, 141, 146-155, 158, 216, 220-279, 284-290, 293-330, 355-401, 406-420, 469, 476-488, 494-517, 557-575, 586-635, 639, 649-793, 890-899, 980-981, 1042-1049, 1054-1055, 1061-1066, 1072-1077, 1081-1082, 1086-1087, 1096-1099, 1105-1108, 1113, 1118, 1136-1151, 1156, 1160-1161, 1260-1327, 1332-1354, 1357-1372, 1375-1391, 1400-1448, 1464-1503, 1512-1535, 1538-1578, 1637, 1642-1652, 1655-1688, 1731-1757, 1760-1771, 1786-1819, 1824-1832, 1840-1846, 1849, 1852-1853, 1862-1873, 1920-1935, 2011-2021, 2097-2107, 2222, 2344-2353, 2448-2449, 2521-2528, 2533-2534, 2543-2546, 2555-2558, 2580-2599, 2605, 2616-2624, 2630-2651 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/groupby.py 1169 895 23% 137, 584, 587-591, 594-600, 635, 640, 648, 653, 661, 670-712, 719, 725-740, 744, 775, 795-802, 814-819, 913-944, 947-952, 959-992, 998, 1010-1063, 1072-1089, 1093-1108, 1113, 1117-1124, 1146-1166, 1175, 1182-1199, 1216-1237, 1248-1273, 1284-1310, 1321-1365, 1402-1406, 1422-1428, 1438-1472, 1486-1512, 1517, 1521-1553, 1560-1578, 1585-1598, 1613-1636, 1644-1647, 1655-1678, 1706, 1726, 1740-1771, 1850-1860, 1883-1888, 1941-1971, 2024-2029, 2051-2165, 2193-2209, 2224-2240, 2251-2270, 2275, 2288-2293, 2309-2314, 2369-2384, 2428-2443, 2462-2486, 2495-2524, 2624-2626, 2758-2760, 2776-2778, 2792-2794, 2826-2885, 2910, 2935, 3029, 3036-3093, 3144-3310, 3376-3394, 3451-3453, 3527-3546, 3564-3569, 3582-3587, 3602-3610, 3627-3635, 3686-3774, 3804-3820, 3844-3861, 3884-3902, 3939-3940, 3978-3983, 4000-4006, 4040-4104, 4202-4235, 4247-4258, 4282-4292 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/grouper.py 405 322 20% 54, 250-254, 265-278, 294-309, 332-395, 400-409, 414-420, 425-431, 436-442, 447-454, 458-465, 520-611, 614, 617, 621, 625-639, 646-654, 658, 663-667, 671, 679-686, 692-698, 702-720, 725-783, 787, 822-1019, 1023, 1027-1044 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/indexing.py 105 78 26% 24-28, 114-120, 126-149, 152-155, 158-169, 172-184, 187-226, 230-235, 239-244, 250, 283-284, 293, 300, 303 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/numba_.py 52 39 25% 45-56, 93-119, 153-179 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/ops.py 533 383 28% 99, 121-123, 156-179, 197-220, 234-265, 268-284, 287-296, 312-322, 337-381, 386-397, 408-419, 434-461, 475-497, 519-615, 631-657, 692-697, 701, 705, 708, 712, 725-727, 736-737, 742-748, 754-782, 787-792, 802-819, 824, 828, 832, 839-845, 850-855, 861, 869, 873-878, 883-884, 891-897, 902, 906-908, 912-917, 925-935, 953-959, 987-1001, 1007-1025, 1068-1074, 1081-1086, 1091, 1096-1100, 1111-1125, 1129-1137, 1141-1151, 1160, 1164-1167, 1171, 1175, 1179-1185, 1189-1196, 1211-1216, 1221, 1226, 1229-1239, 1243, 1246, 1252-1254, 1264-1266, 1272-1278 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/objects.py 130 95 27% 55-59, 70, 85-100, 119, 140-142, 153-213, 228, 268-282, 315-318, 336-375, 390 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/utils.py 148 122 18% 29-30, 54-57, 94-99, 114-118, 152-186, 226-234, 270-274, 278-279, 282-284, 300-331, 342-343, 358-370, 390-396, 403-414, 513-555 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/accessors.py 140 92 34% 43, 56-64, 67-80, 85-114, 117, 123-144, 163-170, 173-191, 194-210, 213, 216-229, 332, 336, 368, 438, 467, 475, 554-580 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/api.py 126 97 23% 94-95, 103-109, 138-157, 174-191, 210-308, 332-346, 362-364 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py 2276 1712 25% 187-193, 259-267, 279-297, 366-372, 379-386, 393-400, 407-414, 433, 482, 488, 492-495, 499, 502, 505-513, 516, 518, 522, 528, 533-535, 541, 544, 548, 552-557, 572, 575, 580, 589-606, 609-611, 614-616, 622-625, 658, 671-681, 685, 701-706, 725-738, 756-758, 764-767, 774-776, 800-807, 818, 827-834, 840, 842, 844, 851, 865, 885, 888-919, 925-929, 951, 956-981, 1007-1038, 1080, 1082, 1095, 1109-1117, 1165-1170, 1202-1208, 1212, 1222, 1232-1243, 1252, 1259, 1266-1275, 1287-1300, 1307-1312, 1317-1319, 1330-1341, 1344-1362, 1376-1396, 1411-1431, 1452, 1509-1516, 1569-1577, 1591-1598, 1608-1631, 1651-1666, 1687-1696, 1704, 1708, 1714, 1784-1821, 1872, 1882, 1888, 1899-1910, 1915-1916, 1937-1951, 1989-1990, 2039-2044, 2052-2097, 2110-2119, 2143, 2167, 2185, 2203, 2275, 2313-2319, 2357-2363, 2409-2415, 2461-2467, 2510-2516, 2562-2569, 2605-2611, 2618, 2628-2634, 2641, 2649-2658, 2666, 2672-2673, 2684, 2686, 2693-2699, 2712-2715, 2771, 2818, 2846-2858, 2874-2880, 2906-2913, 2961-2964, 3020-3023, 3030, 3034, 3050-3053, 3057-3058, 3070-3080, 3165-3202, 3224-3280, 3284-3290, 3321-3374, 3380-3404, 3408, 3422-3440, 3475-3502, 3507-3521, 3525, 3563-3606, 3610-3612, 3615-3620, 3654-3661, 3725-3802, 3811-3831, 3838-3847, 3859-3890, 3897-3911, 3917-3949, 3959-3983, 3994-4015, 4024-4026, 4036-4044, 4055-4057, 4071-4134, 4146-4152, 4171-4172, 4236-4280, 4283-4284, 4287-4290, 4313-4362, 4377, 4389, 4401, 4431-4526, 4537-4561, 4565-4643, 4649-4662, 4677-4811, 4818-4863, 4872-4885, 4892-4900, 4925, 4930-4935, 4971, 4984, 4992-4999, 5006-5010, 5014-5018, 5053-5058, 5066, 5081-5091, 5099-5101, 5107, 5147-5148, 5157, 5177-5203, 5209-5210, 5222-5228, 5242-5258, 5264-5268, 5283-5312, 5373-5399, 5412, 5479-5496, 5531-5541, 5606-5624, 5631, 5689, 5731, 5734-5737, 5744, 5803-5837, 5858-5861, 5867-5894, 5917-5941, 5947, 5953, 5959, 5988-5999, 6009, 6020-6057, 6065-6081, 6089-6099, 6105-6111, 6130-6138, 6159-6187, 6198-6208, 6291-6293, 6298, 6344-6352, 6365, 6373-6376, 6401-6408, 6411-6422, 6442-6484, 6517-6572, 6603-6612, 6629-6664, 6690-6701, 6712-6735, 6744-6785, 6789-6795, 6799-6804, 6807-6817, 6821-6822, 6825, 6828, 6831, 6835, 6876-6882, 6923-6929, 6936-6944, 6948-6956, 6960-6968, 6972-6993, 6997-7018, 7029, 7062-7069, 7105, 7109-7110, 7113, 7119, 7122-7124, 7128, 7135-7140, 7155-7159, 7163-7164, 7174, 7178, 7196-7199, 7215-7228, 7232-7243 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/category.py 123 74 40% 178, 182, 194, 213-223, 246-273, 285-296, 303, 311-319, 322-326, 332, 337-340, 356-368, 376-381, 384-396, 401, 470-471, 475-486 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/datetimelike.py 384 272 29% 73, 91, 95, 100, 104, 109, 114, 119, 125, 131-161, 165-170, 173-174, 189-200, 206, 212, 218-226, 230-234, 243, 247, 251-269, 273-277, 295-321, 340-354, 386, 392-401, 423, 437-438, 441-442, 447, 451-473, 478, 487-490, 493, 496-512, 516-519, 523-526, 532-548, 552-568, 572-586, 592-616, 622-651, 655-667, 676-679, 684-687, 691, 695-696, 705-721, 727-748, 752-754, 758-762, 776-787 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/datetimes.py 285 213 25% 64, 75-100, 254, 264-265, 269-270, 279-280, 284-287, 291-292, 296-297, 301, 320-354, 367-372, 375-376, 382-386, 393-396, 404-416, 428-445, 456-471, 491-510, 513-523, 530-534, 544-586, 591-598, 617-659, 667, 690-705, 731-755, 942-956, 1031-1049, 1063-1064 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/extension.py 83 37 55% 28-29, 62, 71-78, 81, 90, 96-105, 154, 160-171, 177, 188, 191-192 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/frozen.py 44 21 52% 45-47, 63-65, 74-76, 79-81, 84-86, 91, 96, 100, 106, 109, 112 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/interval.py 376 267 29% 113-123, 127-137, 145, 221-232, 263-267, 299-303, 334-336, 344-348, 363-373, 377, 380-386, 391, 398, 408, 415-432, 482, 502-506, 525-571, 574-597, 639-667, 676-693, 699-724, 733-736, 744-768, 772, 779-790, 798, 801, 804-807, 813, 817, 821, 825, 833, 839, 844, 854-867, 882-889, 907-918, 926, 932, 941, 955-957, 1059-1137 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/multi.py 1380 1162 16% 105, 143-153, 184-194, 206-211, 326-359, 377-383, 407-445, 489-510, 561-597, 645-658, 717-722, 729-750, 754, 766, 776-779, 782, 790, 800-804, 818-846, 931-940, 956, 972, 979, 990-1016, 1074-1078, 1089-1113, 1119, 1123-1125, 1128-1137, 1180-1204, 1208, 1212-1214, 1218-1223, 1227, 1232-1235, 1243, 1248, 1261-1270, 1279-1280, 1285-1316, 1328-1390, 1396, 1425-1454, 1479, 1482-1506, 1513-1537, 1545, 1550, 1554-1557, 1567, 1571-1580, 1599-1605, 1655-1657, 1661-1665, 1727-1755, 1787, 1823, 1835-1837, 1873-1898, 1941-1989, 1996-2002, 2007-2032, 2044-2050, 2067-2086, 2113-2137, 2140-2144, 2148-2152, 2184-2221, 2226-2242, 2287-2298, 2334-2344, 2357-2363, 2425-2480, 2483-2496, 2499-2506, 2512-2516, 2524, 2529-2539, 2542-2559, 2565-2569, 2619-2621, 2677, 2680-2727, 2748-2752, 2795-2872, 2915-2926, 2934-3075, 3085-3188, 3226-3319, 3343-3407, 3435-3447, 3463-3514, 3521-3527, 3533-3564, 3567, 3575-3579, 3587-3596, 3599-3600, 3603-3608, 3611-3627, 3633-3644, 3647-3658, 3674-3696, 3714-3731, 3743-3744, 3753-3768, 3804-3808, 3812-3836, 3840-3845, 3862-3878, 3897-3901, 3908-3918 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/period.py 191 122 36% 61-67, 157, 162, 175-176, 180-181, 186, 191, 196, 211-265, 272, 291-308, 314-322, 338-343, 351-356, 362, 372-378, 400-440, 443-455, 458-463, 467-470, 473-474, 478-482, 537-547 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/range.py 508 388 24% 103, 117-142, 155-161, 183-188, 213-214, 217-219, 228-231, 235, 239-245, 277-278, 306, 315, 319, 323, 326-331, 335, 342-351, 360-381, 388, 393, 397, 401-413, 416-418, 422-424, 427-433, 437-439, 443-445, 460-471, 478-483, 489-491, 500-526, 534-575, 579-580, 589-597, 602-608, 630-683, 687-770, 773-782, 791-808, 811-829, 840-890, 900, 906-924, 930-931, 935-947, 953, 956, 961-964, 975-1037 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/timedeltas.py 69 38 45% 110, 121, 136-176, 184, 197-204, 208-209, 213-215, 221, 308-315 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexing.py 917 749 18% 83, 137, 548, 613, 661, 676-683, 689-716, 728-775, 789-829, 833-849, 871, 879-893, 900-910, 919-921, 926-928, 932-940, 950-965, 970-1026, 1033-1084, 1087, 1093-1097, 1106, 1109, 1112, 1115, 1120-1123, 1144-1158, 1161, 1173-1192, 1215-1219, 1238-1242, 1269-1273, 1278-1289, 1293, 1297-1307, 1310-1343, 1350-1362, 1379-1434, 1459-1464, 1479-1518, 1529-1554, 1566-1569, 1589, 1594-1598, 1617-1621, 1625, 1627, 1633, 1636, 1639, 1642-1643, 1647, 1653, 1662-1669, 1675, 1679-1685, 1701-1837, 1844-1928, 1933-1950, 1953-1999, 2011-2036, 2042-2078, 2084-2181, 2188-2198, 2217-2299, 2302-2348, 2360, 2363-2371, 2374-2386, 2401-2404, 2409-2410, 2413-2419, 2422-2430, 2441-2444, 2462-2464, 2471-2473, 2502-2525, 2533-2541, 2549-2552, 2559-2562, 2572-2579, 2589, 2602, 2618, 2627 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/dataframe_protocol.py 101 1 99% 158 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/from_dataframe.py 171 151 12% 48-54, 73-91, 108-137, 156-164, 181-216, 233-307, 313-340, 357-376, 411-436, 469-499 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/utils.py 44 11 75% 75-90 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/api.py 36 26 28% 50-81, 88-97 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/array_manager.py 583 443 24% 130, 134-138, 142, 150, 155, 160-161, 165-167, 170, 176, 179, 182-183, 186-193, 216-255, 261-310, 313-319, 327, 330, 333-339, 347-348, 351, 354-361, 366-370, 375-378, 381-401, 404, 407-411, 423-425, 434, 438, 442, 447, 453, 457, 460-468, 479, 490, 509-529, 544-545, 578-632, 644-660, 665-679, 686-689, 698, 708-715, 718-736, 756-768, 771-781, 787-788, 794, 802, 822-861, 872-881, 894-916, 922-927, 944-969, 983-1001, 1008-1013, 1023-1034, 1052-1089, 1115-1144, 1158, 1166-1177, 1180-1185, 1192, 1196-1199, 1203, 1207, 1211, 1215, 1219, 1223, 1227-1230, 1234-1238, 1242, 1245, 1248-1253, 1256-1258, 1261-1265, 1276-1278, 1284-1289, 1293-1296, 1305, 1311-1314, 1331, 1335, 1351-1361 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/base.py 88 40 55% 44, 48, 52, 61-70, 85, 98-100, 114, 121-130, 138, 142, 148, 151, 154, 160, 181-193, 196-201, 205, 222 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/blocks.py 1041 770 26% 120-121, 135-139, 167, 175-178, 186, 190, 201-203, 211, 221-228, 241, 249-256, 260, 273-277, 288-293, 298-299, 315-318, 329-331, 337-348, 353-369, 376-385, 402-408, 422-424, 430-447, 457-459, 472-474, 509-526, 531-532, 537-544, 567-647, 678-701, 715-806, 839-862, 878, 884, 890, 906, 922-924, 939-940, 954, 961, 991-1007, 1034-1068, 1087-1141, 1160-1260, 1275-1311, 1333-1393, 1398-1399, 1412-1433, 1459-1467, 1484-1500, 1512-1551, 1556, 1563, 1570, 1573, 1608-1639, 1645-1709, 1715-1778, 1782-1789, 1793, 1799-1803, 1806, 1819-1825, 1854-1870, 1875-1877, 1888-1905, 1910-1912, 1919-1934, 1944-1976, 1981, 1985, 2006-2025, 2034-2035, 2040-2041, 2052-2053, 2070-2094, 2103, 2107, 2115, 2134, 2140, 2163-2166, 2171-2173, 2181-2186, 2197, 2211-2238, 2271-2298, 2325, 2329, 2354, 2356, 2358, 2361, 2364, 2379-2382, 2421, 2429, 2434-2440, 2450-2459, 2467-2478, 2486-2494, 2507-2581, 2593-2607 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/concat.py 350 303 13% 68-69, 90-117, 139-173, 194-252, 262-290, 303-319, 336-395, 402-406, 409, 413-418, 422-428, 435-458, 462-487, 492-569, 576-615, 622-638, 651-668, 678-681, 707, 720-738, 749-791 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py 428 290 32% 128-140, 147, 156-159, 174-194, 209-230, 244-388, 400-408, 429-464, 479, 496, 503, 529-560, 567-571, 584-591, 596-605, 622, 633-634, 636-637, 641-642, 645, 648, 650, 655, 658, 663-668, 682-703, 707-721, 730-739, 766-768, 794-804, 807-821, 825-827, 831-838, 848, 861-883, 909-920, 935-937, 972, 987-994, 1042, 1044-1063 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py 951 659 31% 155, 161, 165, 190, 196-209, 212, 219-221, 225-226, 235, 243-244, 252, 259-266, 273-274, 277-278, 295-304, 327-356, 359-365, 374, 386-394, 397-403, 413-414, 417, 422-426, 429-433, 443-451, 460-468, 471-475, 484, 494-505, 512, 516, 521, 526-536, 539-540, 553-565, 574-580, 586-614, 618, 634-665, 675-681, 715, 721-727, 734, 737, 740, 795-901, 908-927, 992-1002, 1005-1011, 1052-1082, 1106-1108, 1121-1136, 1148-1279, 1299-1326, 1340-1353, 1364-1385, 1398-1427, 1441-1444, 1453-1466, 1472-1479, 1496-1515, 1530-1539, 1545, 1552, 1580-1592, 1610-1655, 1670-1675, 1703-1704, 1717, 1724, 1727, 1729-1730, 1737, 1761-1762, 1764, 1766, 1781-1800, 1829-1832, 1840, 1869-1871, 1880-1881, 1887-1892, 1900, 1903-1919, 1922-1939, 1942, 1951, 1956, 1960-1979, 1984-1994, 1998, 2002, 2005, 2009, 2017, 2020-2022, 2026, 2038-2042, 2050-2054, 2061, 2073-2074, 2082-2086, 2106-2116, 2139-2140, 2153-2169, 2184, 2195-2196, 2213, 2217, 2221-2235, 2241, 2267-2276, 2282-2311, 2316-2320, 2326-2343 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/ops.py 62 46 26% 12-15, 33-52, 61-86, 93-95, 107-136, 143-147 /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/describe.py 148 115 22% 46, 80-95, 108, 127-130, 153-159, 162-176, 180-196, 201-207, 220-247, 263-279, 295-327, 341-351, 364-373, 387-408 /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/selectn.py 120 97 19% 34, 42-47, 50, 54, 58, 67-69, 88-156, 176-182, 185-262 /home/admin/.local/lib/python3.8/site-packages/pandas/core/missing.py 314 268 15% 46, 53-61, 81-118, 123-140, 168-183, 204-224, 247-267, 302-359, 366-381, 408-489, 507-566, 605-611, 650-654, 734-740, 765-787, 818-858, 866-870, 880-888, 899-901, 910-912, 917-924, 929-936, 943-946, 950, 984-1013, 1028-1030 /home/admin/.local/lib/python3.8/site-packages/pandas/core/nanops.py 626 505 19% 71, 83, 88-104, 130-160, 167-182, 186-195, 202-217, 253-261, 310-350, 354-356, 361-397, 415-429, 451-462, 474-494, 533-551, 588-606, 643-655, 664-674, 711-740, 768-821, 844-848, 880-895, 934-941, 981-1017, 1058-1070, 1083-1097, 1145-1149, 1191-1195, 1237-1285, 1327-1384, 1419-1427, 1439-1456, 1483-1497, 1513-1545, 1568-1576, 1581-1585, 1599-1614, 1620-1643, 1657-1671, 1675-1700, 1708-1720, 1747-1767 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/__init__.py 184 134 27% 78, 133-150, 163-173, 183-201, 233-334, 343-365, 384-409, 417-433, 446-473, 489-494 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/array_ops.py 191 155 19% 68-83, 99-139, 164-188, 217-234, 256-298, 302-339, 358-405, 424, 430, 433, 435, 448, 470-519, 537-542 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/common.py 56 34 39% 64, 69-81, 101-105, 128-151 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/dispatch.py 6 1 83% 26 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/docstrings.py 57 2 96% 50, 60 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/invalid.py 17 9 47% 30-37, 54-55 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/mask_ops.py 59 52 12% 42-73, 106-126, 156-184, 188-189 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/methods.py 34 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/missing.py 56 48 14% 49-73, 105-134, 158-180 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/api.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/concat.py 284 246 13% 54-58, 78, 95, 112, 129, 146, 364-385, 406-563, 570-623, 626-629, 632-633, 639-640, 653-699, 702-705, 709, 713-821 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/encoding.py 154 138 10% 147-224, 236-334, 447-533 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/melt.py 138 117 15% 33-35, 50-158, 214-248, 489-540 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/merge.py 919 816 11% 104-106, 128, 148-162, 176-217, 324-358, 616-633, 680-747, 760-803, 806-825, 830-831, 835-840, 847-869, 872-884, 906-927, 935-1034, 1038, 1046-1101, 1125-1135, 1155-1276, 1285-1440, 1461-1463, 1471-1558, 1562-1605, 1642-1690, 1737-1779, 1799-1800, 1816-1842, 1846-1847, 1859-1864, 1891-1898, 1914-1990, 1996-2063, 2068-2189, 2204-2234, 2240-2242, 2247, 2275-2279, 2285-2309, 2369-2468, 2475-2505, 2511-2517, 2527-2554, 2558-2560, 2564, 2568-2575, 2591-2645 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/pivot.py 366 333 9% 51, 71-110, 129-255, 269-339, 345-362, 368-436, 448-480, 484-494, 506-562, 671-734, 740-814, 818-831, 864-885 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/tile.py 181 158 13% 241-305, 369-389, 403-472, 481-505, 522-539, 557-561, 568-591, 602-608, 617-625, 632-640, 647-651 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/util.py 28 22 21% 33-60, 77-82 /home/admin/.local/lib/python3.8/site-packages/pandas/core/roperator.py 29 15 48% 11, 15, 19, 23, 27, 31, 38-42, 46, 50, 54, 58, 62 /home/admin/.local/lib/python3.8/site-packages/pandas/core/sample.py 58 48 17% 19, 31-76, 90-113, 144-151 /home/admin/.local/lib/python3.8/site-packages/pandas/core/series.py 1122 756 33% 179-187, 221-230, 384, 394-519, 546-570, 576, 584-586, 591, 605, 619, 716, 754-756, 762, 778-781, 787, 857-863, 916-921, 938, 945-960, 971, 985, 990, 997, 1001, 1004, 1009-1033, 1037-1073, 1077-1093, 1096-1097, 1113, 1121-1138, 1141-1219, 1222-1225, 1230-1248, 1251-1256, 1259-1263, 1280-1290, 1298, 1302-1305, 1311-1312, 1320, 1325, 1331-1336, 1346-1367, 1376, 1429-1432, 1446, 1458, 1470, 1581-1621, 1631-1632, 1648, 1664, 1713-1742, 1815, 1848, 1862, 1894-1901, 1929-1941, 1957-1960, 2056-2064, 2099, 2120-2127, 2194, 2204, 2210, 2216, 2303-2313, 2391-2393, 2460-2463, 2531-2534, 2568-2574, 2580, 2588, 2596, 2644-2660, 2722-2731, 2778-2781, 2870-2871, 2918, 2970-2994, 3002, 3008, 3018, 3026-3036, 3059-3073, 3091-3111, 3185, 3265-3291, 3337-3343, 3413-3420, 3437, 3451, 3608-3651, 3667, 3683, 3699, 3836, 3880-3893, 3993, 4090, 4173-4176, 4196-4199, 4256-4267, 4311-4313, 4397-4398, 4414, 4456-4464, 4479, 4491, 4503, 4514-4518, 4630, 4649-4670, 4680-4692, 4699, 4720, 4744, 4757, 4770, 4847-4864, 4898, 4918, 4938, 4958, 4972, 4986, 5085, 5106, 5119, 5132, 5145, 5179, 5192, 5205, 5223, 5241, 5254-5268, 5275, 5328-5331, 5406-5407, 5484-5502, 5516-5535, 5542, 5550, 5555, 5563, 5574, 5585, 5661-5680, 5695, 5719, 5785-5791, 5825-5831, 5842, 5853, 5864, 5874, 5885, 5896, 5907, 5917, 5928, 5942, 5963, 5975, 5987, 5998, 6016, 6028, 6040, 6051, 6087-6098, 6101-6108, 6111-6112 /home/admin/.local/lib/python3.8/site-packages/pandas/core/shared_docs.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/sorting.py 252 201 20% 47-49, 81-106, 144-200, 223-224, 228-232, 239-254, 276-287, 293-301, 329-366, 399-400, 410, 417, 436-439, 458-473, 480-484, 516-533, 549-577, 587-594, 606-623, 656-670, 681-692, 710-725 /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/accessor.py 581 386 34% 49, 123-129, 140-141, 179-195, 220-236, 239-240, 251-393, 411-448, 593-683, 888-895, 912-913, 1008-1009, 1022-1023, 1085-1086, 1151-1152, 1281-1290, 1321-1322, 1354-1355, 1485-1508, 1551-1552, 1613-1625, 1650, 1655, 1660, 1723-1728, 1802-1803, 1878-1879, 1898-1907, 1925-1926, 2014-2015, 2023-2024, 2032-2033, 2088-2089, 2096-2097, 2155-2156, 2197-2198, 2224-2225, 2292-2293, 2359-2363, 2429-2433, 2525-2526, 2612-2654, 2732, 2771-2776, 2788-2793, 2812-2813, 2855-2860, 2873-2878, 2926-2927, 3026-3027, 3032-3033, 3038-3039, 3044-3045, 3050-3051, 3056-3057, 3261-3274, 3294-3301, 3308-3313, 3317-3320, 3337-3338, 3342-3376 /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/base.py 160 53 67% 16, 36-39, 43, 52, 58, 62, 66, 78, 82, 88, 98, 102, 106, 110, 114, 118, 122, 126, 130, 134, 138, 142, 146, 150, 154, 158, 162, 166, 170, 174, 178, 182, 186, 190, 194, 198, 202, 206, 210, 214, 218, 222, 226, 230, 234, 238, 242, 246, 252, 256, 260 /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/object_array.py 304 234 23% 30, 42, 64-104, 107-109, 117-125, 130-143, 146-147, 150-151, 162-177, 180-205, 210-216, 225-231, 234-235, 238, 241, 244-255, 258-259, 262-269, 272-276, 279-283, 286, 289-290, 293, 296, 299-300, 303-319, 328-353, 356-359, 362, 365-367, 370-393, 396, 399, 402, 405, 408, 411, 414, 417, 420, 423, 426, 429, 432, 435, 438, 441-442, 445, 448, 451, 458-463, 466-473, 476-498 /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/datetimes.py 364 295 19% 87-90, 134-152, 184-212, 239-259, 285-288, 311-314, 338-349, 390-470, 484-488, 495-560, 581-633, 650, 667, 684, 1009-1091, 1140-1211, 1226-1265 /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/numeric.py 108 95 12% 160-310 /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/timedeltas.py 70 50 29% 33-39, 52, 61, 70, 174-211, 220-231, 238-265 /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/times.py 77 67 13% 53-126, 144-154 /home/admin/.local/lib/python3.8/site-packages/pandas/core/util/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/util/hashing.py 116 99 15% 37, 65-83, 113-175, 196-221, 242-261, 289-310, 322-366 /home/admin/.local/lib/python3.8/site-packages/pandas/core/util/numba_.py 37 26 30% 20, 25-27, 52-63, 88-112 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/common.py 81 72 11% 18-146, 150-161, 166-168 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/doc.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/ewm.py 227 170 25% 18-19, 70-94, 118-121, 347-394, 409, 415, 445, 492, 517-546, 569-600, 621-630, 651-663, 700-735, 771-812, 825-830, 843-847, 868-892, 898, 901, 904, 912, 921, 924, 970-1012 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/expanding.py 74 23 69% 17-18, 125, 137, 171, 186, 209, 239, 266, 293, 320, 347, 406, 466, 508, 526, 566, 602, 676, 719, 789, 812-816 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/numba_.py 146 129 12% 49-75, 111-173, 208-236, 244-257, 293-349 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/online.py 52 43 17% 32-86, 91-99, 102-114, 117-118 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/rolling.py 711 562 21% 102-107, 132-160, 163-203, 208-214, 224, 241-246, 261-262, 269-279, 294-310, 313-318, 323, 329-335, 338-353, 357-377, 382-402, 407-410, 414-420, 426-434, 442-454, 466-507, 518-533, 546-556, 584-617, 625-658, 661-664, 686-699, 709-760, 774-844, 853-858, 864-867, 1134-1153, 1159-1162, 1193-1218, 1252-1257, 1275-1279, 1300-1304, 1325-1327, 1343, 1350-1351, 1362-1390, 1403-1419, 1427-1441, 1449-1463, 1471-1485, 1493-1507, 1515-1528, 1537-1548, 1561-1568, 1575-1576, 1584-1585, 1590-1591, 1603-1614, 1623-1630, 1639-1675, 1686-1732, 1751-1790, 1797-1800, 1803-1809, 1849, 1890, 1913, 1991, 2020, 2062, 2111, 2153, 2211, 2270, 2293, 2330-2331, 2373, 2428, 2502, 2545, 2675, 2702-2726, 2733-2741 /home/admin/.local/lib/python3.8/site-packages/pandas/errors/__init__.py 65 16 75% 191-197, 200-204, 397-402, 444-445 /home/admin/.local/lib/python3.8/site-packages/pandas/io/__init__.py 4 2 50% 5-12 /home/admin/.local/lib/python3.8/site-packages/pandas/io/_util.py 6 2 67% 9-10 /home/admin/.local/lib/python3.8/site-packages/pandas/io/api.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/clipboards.py 11 1 91% 20 /home/admin/.local/lib/python3.8/site-packages/pandas/io/common.py 444 337 24% 124-132, 135, 138, 154-156, 161, 166, 183-185, 189-214, 219, 226, 252-260, 268-270, 278, 319-458, 480-482, 522-531, 560-586, 598-600, 615, 630, 645, 702-913, 936, 939-951, 963-969, 977-984, 991-999, 1003-1006, 1017-1025, 1032-1037, 1041-1042, 1053, 1056, 1059-1061, 1064-1066, 1069-1071, 1078-1084, 1087, 1090-1100, 1107-1134, 1139-1148, 1154-1167, 1175-1187, 1209-1212, 1233-1253 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_base.py 413 301 27% 395, 434, 473-515, 523-545, 552, 556, 559-569, 574, 578, 582, 586, 589-591, 596-597, 620-626, 655-690, 716-897, 1103-1121, 1129, 1134, 1192-1233, 1240, 1247, 1254, 1257, 1260-1264, 1280-1298, 1306-1310, 1314, 1322, 1326-1327, 1368-1401, 1470-1513, 1516, 1551, 1575, 1579, 1583, 1586, 1594 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py 141 113 20% 25, 44-45, 49-51, 54-56, 61, 66-69, 72-76, 79-89, 97-158, 166-168, 171-173, 179-183, 186-222, 229-249 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_odswriter.py 138 115 17% 27, 46-62, 71, 76-82, 88-90, 103-149, 164-171, 186-219, 241-278, 293-335 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_openpyxl.py 242 189 22% 35-36, 56-88, 97, 102-103, 109-112, 138-148, 172-177, 207-225, 243, 270-306, 325-339, 366-379, 400-402, 420, 437-439, 450-529, 548-549, 553-555, 558-560, 566, 569-570, 573-574, 577-592, 597-626 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_pyxlsb.py 56 38 32% 34-37, 41-43, 46-52, 56, 59-60, 63-66, 71-80, 87-112 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_util.py 102 71 30% 24-27, 43, 65-86, 90-93, 115-125, 149-158, 163, 168, 173, 178, 197-209, 214, 219, 223-236, 260-271, 295-305, 327-332 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlrd.py 62 43 31% 33-35, 39-41, 44-50, 54, 57-58, 61-62, 67-126 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlsxwriter.py 83 63 24% 101-172, 192-210, 219, 223-224, 230, 241-275 /home/admin/.local/lib/python3.8/site-packages/pandas/io/feather_format.py 43 28 35% 54-96, 139-162 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/__init__.py 4 2 50% 5-8 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/console.py 33 28 15% 15-47, 63-76, 87-94 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/format.py 908 752 17% 112, 209-214, 217-231, 234, 243-261, 279-295, 298-322, 325-363, 366-374, 377, 386-421, 426, 429, 432, 435, 442-451, 457-460, 468-476, 480-484, 504-510, 537-549, 585-609, 615-621, 625, 631, 635, 639, 643, 647, 651, 655, 659, 663, 666-668, 673-678, 684-687, 690-697, 702-721, 725-732, 738-751, 761-765, 769, 772, 775, 779-789, 795-799, 809-825, 834-843, 846-890, 893-895, 906-915, 918-956, 961-996, 999-1005, 1026, 1044-1058, 1094-1109, 1131-1135, 1159-1194, 1205-1210, 1221-1244, 1292-1330, 1349-1360, 1363-1364, 1367-1442, 1447-1456, 1465-1512, 1520-1611, 1614, 1619-1625, 1636-1638, 1642-1653, 1658-1684, 1721-1754, 1759-1779, 1783-1788, 1796-1803, 1812-1817, 1824-1833, 1839-1846, 1857-1859, 1862-1865, 1879-1912, 1921-1946, 1954-1971, 1979-1983, 1994-2019, 2023-2026, 2060-2061, 2083-2123, 2181, 2202-2224, 2238-2240 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/info.py 356 179 50% 28, 315, 342-346, 353-355, 412, 416-427, 438, 451-452, 456, 468, 480, 485, 490, 494-495, 505-511, 524-525, 535-545, 549, 553, 557-559, 570-571, 581-585, 615-619, 624, 629, 634, 639, 642-644, 647-650, 656-667, 692-695, 701-707, 710-713, 730, 735, 740, 745, 750, 754, 758, 762, 766-769, 783, 786-791, 795-797, 806, 811, 816, 820, 830-835, 838, 859, 863-864, 871-872, 880-883, 894-900, 903-911, 914-921, 925-926, 930-931, 945-948, 952-960, 965-967, 970, 974, 982, 991-992, 996-997, 1011, 1014-1016, 1021, 1025, 1039-1043, 1057-1060, 1064-1072, 1075, 1080-1082, 1086, 1090, 1101 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/printing.py 203 183 10% 43-59, 66-71, 107-130, 140-161, 195-234, 240-241, 245-275, 279, 320-456, 481-497, 504 /home/admin/.local/lib/python3.8/site-packages/pandas/io/gbq.py 21 14 33% 12, 18-23, 173-186, 214-215 /home/admin/.local/lib/python3.8/site-packages/pandas/io/html.py 367 291 21% 54, 67-80, 105, 127-134, 150-161, 242-247, 257-258, 278, 294, 310, 326, 341, 360, 375, 401, 420, 431, 458-476, 501-558, 576-579, 603-606, 609-630, 633-634, 637, 640, 643, 646, 649-652, 655, 658-661, 664-679, 698-702, 728-729, 732, 737, 740-765, 768, 786-824, 827-842, 845-848, 851, 855-862, 866-886, 918-935, 939-940, 944-968, 972-1021, 1192-1212 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_json.py 501 386 23% 84, 107, 127, 146-204, 222-237, 240, 243-244, 266-269, 272-273, 281-286, 292-301, 327-379, 383, 408, 433, 458, 483, 743-784, 817-862, 872-878, 894-919, 925, 931, 935, 939, 946-981, 987-1008, 1017-1018, 1021, 1025, 1029, 1033, 1036-1061, 1064, 1072, 1099-1122, 1128-1131, 1134-1141, 1144, 1150-1160, 1163, 1176-1241, 1251-1280, 1283, 1291-1298, 1301-1307, 1315-1346, 1354-1374, 1377-1382, 1387-1415 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_normalize.py 142 126 11% 35-39, 86-120, 146-166, 184-191, 237-244, 388-536 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_table_schema.py 134 114 15% 42-43, 79-96, 101-120, 124-151, 195-224, 283-316, 355-382 /home/admin/.local/lib/python3.8/site-packages/pandas/io/orc.py 52 38 27% 80-97, 162-205 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parquet.py 168 131 22% 46-75, 88-115, 121-122, 125, 128, 133-141, 153-194, 205-237, 244-247, 259-287, 299-342, 405-425, 493-509 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/arrow_parser_wrapper.py 67 53 21% 21-25, 31-42, 48-79, 101-136, 149-164 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/base_parser.py 578 491 15% 96, 109-176, 202-234, 240, 245, 253-267, 294-340, 349-352, 359-383, 387-406, 410-434, 438-451, 455-503, 515-593, 617-662, 683-766, 784-837, 845, 853, 862-874, 891-898, 911, 917, 931-933, 957-964, 991-1013, 1016-1050, 1055-1096, 1105-1165, 1221-1302, 1308-1327, 1353-1362, 1371-1384, 1388 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/c_parser_wrapper.py 177 149 16% 50, 61-192, 196-199, 208-220, 232-337, 341-346, 349-357, 360-365, 375-402, 412-423 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/python_parser.py 630 576 9% 50, 67-176, 179-243, 250-300, 307-323, 333-336, 343-364, 376-573, 587-621, 627-630, 642-683, 698, 701-754, 773-777, 792-825, 828-846, 863-872, 875-878, 885-899, 902-905, 910, 928-989, 992-1075, 1078-1156, 1159-1163, 1180-1204, 1234-1245, 1251-1267, 1272-1281, 1292-1294, 1297, 1313, 1321, 1345-1351 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/readers.py 486 399 18% 473, 478, 483, 502-513, 531-537, 546-583, 640, 697, 754, 811, 884-912, 969, 1026, 1083, 1140, 1213-1242, 1312-1352, 1368-1407, 1410-1412, 1415-1456, 1460-1465, 1472-1620, 1623-1627, 1634-1683, 1686, 1689-1724, 1727-1733, 1736, 1744, 1802-1803, 1808-1840, 1845-1853, 1858-1878, 1936-2008, 2019-2028, 2050-2052, 2074-2106, 2123-2127 /home/admin/.local/lib/python3.8/site-packages/pandas/io/pickle.py 28 15 46% 92-103, 178-204 /home/admin/.local/lib/python3.8/site-packages/pandas/io/pytables.py 2255 1832 19% 118-124, 136-138, 143-146, 156-158, 173-182, 235-248, 272-307, 389-462, 467-476, 557-579, 582, 587-589, 593, 596, 599, 602, 606-610, 619-624, 627, 630-631, 634, 642, 666-674, 679, 685-686, 699-731, 737-739, 746-748, 766-770, 785-791, 839-866, 894-898, 935-938, 983-1059, 1113-1116, 1154-1188, 1249-1259, 1309-1373, 1406-1413, 1426-1430, 1470-1489, 1493-1505, 1509-1515, 1545-1574, 1584-1613, 1619-1620, 1625-1630, 1643-1721, 1746-1783, 1786-1788, 1792-1806, 1811-1827, 1867-1894, 1898-1910, 1913-1914, 1918-1939, 1977-2001, 2006, 2010, 2014-2016, 2019-2022, 2031, 2037, 2042-2045, 2053-2092, 2096, 2100, 2104, 2109, 2114, 2117, 2125-2130, 2133, 2136-2141, 2146-2160, 2164-2167, 2176-2201, 2205-2207, 2211, 2215-2223, 2230-2231, 2239, 2254-2257, 2260, 2296-2310, 2314, 2318, 2321-2326, 2335, 2341-2348, 2352, 2359-2384, 2388, 2393-2403, 2407, 2411, 2415, 2419, 2424, 2428-2435, 2455-2537, 2541-2544, 2553-2555, 2559, 2563, 2567, 2571, 2605-2611, 2615, 2620-2627, 2631, 2635-2642, 2646-2647, 2650-2651, 2655, 2659, 2663, 2667, 2671, 2675, 2679, 2690, 2694, 2698, 2702-2704, 2714-2718, 2727, 2732, 2743-2747, 2759, 2765, 2768-2811, 2817-2823, 2830, 2834-2835, 2839-2842, 2846, 2850-2880, 2885-2892, 2897-2917, 2920-2942, 2947-2963, 2970-3003, 3008-3012, 3021-3102, 3113-3116, 3125-3128, 3132-3135, 3145-3168, 3178-3203, 3207-3229, 3282-3288, 3292, 3296-3306, 3314-3317, 3321-3347, 3355, 3364-3372, 3377, 3382, 3386, 3391, 3395, 3399, 3403, 3408, 3412, 3417, 3427-3436, 3441, 3445, 3449-3450, 3461, 3472-3474, 3478-3487, 3491-3499, 3503-3506, 3517-3528, 3536-3607, 3639-3689, 3712-3727, 3732, 3739-3770, 3804-4002, 4015-4070, 4075-4125, 4136-4156, 4166-4182, 4196-4225, 4247, 4255, 4280-4321, 4327-4375, 4398-4424, 4428-4482, 4495, 4500-4502, 4512-4581, 4594, 4598, 4602-4605, 4614-4629, 4640-4646, 4660, 4664, 4668-4674, 4679-4709, 4712, 4725, 4728-4737, 4746-4754, 4760-4777, 4785-4786, 4793, 4798, 4814-4836, 4840-4900, 4906-4925, 4938-4998, 5017-5029, 5051-5067, 5071-5075, 5079-5082, 5090-5092, 5109-5117, 5124-5150, 5157-5172, 5194-5230, 5234-5253, 5259-5265, 5271-5289 /home/admin/.local/lib/python3.8/site-packages/pandas/io/sas/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/sas/sasreader.py 51 29 43% 29, 40, 44, 47, 55, 69, 83, 134-180 /home/admin/.local/lib/python3.8/site-packages/pandas/io/spss.py 22 12 45% 18-20, 54-67 /home/admin/.local/lib/python3.8/site-packages/pandas/io/sql.py 746 610 18% 71-72, 85-90, 96-118, 126-139, 148-165, 178-188, 208-219, 238, 253, 332-353, 368, 383, 464-469, 494, 510, 629-663, 758-769, 804-805, 821-841, 868-886, 889, 892-894, 898-900, 903-914, 928-930, 941-946, 949-986, 992-1030, 1043-1066, 1077-1113, 1117-1145, 1148-1159, 1162-1189, 1207-1244, 1247-1314, 1317-1343, 1352, 1355, 1368, 1382, 1399, 1403, 1407, 1418, 1437, 1442, 1458-1470, 1475-1502, 1526-1544, 1547-1548, 1552-1556, 1560-1563, 1627-1631, 1653-1671, 1738-1765, 1782-1815, 1826-1840, 1908-1933, 1937, 1940-1943, 1946-1956, 1959-1964, 1974-1983, 2001-2005, 2016-2023, 2035-2044, 2047, 2050-2052, 2055-2071, 2074-2076, 2079-2082, 2090-2133, 2136-2167, 2182, 2186-2194, 2197-2214, 2228-2245, 2266-2293, 2296-2299, 2353-2379, 2382-2385, 2388, 2391-2392, 2402-2411, 2444-2445 /home/admin/.local/lib/python3.8/site-packages/pandas/io/stata.py 1590 1368 14% 84, 280-397, 412-505, 577-664, 682-692, 697-736, 752-788, 811-819, 897-900, 912, 924, 927, 930, 933, 941-953, 973-1051, 1134-1169, 1175-1176, 1182-1207, 1211-1212, 1220-1221, 1231-1239, 1245-1248, 1251, 1254, 1257, 1260, 1263, 1266, 1269, 1272, 1275, 1278, 1284-1293, 1297-1348, 1354-1377, 1381-1382, 1386-1395, 1399-1405, 1408-1420, 1423-1426, 1429-1438, 1441-1450, 1453-1462, 1465-1537, 1541-1553, 1556-1558, 1562-1579, 1582-1636, 1639-1667, 1670-1671, 1686-1688, 1702-1847, 1851-1894, 1897-1904, 1907-1936, 1948-2013, 2020-2021, 2028-2029, 2039-2040, 2050-2053, 2072-2090, 2094-2099, 2106-2108, 2115-2133, 2137-2147, 2167-2183, 2205-2228, 2336-2357, 2363, 2369, 2378-2402, 2409-2444, 2452-2461, 2486-2494, 2509-2567, 2570-2574, 2580-2640, 2650-2677, 2683-2730, 2740-2743, 2759, 2762-2763, 2770-2818, 2821-2822, 2827-2830, 2834-2835, 2839-2840, 2844-2852, 2856-2876, 2880, 2883-2912, 2915, 2919-2920, 2923, 2944-2965, 2972-2974, 3013-3037, 3040-3041, 3072-3092, 3123-3155, 3266-3284, 3289-3291, 3295-3296, 3304-3354, 3362-3384, 3387-3391, 3394-3402, 3405-3407, 3410-3415, 3418-3430, 3434-3462, 3465-3466, 3469-3472, 3475-3476, 3482-3488, 3491-3493, 3501-3504, 3511-3522, 3525-3536, 3659-3684, 3707-3721 /home/admin/.local/lib/python3.8/site-packages/pandas/io/xml.py 241 201 17% 50-54, 162-176, 186, 210-288, 313-390, 407, 421, 432, 442-461, 471-502, 507-520, 527-543, 561-580, 583-607, 612-626, 633-661, 671-676, 696-721, 733-739, 751-758, 801-846, 1116-1118 /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/_core.py 191 134 30% 30-32, 98-99, 222-223, 474-475, 506-507, 596-597, 791, 802-892, 895-975, 1044, 1136, 1222, 1289, 1351, 1459, 1534, 1583-1589, 1674, 1760-1765, 1786-1831, 1857-1864 /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/_misc.py 73 43 41% 12-16, 40-41, 64-65, 84-85, 159-160, 252-253, 321-322, 387-388, 455-456, 514-515, 549-550, 571-574, 577-578, 581-584, 587-588, 599, 602, 610-615 /home/admin/.local/lib/python3.8/site-packages/pandas/testing.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/__init__.py 4 2 50% 5-11 /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/api.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/frequencies.py 307 236 23% 98, 132-175, 184-210, 216, 222, 226, 230, 241-280, 284-285, 289-290, 294, 298, 301, 305-306, 310, 313-342, 345-353, 356-367, 370-381, 384-391, 395-405, 413-427, 432-433, 437, 441-446, 470-506, 525-564, 580-583, 587-589, 593-594, 598-599, 603-604, 608-609 /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/offsets.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/util/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/util/_decorators.py 135 79 41% 56-94, 164-214, 243-252, 257-260, 291-337, 368, 436, 448-449, 491 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_exceptions.py 48 36 25% 16-27, 36-51, 75-89 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_print_versions.py 48 34 29% 24-27, 34-36, 56-90, 108-134 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_str_methods.py 12 1 92% 23 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_tester.py 18 11 39% 25-35 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_validators.py 122 87 29% 33-41, 55-79, 117-123, 132-136, 161-163, 206-221, 259, 285-302, 326-336, 341, 346, 357, 377-390, 409-424, 435-442, 446-448 /home/admin/.local/lib/python3.8/site-packages/pandas/util/version/__init__.py 270 129 52% 28, 31, 34, 37, 40, 43, 46, 49, 52, 60, 63, 66, 69, 72, 75, 78, 81, 84, 123-126, 139, 146, 151-154, 157-160, 164, 169-172, 175-178, 183-186, 193, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 255-268, 276-294, 338, 363, 366-391, 395-396, 400-401, 405-406, 410, 418-421, 425, 429-438, 442, 446, 450, 454, 458, 462, 471-489, 493-495, 508, 537, 543, 550, 557, 570 /home/admin/.local/lib/python3.8/site-packages/psutil/__init__.py 950 691 27% 37-38, 127-128, 131-132, 135-136, 139-140, 143-180, 248-259, 271-281, 290-292, 298-304, 346, 349-393, 396-411, 421-423, 426, 429-431, 436, 467-505, 518-549, 556-568, 574-579, 586-601, 617-618, 628-647, 654-686, 690, 694-697, 703-715, 722-724, 728, 732-737, 746, 752, 758, 764, 776, 793-798, 813-816, 829-837, 850, 858, 862-866, 872, 876, 886, 915-956, 993-1048, 1059, 1070, 1074, 1090, 1102-1119, 1133-1147, 1154, 1178, 1184-1202, 1211-1212, 1222-1223, 1233-1234, 1244-1245, 1254-1255, 1273-1275, 1322-1323, 1326, 1329-1331, 1334-1347, 1350-1356, 1360-1364, 1383-1385, 1393-1403, 1431-1482, 1521-1571, 1593-1599, 1632-1634, 1638-1640, 1647-1659, 1666-1675, 1679-1696, 1736-1783, 1808-1859, 1864, 1877-1907, 1918, 1981-1984, 2000, 2013, 2025, 2060-2072, 2111-2121, 2155, 2176-2203, 2218, 2237-2259, 2272, 2290, 2304, 2317, 2327-2337, 2406, 2409 /home/admin/.local/lib/python3.8/site-packages/psutil/_common.py 442 251 43% 29-30, 33-34, 39, 131-133, 144-145, 157, 161-162, 278-279, 282-283, 295-304, 307, 320-332, 340-350, 360-367, 377-384, 412, 447-457, 462, 466-469, 481-488, 496-503, 509-517, 524-545, 552-553, 565-566, 576-590, 605-606, 623-628, 634-639, 645-678, 682-690, 694-695, 703-704, 721-727, 739-747, 757-760, 770-780, 785-798, 803-832, 836-842, 846 /home/admin/.local/lib/python3.8/site-packages/psutil/_compat.py 243 215 12% 27, 30-41, 57-119, 132-272, 278-324, 330-345 /home/admin/.local/lib/python3.8/site-packages/psutil/_pslinux.py 1130 874 23% 56, 113, 121-124, 217-232, 239-245, 258-264, 298-305, 310-313, 344-371, 390-492, 498-546, 592-616, 622-652, 657-672, 683-727, 778-797, 800-813, 832-868, 873-908, 913-946, 949-977, 985, 992-1022, 1027-1043, 1058-1146, 1151-1182, 1203-1290, 1303-1322, 1332-1405, 1415-1428, 1434-1441, 1452, 1459-1484, 1491-1506, 1515-1526, 1536-1539, 1545, 1558-1580, 1589-1590, 1595-1597, 1600-1602, 1605-1607, 1611-1615, 1618-1632, 1636-1657, 1661-1663, 1667-1672, 1678-1704, 1709-1715, 1720, 1724, 1728-1735, 1750-1753, 1766-1788, 1801-1858, 1862-1869, 1874-1883, 1890-1891, 1895-1919, 1928, 1932, 1939, 1944-1949, 1953-1968, 1975-1978, 1982-1988, 1997-2017, 2021-2025, 2029-2069, 2073-2075, 2079, 2083, 2087-2089, 2093-2095 /home/admin/.local/lib/python3.8/site-packages/psutil/_psposix.py 89 67 25% 30-47, 61-110, 120-158, 167-175 /home/admin/.local/lib/python3.8/site-packages/pyarrow/__init__.py 170 121 29% 41-59, 80-93, 97-102, 106-116, 123-157, 312-315, 343, 347, 351-356, 360-367, 375, 393-416, 426-466 /home/admin/.local/lib/python3.8/site-packages/pyarrow/_compute_docstrings.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/_generated_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/compute.py 203 92 55% 114, 137-138, 175-179, 208-211, 215-232, 238-245, 248-262, 391-403, 425-441, 484-485, 539-544, 584-591, 631-638, 663-664, 700-713, 730 /home/admin/.local/lib/python3.8/site-packages/pyarrow/filesystem.py 225 139 38% 54-55, 66, 79, 94-105, 108, 118, 124, 130, 143, 156, 167, 178, 189, 196, 225-228, 235, 239, 260-261, 265-266, 270-274, 278-279, 283-284, 288, 292-293, 300-301, 305, 311-312, 321-325, 329, 333, 341, 345-346, 350-351, 355-359, 366-367, 370-371, 377-378, 385-393, 397-402, 411-433, 437-440, 444-459, 467-511 /home/admin/.local/lib/python3.8/site-packages/pyarrow/hdfs.py 82 52 37% 42-49, 52, 59, 63, 67, 71, 86, 90, 94, 111, 126-131, 135-149, 153-165, 169-172, 176-185, 223-227, 235-240 /home/admin/.local/lib/python3.8/site-packages/pyarrow/ipc.py 61 36 41% 51-52, 84-85, 109-110, 121-122, 126-141, 146-150, 154, 190, 195, 234, 259-264, 282-285 /home/admin/.local/lib/python3.8/site-packages/pyarrow/types.py 155 47 70% 56, 61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116, 121, 126, 131, 136, 141, 146, 151, 156, 161, 166, 171, 176, 181, 186, 191, 196, 201, 206, 211, 223, 228, 233, 238, 243, 248, 253, 258, 263, 268, 273, 278, 283, 288, 293 /home/admin/.local/lib/python3.8/site-packages/pyarrow/util.py 96 60 38% 64, 101-106, 114-123, 127-131, 135, 142-151, 158, 177-194, 198-202, 206-207, 213-230 /home/admin/.local/lib/python3.8/site-packages/pyarrow/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/vendored/docscrape.py 473 282 40% 24, 46, 62, 82, 101, 104, 109-112, 155-157, 164, 169, 172, 183, 187-188, 222, 224, 238-241, 297-341, 349-360, 365, 373-376, 395-396, 398-399, 406, 412-420, 424-428, 431-442, 447, 450, 453-455, 458-460, 463-465, 468-481, 484-489, 492-521, 524-538, 541-555, 565-574, 577-582, 585-599, 604-607, 616-666, 670-672, 680-682, 689-693, 697-716 /home/admin/.local/lib/python3.8/site-packages/pycparser/__init__.py 25 18 28% 32-48, 82-90 /home/admin/.local/lib/python3.8/site-packages/pycparser/ast_transforms.py 21 18 14% 64-96, 103-105 /home/admin/.local/lib/python3.8/site-packages/pycparser/c_ast.py 782 446 43% 25-28, 37-51, 56, 80-100, 149-158, 164-165, 176-179, 182-185, 192-194, 197-200, 203-206, 213-216, 219-222, 225-228, 235-238, 241-244, 247-250, 257, 260, 263-264, 271-273, 276-280, 283-286, 293-295, 298-301, 304-307, 314-315, 318-321, 324-325, 332-334, 337-340, 343-346, 358-359, 362-363, 370, 373, 376-377, 394-398, 401-406, 413-414, 417-420, 423-424, 431-432, 435-438, 441-442, 449-451, 454-457, 460-463, 470, 473, 476-477, 484, 487, 490-491, 498-500, 503-505, 508-509, 516-518, 521-523, 526-527, 534-535, 538-541, 544-545, 552-553, 556-559, 562-563, 574-577, 580-581, 588-592, 595-600, 603-610, 617-619, 622-625, 628-631, 643-646, 649-652, 659-662, 665-670, 673-678, 685-686, 689-690, 693-694, 701-702, 705-706, 709-710, 721-722, 725-726, 733-736, 739-743, 746-751, 758-759, 762-765, 768-769, 776-778, 781-783, 786-787, 794-796, 799-803, 806-809, 820-823, 826-827, 839-841, 844-845, 852-853, 856-858, 861-862, 874-877, 880-881, 888-891, 894-897, 900-903, 910-912, 915-918, 921-924, 931-934, 937-941, 944-949, 962-964, 967-968, 982-984, 987-988, 1001-1003, 1006-1007, 1014-1016, 1019-1021, 1024-1025, 1032-1034, 1037-1040, 1043-1044, 1051-1053, 1056-1059, 1062-1065, 1072-1073, 1076-1077, 1080-1081 /home/admin/.local/lib/python3.8/site-packages/pycparser/c_lexer.py 227 39 83% 83-84, 92-94, 97, 278-282, 290, 306, 317, 322, 329-330, 334, 340-341, 344, 442, 446, 450, 454, 458-459, 463, 474, 478, 482, 486-487, 491-492, 496, 502-503, 513-514 /home/admin/.local/lib/python3.8/site-packages/pycparser/c_parser.py 581 219 62% 167, 177, 193, 284, 327, 336-339, 387, 395-411, 422-423, 447, 467-474, 487, 518, 543, 554, 559, 564, 571-574, 583-590, 599-601, 616, 664-669, 693-712, 753, 773, 779, 784, 789, 819, 871, 877, 884, 889, 899-900, 909-911, 923, 940-948, 981-1002, 1011, 1016, 1036-1039, 1045, 1050, 1056, 1063-1069, 1075-1085, 1119, 1144-1154, 1162-1168, 1192-1195, 1223, 1233, 1240, 1270, 1281, 1290, 1311-1315, 1320, 1326-1329, 1335-1341, 1346, 1355, 1361, 1366-1372, 1385, 1399, 1404-1410, 1425-1431, 1436, 1445-1450, 1455, 1467, 1476, 1480, 1486, 1490, 1494, 1498, 1502, 1506, 1511, 1515, 1519, 1523, 1528, 1532, 1536, 1542, 1546-1549, 1555-1562, 1575, 1595, 1599, 1608, 1634, 1642, 1653, 1659, 1672, 1680, 1686, 1694-1695, 1701, 1707, 1711, 1721, 1725, 1730-1731, 1740-1747, 1753-1757, 1761, 1774, 1776, 1779, 1781, 1790-1800, 1807, 1819-1824, 1830-1835, 1857-1863 /home/admin/.local/lib/python3.8/site-packages/pycparser/lextab.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pycparser/ply/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pycparser/ply/lex.py 692 415 40% 59-60, 66, 69, 80, 83, 86, 95, 98, 145-169, 175-206, 213, 219, 244, 255, 265, 277-278, 284, 290, 296, 339-340, 360-361, 368-396, 399-407, 411, 416, 419-422, 440, 450-454, 463-469, 494-523, 546, 578-581, 587-589, 592-594, 597-599, 605-612, 622-630, 637-638, 642-644, 647-649, 651-653, 655-657, 679-681, 693-694, 696-699, 708, 711-712, 717-718, 730-822, 833-856, 867, 881-882, 896, 902-903, 909-910, 920-1047, 1056-1080, 1092 /home/admin/.local/lib/python3.8/site-packages/pycparser/ply/yacc.py 1916 1562 18% 97, 114, 119, 122, 129, 132, 140-146, 150-156, 176-177, 180-181, 184-185, 190-198, 219, 222, 242, 246, 252, 264-266, 272-274, 277, 295, 298-303, 321, 325-327, 329, 350-683, 697-989, 1015-1016, 1030, 1063, 1087-1088, 1123-1135, 1157-1168, 1176-1271, 1312-1339, 1342, 1345, 1348, 1351, 1354, 1358-1370, 1374-1375, 1392, 1395, 1429-1437, 1440-1444, 1447, 1455-1460, 1475-1507, 1511, 1514, 1525-1530, 1551-1624, 1634-1640, 1652-1662, 1673-1727, 1737-1745, 1754-1759, 1769-1774, 1786-1791, 1804-1829, 1837-1864, 1875-1914, 1933-1958, 1980, 1986, 1999-2023, 2056-2064, 2067-2087, 2101-2133, 2138-2154, 2165-2195, 2199-2224, 2255-2270, 2284-2292, 2304-2320, 2330-2341, 2372-2437, 2452-2455, 2474-2477, 2492-2500, 2511-2526, 2534-2718, 2727-2839, 2849-2866, 2884-2888, 2896-2927, 2947, 2961-2967, 2982-2983, 2999-3018, 3027-3029, 3037-3055, 3061-3063, 3066-3068, 3071-3073, 3080-3089, 3097-3124, 3149-3206, 3219, 3226, 3239, 3246-3256, 3262-3263, 3276, 3285, 3294-3494 /home/admin/.local/lib/python3.8/site-packages/pycparser/plyparser.py 55 7 87% 26-28, 67, 107-112 /home/admin/.local/lib/python3.8/site-packages/pycparser/yacctab.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/pydrive/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pydrive/apiattr.py 80 54 32% 16, 20-22, 26-31, 37-39, 54-56, 65, 74, 78-79, 83-84, 88-90, 97-103, 121-126, 133, 144-148, 159-167, 176, 180-181 /home/admin/.local/lib/python3.8/site-packages/pydrive/auth.py 305 231 24% 50-75, 82-100, 107-127, 165-176, 184-186, 204-241, 250-256, 264-275, 284-291, 302-310, 322-329, 338-349, 361-372, 383-410, 416-423, 430-434, 441-457, 467-477, 484-486, 495-496, 505-511, 518-523, 531-533 /home/admin/.local/lib/python3.8/site-packages/pydrive/drive.py 15 5 67% 16-17, 28, 39, 47 /home/admin/.local/lib/python3.8/site-packages/pydrive/files.py 267 199 25% 41-43, 55, 63-72, 95-120, 133-142, 155-157, 169-173, 190-194, 207-213, 228-244, 252-270, 279-285, 292, 300, 309, 322-331, 341-342, 352, 362-375, 384-395, 405-416, 427-435, 446-460, 471-481, 490-492, 503-506, 518-531, 543-571, 583-609 /home/admin/.local/lib/python3.8/site-packages/pydrive/settings.py 46 34 26% 5-6, 141-146, 156, 169-171, 186-213 /home/admin/.local/lib/python3.8/site-packages/pygit2/__init__.py 92 43 53% 123-162, 203-225 /home/admin/.local/lib/python3.8/site-packages/pygit2/_build.py 18 12 33% 46-53, 58-67 /home/admin/.local/lib/python3.8/site-packages/pygit2/blame.py 69 36 48% 33-36, 44-47, 52, 58, 63, 68, 72, 77, 82, 86, 91-95, 102-105, 108, 111, 114-118, 130-137, 140 /home/admin/.local/lib/python3.8/site-packages/pygit2/callbacks.py 209 162 22% 84-86, 89-93, 111-115, 148, 170, 221-238, 243-263, 268-288, 293-309, 328-341, 352-367, 372-378, 383-390, 395-402, 407-412, 417-423, 428-435, 440-448, 458-505 /home/admin/.local/lib/python3.8/site-packages/pygit2/config.py 186 125 33% 35-38, 44-45, 48, 51, 54-58, 61, 64, 69-70, 78-87, 91-95, 98-101, 104-109, 112-118, 121-128, 136-138, 141-151, 154-157, 166-170, 178-185, 191-196, 202-206, 216-221, 231-236, 241-243, 251-255, 263-267, 271-275, 283-289, 295, 301, 307, 321-336, 339-340, 345, 349, 353, 358, 363, 368 /home/admin/.local/lib/python3.8/site-packages/pygit2/credentials.py 56 18 68% 52, 56, 60, 63, 74-75, 79, 83, 86, 113-116, 120, 124, 127, 132, 138 /home/admin/.local/lib/python3.8/site-packages/pygit2/errors.py 26 20 23% 34-65, 70 /home/admin/.local/lib/python3.8/site-packages/pygit2/ffi.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/pygit2/index.py 226 171 24% 44-50, 54-59, 63, 66, 69, 72-77, 80-94, 97, 110-111, 115-116, 119-120, 129-144, 157-167, 172-173, 178-180, 188-190, 202-212, 232-249, 272-295, 322-331, 338-343, 348, 353, 356, 359-360, 365-369, 377-384, 388-396, 402, 405-417, 420-421, 424, 430-434, 437, 440, 443-457, 460 /home/admin/.local/lib/python3.8/site-packages/pygit2/packbuilder.py 40 23 42% 37-43, 47, 50, 53, 57-59, 62-64, 67-69, 72, 75-77, 81 /home/admin/.local/lib/python3.8/site-packages/pygit2/refspec.py 37 17 54% 37-38, 43, 48, 53, 58, 63, 69, 74, 77-84, 90, 96 /home/admin/.local/lib/python3.8/site-packages/pygit2/remote.py 155 112 28% 41-60, 69-71, 74, 80, 86, 92, 97-101, 106-107, 122-130, 138-164, 169-171, 177, 181-182, 188-192, 198-202, 220-224, 241-249, 252-265, 268-275, 284-296, 306-319, 326-327, 332-333, 338-339, 345-346, 352-353 /home/admin/.local/lib/python3.8/site-packages/pygit2/repository.py 552 422 24% 77, 85, 105-117, 121, 139-163, 169-174, 184-198, 204-205, 208-211, 214, 217, 223-224, 237-241, 250-254, 281-291, 308-316, 324-346, 353-354, 361-362, 369-373, 410-429, 444-453, 459-479, 543-568, 575, 615-636, 644-648, 690-698, 704-729, 743-762, 818-838, 894-919, 983-1039, 1074-1091, 1095-1104, 1128-1129, 1141, 1148-1149, 1187-1224, 1249-1262, 1288-1303, 1310-1316, 1326-1327, 1347-1363, 1372-1375, 1383-1393, 1396-1399, 1402-1404, 1407, 1410, 1413-1416, 1423-1424, 1427, 1439-1442, 1445-1446, 1449, 1452, 1455, 1459, 1462, 1491, 1493, 1497, 1501-1506 /home/admin/.local/lib/python3.8/site-packages/pygit2/settings.py 74 25 66% 40, 43, 65-71, 81, 86, 90, 98, 102, 110, 120, 128, 138, 148, 153, 158, 163, 168, 173, 178 /home/admin/.local/lib/python3.8/site-packages/pygit2/submodule.py 37 19 49% 35-40, 43, 47-51, 56-57, 62-63, 68-69, 74-75, 80-81 /home/admin/.local/lib/python3.8/site-packages/pygit2/utils.py 60 46 23% 33-36, 40-49, 53-62, 66-70, 85-102, 105, 108, 119-121, 124, 127-132 /home/admin/.local/lib/python3.8/site-packages/python_http_client/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/python_http_client/client.py 106 27 75% 11-15, 52, 59-62, 126, 129-130, 135, 145, 177-184, 208-216, 243, 246, 253, 293, 296 /home/admin/.local/lib/python3.8/site-packages/python_http_client/exceptions.py 46 16 65% 8-17, 20, 30, 93-97 /home/admin/.local/lib/python3.8/site-packages/pytz/__init__.py 198 125 37% 56-75, 87-108, 113-124, 167-190, 195, 204-206, 226-228, 231, 234, 237, 240, 244-246, 250-254, 257, 260, 295, 307, 347, 350-366, 379-390, 403-406, 409, 412, 415, 418, 421, 425-427, 431-435, 491-502, 509-512, 516 /home/admin/.local/lib/python3.8/site-packages/pytz/exceptions.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pytz/lazy.py 100 59 41% 4-8, 21-28, 31-38, 41-48, 51-58, 61-68, 87, 98-106, 142, 151-160 /home/admin/.local/lib/python3.8/site-packages/pytz/tzfile.py 76 66 13% 21, 25-123, 126-133 /home/admin/.local/lib/python3.8/site-packages/pytz/tzinfo.py 178 126 29% 7-8, 34-41, 49-58, 66, 76, 87-89, 97, 105, 113, 117-119, 144-148, 151, 156, 183-194, 198-204, 251-259, 320-397, 422-428, 461-467, 499-505, 508-517, 524, 542-580 /home/admin/.local/lib/python3.8/site-packages/reportlab/__init__.py 28 2 93% 12, 18 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/PyFontify.py 67 46 31% 101-152, 156-161 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/abag.py 19 8 58% 27-30, 33-36 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/boxstuff.py 40 28 30% 9-19, 48, 50, 52-53, 55-56, 58-78, 80-83 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/colors.py 716 389 46% 60, 75, 81-86, 92-93, 97, 101, 104, 107, 110, 113, 116-117, 120-121, 125-126, 131-133, 136-144, 148, 153, 193-197, 200, 211-215, 230, 233, 237, 241, 244, 248-256, 260, 269, 278-283, 291, 299, 313-321, 326-330, 377-385, 388, 398-499, 503-511, 697, 707, 735-748, 751-756, 759-764, 770-776, 779, 782-788, 791-796, 799-803, 810-833, 845, 852-854, 863-883, 890, 895-917, 921-932, 936-947, 954-966, 969-974, 978-981, 985-991, 995-1001, 1005-1014, 1017-1024, 1028-1040, 1044-1045 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/enums.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/fonts.py 31 9 71% 79, 87-91, 95-97 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/geomutils.py 11 8 27% 24-32 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/logger.py 40 19 52% 19-23, 27-33, 37-38, 41, 53-55, 58 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/normalDate.py 401 263 34% 35, 41, 44, 47, 50, 58-66, 134, 140-143, 147-152, 156, 160, 163-165, 168-170, 173-175, 178-180, 183-185, 188-190, 194, 198, 202, 206, 210, 214-221, 228-231, 234-240, 244, 248, 252-253, 257-258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 307-317, 321, 324, 327, 334, 339, 341, 347, 350, 353, 356, 358, 363, 365, 370-373, 377, 381, 385, 389, 393, 397-440, 445-450, 457-460, 464-475, 479-484, 488-491, 500-507, 510-515, 517, 520-527, 532-534, 537-540, 544, 548, 558, 562-565, 569-584, 588, 601-603, 607-611, 614, 617, 620, 623, 626-627, 630-633, 636-637, 640-657 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/pagesizes.py 58 8 86% 72-76, 80-84 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/rl_accel.py 239 207 13% 29-30, 34-36, 39-68, 71-96, 99-103, 106-113, 116-118, 121-124, 127-132, 135-152, 155-235, 238-312, 315-322, 328, 333-348 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/rl_safe_eval.py 625 424 32% 66-68, 73-79, 83-85, 88, 103-119, 122, 169-198, 201-202, 227-264, 267, 270, 276, 286-320, 327-328, 338-348, 358-365, 373-378, 387-395, 399-407, 425-460, 467-489, 508-532, 540-582, 597-618, 628-629, 649-652, 656-669, 674-677, 681-685, 689-701, 705-713, 716-728, 736, 739, 743, 746-750, 840, 844-845, 848, 851-852, 858-864, 873-962, 965-966, 969-970, 973, 976, 979-982, 985-988, 991, 994, 997, 1000-1004, 1007, 1010, 1013, 1016, 1019, 1022, 1025, 1028, 1031, 1041-1042, 1055-1066, 1073-1077, 1080-1083, 1086-1102, 1109-1112, 1115-1118, 1121-1127, 1130-1141, 1144-1148, 1151-1158, 1161, 1164-1175, 1178-1206, 1215-1216 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/rltempfile.py 23 15 35% 18-21, 25-33, 36-38 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/sequencer.py 187 127 32% 18-29, 32, 36, 40-41, 45-46, 65-68, 71, 74-77, 80-84, 88, 92, 95, 98-99, 148-153, 158-160, 165, 168-176, 179-181, 186-188, 192, 198, 203-204, 207-209, 212-214, 219-223, 227, 231-235, 249-251, 255-256, 263-307, 311 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/styles.py 134 50 63% 81, 93-101, 104-109, 170, 223-224, 227-231, 234, 237, 242, 244, 248, 250, 257-266, 269-279 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/textsplit.py 88 69 22% 41, 56, 75-90, 109-184, 206-220, 229, 239-241 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/units.py 17 10 41% 21-30 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/utils.py 1051 740 30% 12-13, 21, 24, 35-36, 41-42, 45, 51, 55, 61, 74-78, 82-85, 88-92, 100, 112, 115, 123, 130-132, 137-139, 144-147, 150, 153, 157, 161, 166, 168-170, 172-174, 179, 182-183, 186-196, 200-201, 204, 207-211, 214-218, 221-225, 228-231, 234-238, 241-245, 248-252, 258-285, 288, 301-302, 309-346, 351, 353, 359, 363, 367, 370, 376-418, 423-426, 434-439, 443-447, 453-454, 461-498, 501-507, 511-517, 523-530, 535, 551-568, 571-575, 583-591, 596-601, 604, 607-611, 616-618, 621-624, 627-635, 639-644, 647-665, 670-671, 679-680, 691-696, 703-714, 717-719, 727-741, 745-749, 753-754, 762-766, 770-773, 776-778, 786-787, 798-814, 819-823, 825-831, 834-835, 838-839, 843, 846-855, 861, 865, 870-873, 902-990, 993-995, 998, 1001-1013, 1016-1020, 1023-1025, 1028, 1031-1035, 1038, 1041-1054, 1057, 1060, 1063, 1066-1068, 1071-1072, 1075-1079, 1082-1093, 1101-1107, 1110, 1113, 1116, 1119, 1135-1137, 1141-1142, 1144-1150, 1155-1156, 1159-1173, 1176-1184, 1188-1196, 1199-1207, 1226-1234, 1249-1266, 1279, 1283-1292, 1296-1297, 1310-1324, 1329-1338, 1346-1349, 1354, 1361-1362, 1365, 1368, 1371, 1376, 1379-1382, 1397-1400, 1404, 1408-1413, 1418-1425, 1428-1435, 1439-1449 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/validators.py 267 117 56% 20, 23, 26-30, 34, 38, 42-43, 46-53, 57, 61-67, 71-72, 75-78, 82-83, 86, 90, 93-94, 100, 104-105, 108-114, 120-128, 134-135, 140-149, 154, 159-160, 165-167, 170, 179-180, 184, 188, 202-204, 209, 220-228, 237-239, 247-248, 253-254, 258, 262, 269, 273, 277, 279, 283, 285, 290, 293-295, 313, 317, 320, 329-332 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata.py 88 33 62% 74-94, 99, 104-108, 112-125, 137-141, 248-251 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_macexpert.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_macroman.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_pdfdoc.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_standard.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_symbol.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_winansi.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_zapfdingbats.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courier.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courierbold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courierboldoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courieroblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helvetica.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticabold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticaboldoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticaoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_symbol.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesbold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesbolditalic.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesitalic.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesroman.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_zapfdingbats.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/pdfdoc.py 1657 716 57% 32-35, 78, 89, 140, 165, 188-189, 194, 203, 206-213, 219, 223, 229-230, 236, 251-254, 273, 276-277, 283, 301, 304, 307-308, 311-314, 317-335, 342, 350, 359, 366, 373, 380, 385, 388-395, 410, 432-436, 444, 462, 468-469, 475-487, 516, 519, 526-529, 550, 554-555, 558, 565-572, 579-580, 593-595, 604-621, 627-633, 637-638, 642-643, 645, 647, 650, 653, 662, 676, 678, 680, 685-687, 701, 705-708, 714-717, 720, 725-726, 729-734, 766-769, 780, 783, 805, 832-839, 856, 861-864, 893-894, 913, 948, 950, 972, 983, 1041, 1044, 1047-1048, 1051-1052, 1070, 1116, 1127, 1134-1135, 1139, 1154, 1159, 1179, 1195, 1198-1209, 1274-1278, 1281-1283, 1287-1296, 1312, 1318-1327, 1362-1395, 1398, 1402-1408, 1411-1412, 1416-1417, 1421-1444, 1450-1453, 1462-1464, 1467-1519, 1523-1536, 1575-1578, 1589-1593, 1595-1612, 1614, 1618-1619, 1625-1627, 1629-1634, 1639-1642, 1644-1650, 1657-1661, 1664, 1670-1677, 1691-1695, 1699-1709, 1712-1719, 1726, 1736, 1738-1739, 1745, 1780-1781, 1783-1788, 1790, 1792, 1794, 1796, 1798, 1800, 1802, 1804, 1806, 1812-1815, 1817-1819, 1824, 1826-1828, 1836, 1838-1840, 1848, 1850-1852, 1860, 1862-1864, 1878, 1894, 1898, 1906-1907, 1917, 1997, 2000-2002, 2006-2009, 2012-2050, 2055, 2058-2064, 2089, 2099-2102, 2107-2119, 2127-2128, 2132, 2136-2137, 2143, 2148-2160, 2165-2167, 2171, 2174-2176, 2182, 2194, 2209, 2211, 2220-2225, 2230-2248, 2251, 2272-2283, 2286, 2291-2292, 2299-2302, 2304-2310, 2316-2319, 2321-2327, 2334-2343, 2346, 2351-2352, 2358-2360, 2362-2367, 2374-2377, 2379-2385, 2392-2395, 2397-2403, 2406 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/pdfmetrics.py 548 340 38% 52-115, 142, 154, 157-181, 184-191, 197-210, 226-240, 245, 249-253, 260, 264-268, 289-295, 297, 315-330, 365, 379-384, 389-396, 418-421, 438-446, 455-464, 467, 472-484, 490-546, 550-576, 584, 590-593, 600-609, 616-625, 633, 650-660, 673, 708, 716, 719, 722, 726-728, 736-756, 760-782, 785-792, 796-804, 815-819, 826-827 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/pdfutils.py 172 112 35% 17-19, 30-55, 57-81, 86-105, 116-128, 136-146, 161, 173-178, 187-194, 198-209, 252, 268, 277-280, 283, 286, 289-293, 296-297 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/rl_codecs.py 82 20 76% 993, 1003, 1036, 1038-1042, 1055-1057, 1065-1068, 1072-1076, 1080 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfgen/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfgen/canvas.py 987 563 43% 55-62, 74-87, 90-98, 101-135, 158, 162, 166, 175-176, 178-181, 185-192, 195, 200, 364-372, 434, 438, 440, 463, 517, 536, 567, 573, 579, 604, 607, 610, 613, 616-622, 635-680, 693, 695, 712-713, 724, 746-751, 791-828, 839, 843-844, 862-863, 867, 889-895, 966, 992-999, 1011, 1021, 1035-1043, 1055-1063, 1069-1089, 1109-1128, 1131-1148, 1152-1153, 1158-1159, 1175-1178, 1181-1186, 1205, 1213-1219, 1231-1255, 1258-1262, 1265-1266, 1270, 1283-1287, 1292-1293, 1297-1301, 1304, 1307, 1310, 1320-1323, 1340-1348, 1369-1374, 1391-1393, 1396-1398, 1462-1465, 1468-1482, 1487-1496, 1500, 1508-1509, 1523-1524, 1529-1533, 1538-1542, 1548-1549, 1552-1554, 1557-1558, 1562-1571, 1575-1584, 1612-1625, 1631-1644, 1674-1699, 1706-1707, 1711-1713, 1723, 1733-1735, 1739, 1761-1762, 1766-1779, 1787, 1791-1794, 1797, 1801-1806, 1829, 1844, 1876-1918, 1925, 1929-1934, 1938, 1942, 1945-1952, 1955, 1959, 1963-1970, 1975-1980, 1984-1987, 1990 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfgen/pathobject.py 78 59 24% 32-33, 36-40, 44, 47, 50, 53, 65, 70, 74, 78, 81-83, 87-90, 98-135, 139 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfgen/pdfgeom.py 29 25 14% 30-77 /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfgen/textobject.py 291 154 47% 22, 24-31, 37-41, 51, 57, 62, 67, 72, 74-81, 87-95, 99, 101, 108, 110-117, 122-135, 137, 143-146, 150-153, 157, 162, 166, 169, 172, 205, 212, 220-230, 243-261, 280, 285, 289, 293, 300-308, 318, 322, 329-330, 336-337, 341-342, 346-347, 364-370, 374-376, 382, 388-393, 396-401, 406-410, 414-415, 418, 427-428, 438, 453-465, 469, 472-473, 476, 479, 482 /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/doctemplate.py 819 417 49% 61-62, 73-89, 97-100, 104-108, 112, 120, 123, 129, 134, 138, 165-171, 174, 177, 182, 189, 193, 198, 202, 206, 215-217, 220-222, 228-232, 237, 240, 252-253, 256-257, 262, 265-266, 272, 323-326, 330, 342-343, 347-351, 359-363, 366, 369, 372, 375, 378-393, 397-398, 402-407, 411, 414, 418, 535-537, 579, 583, 593, 603-604, 623, 631, 635-637, 640-647, 660, 665-669, 672, 689-691, 699, 704, 706, 718-721, 728-730, 740-767, 770-798, 802-817, 821-830, 834-835, 844-845, 849-851, 853-855, 857-859, 861-863, 871-885, 888-892, 908, 911-920, 939, 943-945, 951, 954-958, 963, 1018-1020, 1023, 1034-1035, 1056-1057, 1070-1075, 1081-1095, 1097, 1107, 1111-1116, 1120-1128, 1132-1134, 1144-1188, 1229-1232, 1235-1247, 1251-1256, 1264-1267, 1271-1274, 1311, 1313, 1350, 1353-1400 /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/flowables.py 1875 1491 20% 50-54, 102-105, 116-117, 132, 136, 141-144, 153, 175, 183-191, 195, 198, 201-204, 210-213, 216, 219-225, 229-233, 242-252, 258-270, 276-310, 324-328, 336-340, 343-345, 348-351, 357-368, 375-388, 410-411, 413-417, 429-432, 438, 442, 449-450, 467, 469-470, 476-482, 485-490, 493-495, 502-503, 519-526, 538-539, 542-545, 549, 557-561, 564, 568, 571, 574-576, 583, 586, 598, 601, 604-609, 612, 616-665, 669-672, 676-679, 682-685, 691-703, 706-710, 713-721, 724-746, 749-753, 767, 769, 771, 773, 776, 785-786, 788, 790-791, 793, 798-802, 805, 808, 811-832, 835-844, 847-857, 861, 865, 868, 880-889, 892, 895-902, 905-912, 916-917, 920-923, 928-975, 978-981, 991-998, 1001-1002, 1005-1083, 1089-1100, 1104-1118, 1129-1140, 1143, 1146, 1152-1209, 1212-1226, 1232-1275, 1278-1285, 1291-1298, 1301-1304, 1307-1313, 1316, 1319-1323, 1326-1359, 1362-1379, 1390-1405, 1411-1418, 1421, 1424-1434, 1440-1442, 1445, 1448-1456, 1462-1468, 1471, 1474-1479, 1493-1513, 1516, 1521, 1524, 1527-1768, 1772-1780, 1787-1788, 1791, 1794, 1797, 1801-1802, 1806-1807, 1810-1811, 1814-1837, 1847-1856, 1859, 1862-1883, 1893-1897, 1900-1935, 1953-1965, 1980-1989, 1992-2018, 2021-2022, 2027-2031, 2034-2035, 2043, 2046-2049, 2052-2058, 2061-2064, 2067-2070, 2073, 2078-2087, 2090-2091, 2100-2102, 2124-2146, 2150-2153, 2175-2213, 2217-2220, 2223-2226, 2229, 2232-2260, 2263-2270, 2273-2283, 2297-2404, 2411, 2414-2416, 2419-2422, 2425, 2428, 2431, 2434, 2440-2441, 2444-2453, 2456, 2459-2460, 2465-2466, 2476-2481, 2484-2487, 2490, 2493-2501, 2504-2516, 2520-2522, 2525-2526, 2529-2532, 2536-2538, 2541-2543, 2546-2547, 2551-2553, 2556-2558, 2563-2564, 2567-2572, 2577-2578, 2581-2586 /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/frames.py 195 75 62% 25-27, 30, 97-98, 103-108, 112-116, 154-155, 166, 179-180, 188-211, 219, 259-278, 281, 289-299, 302, 305 /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/multicol.py 88 77 12% 7-14, 17-40, 43-57, 60-77, 91-96, 99-104, 107-111 /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/paragraph.py 2141 1799 16% 76-86, 98-99, 102-103, 142-145, 152-153, 157-159, 167-168, 174-178, 183-199, 202-216, 219, 223-239, 242-245, 248-263, 268-427, 430-444, 447-460, 463-482, 485-500, 503-516, 519, 522-523, 556-568, 572-586, 591-626, 648-666, 669, 678-860, 863-872, 886-918, 926-936, 939-949, 965-1026, 1034-1048, 1052, 1055-1143, 1147-1163, 1185-1196, 1203-1217, 1227-1231, 1244-1263, 1267-1277, 1280-1296, 1299-1305, 1308-1320, 1324-1371, 1377-1391, 1404-1449, 1452-1459, 1462-1482, 1486-1499, 1502-1508, 1511-1557, 1562-1594, 1599-1606, 1611-1640, 1644-1721, 1724-1730, 1797, 1804-1810, 1823, 1838, 1848, 1855-1866, 1869, 1871, 1878-1887, 1890-1898, 1901, 1906-1988, 2037-2044, 2054, 2076, 2080-2084, 2095, 2097, 2102-2156, 2161-2168, 2173-2180, 2188-2398, 2404-2441, 2465-2467, 2475-2496, 2518-2521, 2526, 2528, 2536, 2538, 2549, 2552-2581, 2585-2656, 2664-2684, 2691-2696, 2700-2713, 2716-2918 /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/paraparser.py 745 487 35% 36-45, 55-59, 62-65, 68-73, 76-79, 83-86, 89-94, 97-100, 103, 106-108, 118, 121-122, 125-128, 134-136, 139-140, 147-156, 159-170, 173-176, 179-184, 187-190, 193-197, 200-204, 357-363, 2511-2526, 2594, 2599, 2602, 2605, 2608, 2612, 2615, 2618, 2621, 2624-2635, 2639-2641, 2644, 2648-2650, 2653, 2657-2666, 2669, 2672-2673, 2677-2690, 2693-2706, 2709-2713, 2716-2729, 2733-2738, 2741, 2748-2752, 2755, 2758-2759, 2762, 2767-2775, 2778, 2781-2784, 2787, 2790, 2793-2819, 2822, 2825-2828, 2831, 2834-2846, 2849, 2852, 2856-2861, 2866-2868, 2877-2879, 2884-2885, 2916-2922, 2925, 2929-2933, 2936, 2939-2947, 2950, 2953-2960, 2964-2972, 2989-3009, 3012, 3015-3023, 3028-3052, 3056, 3061-3064, 3069, 3072-3085, 3124-3125, 3127-3128, 3132-3133, 3142-3143, 3148, 3157, 3166, 3176-3197, 3200-3201, 3205-3207, 3210, 3221-3222, 3233, 3238-3241, 3251-3254, 3260-3264, 3267-3399 /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/tables.py 1452 828 43% 60, 62-66, 73-77, 86, 88, 112-124, 133-134, 138-140, 146-155, 158-176, 187-198, 201-205, 209-217, 220-223, 227-243, 263, 269-270, 275-289, 298-305, 308, 315, 348, 350, 353-357, 363-369, 380, 393-429, 435-436, 446-451, 453, 494-495, 501-502, 516, 520, 523-528, 534-537, 561-565, 570-574, 576, 580-581, 599-602, 622, 629, 632-633, 638-639, 645-650, 661-663, 667-670, 694, 702-704, 707, 717, 726-732, 746, 754, 770-910, 913-932, 945-979, 991-1027, 1041-1079, 1087, 1090, 1104, 1107, 1117-1118, 1126-1127, 1132-1136, 1141-1144, 1148-1159, 1168-1181, 1191-1195, 1203, 1209-1211, 1214-1215, 1254-1256, 1259-1261, 1278-1280, 1283-1285, 1301-1310, 1313-1343, 1347-1357, 1368-1374, 1387, 1403-1443, 1450-1453, 1457-1481, 1497-1500, 1509-1520, 1526, 1528, 1547, 1553-1577, 1580-1654, 1670-1677, 1681, 1704, 1710-1719, 1722-1731, 1741-1756, 1761-1764, 1788, 1792, 1796-1800, 1809-1819, 1826-1832, 1839, 1843, 1846, 1852, 1876-1909, 1944-1945 /home/admin/.local/lib/python3.8/site-packages/reportlab/platypus/xpreformatted.py 208 186 11% 17-36, 39-43, 47, 61-79, 83-85, 119-207, 213, 227-229, 232-235, 240-254, 257-345 /home/admin/.local/lib/python3.8/site-packages/reportlab/rl_config.py 95 15 84% 22, 29, 35-36, 39-40, 56, 100-101, 127-133 /home/admin/.local/lib/python3.8/site-packages/reportlab/rl_settings.py 76 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/__init__.py 68 27 60% 49-50, 54-55, 64, 80-86, 91-100, 108-109, 123-124, 127-136 /home/admin/.local/lib/python3.8/site-packages/requests/__version__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/_internal_utils.py 21 3 86% 33, 49-50 /home/admin/.local/lib/python3.8/site-packages/requests/adapters.py 194 82 58% 61, 93, 97, 145, 158, 163-169, 211-235, 255, 261, 271-275, 278-290, 318, 343-351, 368, 390-391, 395, 426-432, 455-456, 472-476, 481, 500-536 /home/admin/.local/lib/python3.8/site-packages/requests/api.py 19 6 68% 85, 99-100, 130, 145, 157 /home/admin/.local/lib/python3.8/site-packages/requests/auth.py 173 141 18% 35-66, 73, 80-81, 84, 92, 95-96, 103-104, 111-114, 118-124, 131-234, 238-239, 250-284, 288-304, 307, 315 /home/admin/.local/lib/python3.8/site-packages/requests/certs.py 4 1 75% 17 /home/admin/.local/lib/python3.8/site-packages/requests/compat.py 30 5 83% 12-13, 36-37, 42 /home/admin/.local/lib/python3.8/site-packages/requests/cookies.py 239 149 38% 19-20, 41, 44, 47, 52-58, 70, 73, 76, 80, 85, 92, 96, 100, 121, 132, 156-167, 201-204, 212-223, 231-232, 240, 248-249, 257, 265-266, 275, 279-283, 287-291, 299-304, 313-319, 322-325, 334, 341, 347, 350-356, 362-364, 378-384, 398-413, 417-420, 424-426, 430-433, 437, 441-452, 461-489, 495-504, 536-537, 550, 553, 557-559 /home/admin/.local/lib/python3.8/site-packages/requests/exceptions.py 37 8 78% 19-24, 41-42 /home/admin/.local/lib/python3.8/site-packages/requests/hooks.py 14 6 57% 27-32 /home/admin/.local/lib/python3.8/site-packages/requests/models.py 455 182 60% 95, 118, 122-126, 134, 147, 149, 178-183, 189, 192-195, 211, 214, 223-227, 281, 294, 298-311, 381, 384-392, 402-408, 418, 429-430, 435-436, 439, 445, 452-455, 457, 462, 465, 469, 472, 476-479, 487-493, 508-516, 528-529, 537-542, 545, 550, 563, 587, 598-609, 625, 629, 707, 710, 715-718, 721-726, 729, 739, 749, 753, 764-768, 780, 788, 793, 817-831, 836, 838, 849, 863-885, 894, 897, 924, 928, 933-940, 957-968, 972-975, 981-992, 997-1021 /home/admin/.local/lib/python3.8/site-packages/requests/packages.py 17 4 76% 5-10 /home/admin/.local/lib/python3.8/site-packages/requests/sessions.py 268 118 56% 56, 86, 100-103, 116-124, 129-157, 178-281, 288-301, 315-332, 338-354, 601-602, 612-613, 623-624, 637, 649, 661, 671, 684, 689, 716-717, 732-735, 763, 794, 810, 813-814, 817-818, 833 /home/admin/.local/lib/python3.8/site-packages/requests/status_codes.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/structures.py 39 11 72% 55, 65, 68-73, 77, 80, 91, 96, 99 /home/admin/.local/lib/python3.8/site-packages/requests/utils.py 485 315 35% 76-121, 127-130, 141, 144-157, 170-191, 204, 216-220, 223-224, 230-253, 274-297, 303-310, 331-337, 358, 361, 393-398, 424-433, 445-459, 469-474, 485, 493-506, 548, 556, 566-577, 582-587, 602-626, 643-655, 674-678, 689-693, 703-704, 711-715, 724-739, 752-753, 758-761, 784, 789-809, 815-816, 819, 831, 845, 856-857, 873-886, 920-946, 962-984, 993-1013, 1038-1040, 1044-1056, 1068-1076, 1083-1094 /home/admin/.local/lib/python3.8/site-packages/seaborn/__init__.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/_core.py 638 564 12% 48, 52-54, 58, 62-65, 90-138, 142-159, 163-174, 180-211, 215-254, 273-314, 318-325, 329-338, 342-399, 403-478, 498-546, 550-554, 558-577, 604-615, 621-627, 632, 647-653, 657-681, 706-821, 850-935, 963-1016, 1021-1058, 1062-1073, 1091-1161, 1165-1178, 1186-1191, 1218-1273, 1304-1348, 1369-1399, 1418-1445, 1467-1484 /home/admin/.local/lib/python3.8/site-packages/seaborn/_decorators.py 31 7 77% 32-46 /home/admin/.local/lib/python3.8/site-packages/seaborn/_docstrings.py 36 1 97% 30 /home/admin/.local/lib/python3.8/site-packages/seaborn/_statistics.py 196 168 14% 69-79, 83-87, 91-96, 100-114, 118-126, 130-140, 144-158, 162-182, 186-189, 232-241, 245-259, 263-312, 316-342, 346-366, 370-373, 389-391, 395, 399-413, 417-426 /home/admin/.local/lib/python3.8/site-packages/seaborn/algorithms.py 66 60 9% 36-86, 91-105, 114-129 /home/admin/.local/lib/python3.8/site-packages/seaborn/axisgrid.py 1011 915 9% 38-43, 47-50, 54-56, 60-64, 95-176, 180-183, 187-196, 200-226, 231-234, 309-474, 581-606, 632-688, 720-755, 759-763, 768-779, 783-785, 791-799, 803-804, 808-815, 819-826, 830-837, 841-853, 857-865, 890-957, 964, 969, 974-980, 991, 998-1011, 1016-1023, 1028-1035, 1040-1052, 1057-1069, 1145-1250, 1263-1267, 1280-1282, 1295-1297, 1310-1321, 1335-1406, 1411-1447, 1454-1469, 1473-1508, 1512-1553, 1557-1562, 1566-1570, 1591-1669, 1673-1676, 1700-1702, 1724-1738, 1761-1799, 1820-1822, 1830-1831, 1962-2049, 2064-2214 /home/admin/.local/lib/python3.8/site-packages/seaborn/categorical.py 1249 1116 11% 44-240, 245-265, 269-324, 329-337, 342-346, 350-388, 392-397, 406-415, 419-476, 481-505, 509-512, 522-546, 551-671, 675-686, 690-692, 702-714, 718-724, 728-747, 752-757, 761-935, 939-946, 954-979, 987-995, 1001-1010, 1015-1016, 1021-1039, 1043-1046, 1057-1082, 1086-1088, 1099-1112, 1116-1156, 1160-1164, 1172-1177, 1184-1192, 1196-1209, 1215-1242, 1249-1276, 1280-1298, 1303-1332, 1336-1416, 1420-1424, 1434-1438, 1442-1545, 1550-1571, 1584-1593, 1598-1630, 1639-1642, 1655-1687, 1692-1697, 1702-1781, 1788-1791, 1805-1839, 1844-1874, 1878-1883, 1887-1890, 1896-1999, 2003-2058, 2062-2065, 2240-2249, 2397-2406, 2631-2640, 2799-2820, 2996-3017, 3179-3188, 3372-3381, 3577-3608, 3708-3724, 3743-3862 /home/admin/.local/lib/python3.8/site-packages/seaborn/cm.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/crayons.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/xkcd_rgb.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/distributions.py 923 859 7% 109, 118, 124-126, 132, 140-155, 164-176, 180-186, 192-200, 204-210, 215-268, 284-329, 353-713, 728-873, 889-1031, 1050-1215, 1221-1279, 1285-1322, 1328-1358, 1391-1456, 1618-1754, 1922-1956, 2044-2082, 2147-2302, 2395-2403, 2543-2661 /home/admin/.local/lib/python3.8/site-packages/seaborn/external/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/external/docscrape.py 455 272 40% 43, 64, 80, 100, 119, 122, 127-130, 173-175, 182, 187, 190, 201, 236, 238, 250-253, 308-350, 358-369, 374, 382-385, 391, 404-405, 407-408, 415, 421-429, 432-443, 448, 451-454, 457-460, 463-466, 469-472, 475-488, 491-496, 499-528, 531-546, 549-563, 567-571, 580, 585-609, 612-617, 620-634, 643-691, 695-697, 705-707, 714-718 /home/admin/.local/lib/python3.8/site-packages/seaborn/external/husl.py 194 151 22% 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72-91, 95-114, 118-119, 123, 127-130, 134-137, 141-144, 148-153, 157-175, 179-184, 188-189, 193-194, 198-199, 203-219, 223-235, 239-247, 251-257, 261-271, 275-285, 289-299, 303-313 /home/admin/.local/lib/python3.8/site-packages/seaborn/matrix.py 548 491 10% 25-28, 33-36, 41-49, 61-86, 98-182, 189-239, 243-255, 259-268, 272-284, 289-343, 535-549, 563-616, 619-621, 624-638, 643-651, 666, 672, 683-728, 774-780, 790-857, 861-899, 906-919, 939-949, 974-986, 990-1004, 1033-1055, 1058-1060, 1065-1085, 1100-1150, 1153-1207, 1214-1235, 1402-1408 /home/admin/.local/lib/python3.8/site-packages/seaborn/miscplot.py 27 20 26% 20-30, 35-48 /home/admin/.local/lib/python3.8/site-packages/seaborn/palettes.py 231 199 14% 64-67, 71-72, 76-77, 81-90, 145-226, 287-297, 359-371, 431-454, 459-467, 543-548, 624-629, 702-709, 729-735, 761-762, 789-790, 905-942, 948-977, 1021-1038 /home/admin/.local/lib/python3.8/site-packages/seaborn/rcmod.py 113 83 27% 113-117, 122, 127, 132-135, 174-298, 330-331, 378-443, 483-484, 489-491, 494, 497-501, 548-556 /home/admin/.local/lib/python3.8/site-packages/seaborn/regression.py 319 271 15% 13-14, 36-56, 60-66, 69, 86-136, 141-153, 158-188, 193-229, 233-248, 252-265, 269-290, 294-296, 300-317, 321-331, 335-340, 345-376, 385-408, 413-425, 576-636, 826-839, 1071-1096 /home/admin/.local/lib/python3.8/site-packages/seaborn/relational.py 349 310 11% 197-345, 363-377, 381-422, 436-563, 583-590, 603-667, 684-704, 800-822, 914-1042 /home/admin/.local/lib/python3.8/site-packages/seaborn/utils.py 281 240 15% 24-29, 49-59, 84-89, 109-124, 141, 161-168, 177-181, 198, 214-215, 243-317, 322-326, 350-368, 373-374, 383-394, 403-409, 418-424, 436-442, 477-530, 546-554, 570, 576-597, 613-619, 643-648, 653-668, 673-674, 688-695, 701-710 /home/admin/.local/lib/python3.8/site-packages/seaborn/widgets.py 184 168 9% 8-23, 38-42, 47-48, 53-58, 93-154, 188-239, 273-324, 355-383, 414-440 /home/admin/.local/lib/python3.8/site-packages/sendgrid/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/base_interface.py 22 2 91% 44, 49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/endpoints/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/__init__.py 14 6 57% 19, 30, 46-50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/eventwebhook_header.py 5 1 80% 10 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/__init__.py 63 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/amp_html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/asm.py 33 20 39% 16-23, 31, 40-43, 52, 62-65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/attachment.py 75 47 37% 43-62, 70, 79-82, 90, 99-102, 110, 119-122, 137, 162-165, 176, 191-194, 203-218 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/batch_id.py 15 7 53% 14-17, 25, 34, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings.py 27 16 41% 16-23, 31, 40, 48, 57, 66-72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings_email.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_bounce_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_list_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_spam_management.py 16 9 44% 15-18, 26, 35, 44-47 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_unsubscribe_management.py 16 9 44% 17-20, 28, 37, 46-49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/category.py 13 6 54% 10-13, 21, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/cc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/click_tracking.py 27 16 41% 12-19, 27, 36, 45, 56, 65-71 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content.py 30 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content_id.py 13 6 54% 13-16, 27, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/custom_arg.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/disposition.py 13 6 54% 21-24, 39, 63, 72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/dynamic_template_data.py 24 12 50% 16-22, 30, 39, 47, 57, 64, 73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/email.py 79 13 84% 50-54, 78, 137, 154, 171, 189, 202-203, 209, 224 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/exceptions.py 22 10 55% 24-31, 39, 48, 56, 65 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_content.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_name.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_type.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_html.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_settings.py 38 23 39% 14-25, 33, 42, 50, 59, 67, 76, 85-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_text.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/from_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ganalytics.py 63 35 44% 26-38, 48-49, 57, 66, 75, 86, 94, 103, 111, 120, 128, 137, 145, 154, 163-176 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/group_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/groups_to_display.py 15 8 47% 13-16, 25, 37-39, 48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/header.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ip_pool_name.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail.py 470 241 49% 72, 78, 88, 151-167, 171, 177-178, 183-187, 213, 229-241, 262-265, 273, 280, 296-308, 324-330, 335, 356-368, 388-394, 416-431, 445, 454-458, 466-490, 494, 503-507, 515-535, 547, 557-561, 569-592, 612-629, 633, 642-653, 674, 692-696, 708, 717-721, 737-748, 752, 768, 777-781, 789, 806-809, 821, 829-833, 841, 853, 861-865, 872, 889, 906, 923, 940, 957, 997-1013 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail_settings.py 93 58 38% 38-69, 77, 86, 94, 103, 111, 120, 128, 137, 145, 154, 162, 171, 179, 188, 196, 206, 215-243 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mime_type.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking.py 27 16 41% 16-23, 31, 40, 50, 65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking_substitution_tag.py 13 6 54% 12-15, 26, 39, 49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/personalization.py 130 38 71% 23-29, 55, 63-67, 70, 73-76, 90, 98, 110, 118, 133, 145, 152, 164, 171-174, 186, 193, 207, 220-223, 242, 247-250 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/plain_text_content.py 25 14 44% 15-19, 27, 35, 44-45, 54-60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/reply_to.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/sandbox_mode.py 16 9 44% 12-15, 23, 32, 41-44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/section.py 25 14 44% 12-18, 26, 35, 43, 52, 61-64 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/send_at.py 24 12 50% 22-28, 36, 45, 53, 63, 70, 79 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_check.py 44 27 39% 18-27, 35, 44, 54, 68-71, 80, 91-94, 103-112 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_threshold.py 13 6 54% 15-18, 29, 44, 53 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_url.py 13 6 54% 12-15, 24, 35, 44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subject.py 23 4 83% 18, 43, 53, 60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_html.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_substitution_tag.py 13 6 54% 18-21, 32, 48, 58 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_text.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_tracking.py 49 30 39% 21-33, 41, 50, 59, 71, 80, 92, 103, 120, 129-142 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/substitution.py 34 19 44% 17-26, 34, 43, 51, 60, 68, 78, 87-90 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/template_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/to_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/tracking_settings.py 49 30 39% 30-45, 53, 63, 71, 81, 89, 98, 106, 115, 124-134 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_campaign.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_content.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_medium.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_source.py 13 6 54% 11-14, 23, 34, 43 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_term.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/validators.py 27 10 63% 23-24, 46-55, 69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/stats.py 166 108 35% 12-22, 29, 38-53, 61, 70, 78, 87, 95, 104, 112, 121, 129, 138, 146, 155, 163, 172, 187-194, 202-220, 228, 236-238, 253-260, 268-286, 294, 302-304, 317-319, 327, 336, 344, 357-359, 367, 376, 384 /home/admin/.local/lib/python3.8/site-packages/sendgrid/sendgrid.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/twilio_email.py 9 4 56% 63-73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/__check_build/__init__.py 18 12 33% 19-31, 45-46 /home/admin/.local/lib/python3.8/site-packages/sklearn/__init__.py 29 9 69% 69, 103-112 /home/admin/.local/lib/python3.8/site-packages/sklearn/_config.py 21 13 38% 75-82, 144-150 /home/admin/.local/lib/python3.8/site-packages/sklearn/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/glm_distribution.py 86 54 37% 59-66, 132, 156, 175, 204, 208, 215-235, 246, 272-323, 329, 335, 341, 347 /home/admin/.local/lib/python3.8/site-packages/sklearn/base.py 259 142 45% 54-88, 108-138, 159, 169, 197-198, 221-244, 265-291, 304, 310, 322, 325-333, 362, 365, 413-419, 424-434, 449-453, 460, 464-467, 499-500, 503, 552-554, 557, 583-584, 587, 599, 619-621, 639-640, 662-665, 697-702, 724, 750, 761, 767, 784, 800, 816, 840-857 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_affinity_propagation.py 142 119 16% 22-32, 119-254, 370-377, 385, 388, 409-434, 450-463, 485 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_agglomerative.py 337 242 28% 42-80, 90-131, 221, 228, 240-241, 245-342, 423-603, 608-609, 613-614, 618-619, 658, 838, 842, 848, 852, 857, 864-866, 874-880, 888-889, 907, 910, 920-924, 946, 1077-1082, 1098-1105, 1109 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_bicluster.py 178 145 19% 35-47, 58-69, 74-83, 93-100, 103-105, 119-126, 133-164, 167-179, 291, 301-315, 444-454, 457-481, 486-520, 532-540, 544-546 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_birch.py 231 194 16% 27-37, 50-89, 140-152, 155-164, 171-175, 179-242, 281-290, 293-297, 303-315, 320-321, 437-441, 460-461, 464-518, 529-534, 555-561, 564-568, 588-595, 614-617, 623-659 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_dbscan.py 52 37 29% 141-145, 276-283, 310-361, 389-390 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_feature_agglomeration.py 19 12 37% 38-51, 70-73 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py 526 454 14% 85-144, 152-158, 289-298, 354-428, 484-541, 579-601, 771-781, 785-844, 848-853, 858-861, 869-887, 924-951, 979-1052, 1077, 1105, 1124-1127, 1131, 1154-1160, 1183-1189, 1193, 1264-1337, 1346-1403, 1571-1579, 1585, 1591, 1597, 1600-1633, 1660-1781, 1805-1813, 1835-1891, 1914-1917, 1920, 1988-2019 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_mean_shift.py 116 94 19% 68-86, 92-109, 186-191, 223-240, 358-364, 377-450, 465-468 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_optics.py 218 193 11% 210-222, 246-287, 291-298, 327-338, 448-503, 509-538, 571-578, 631-646, 690-709, 716-722, 736-744, 790-898, 920-927 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_spectral.py 120 96 20% 76-158, 260-284, 462-476, 499-544, 568, 571, 580 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/__init__.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_base.py 47 28 40% 37-44, 57-77, 131, 154-158 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_dict_learning.py 359 309 14% 27-28, 113-191, 297-354, 394-435, 547-632, 766-891, 899-905, 910-926, 945-946, 1066-1071, 1089, 1096, 1115, 1118, 1122, 1126, 1304-1318, 1336-1358, 1546-1560, 1578-1599, 1623-1650 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_factor_analysis.py 139 119 14% 155-167, 183-264, 282-294, 306-310, 320-339, 354-362, 379, 384-390, 396-414 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py 181 154 15% 49-50, 57-60, 69-95, 104-122, 128-136, 140-143, 147, 272-301, 399-411, 431-540, 557, 574-575, 593-600, 617-624 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_incremental_pca.py 88 76 14% 170-173, 191-216, 237-319, 350-358 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_kernel_pca.py 106 80 25% 151-168, 176, 179-185, 192-256, 259-267, 283-295, 310-318, 331-344, 361-369, 372 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_lda.py 238 208 13% 75-132, 303-318, 322-335, 341-362, 396-426, 456-476, 479, 491-494, 510-537, 556-609, 624-640, 658-664, 692-740, 757-763, 787-813, 837 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_nmf.py 422 383 9% 38, 51, 55-61, 91-167, 172-187, 192-203, 207-233, 238-248, 311-401, 413-432, 508-538, 544-633, 638-715, 789-850, 1022-1093, 1268-1279, 1282, 1307-1326, 1342-1343, 1358-1374, 1391-1392 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_pca.py 164 92 44% 59-97, 105-109, 376-386, 394, 402-405, 414, 416, 424-427, 435-436, 439, 445, 468, 476-477, 484, 500-564, 583-592, 613, 616 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_sparse_pca.py 68 52 24% 116-126, 144-175, 198-206, 309-316, 334-362 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_truncated_svd.py 58 39 33% 125-129, 146-147, 164-208, 223-225, 242-243, 246 /home/admin/.local/lib/python3.8/site-packages/sklearn/exceptions.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/isotonic.py 109 84 23% 56-76, 117-131, 222-225, 228-231, 237-247, 252-295, 325-342, 361-385, 400, 404-407, 414-416, 419 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py 207 160 23% 81-101, 124-179, 197-207, 218-221, 238, 245-249, 252, 282-293, 309-314, 323-330, 353-357, 385-388, 485-489, 514-575, 588-642 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_bayes.py 203 180 11% 162-174, 197-300, 324-332, 343-356, 361-386, 515-526, 546-633, 641-650, 656-661, 685-694 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py 492 409 17% 58-75, 124-168, 311, 440-551, 706-717, 751-874, 879, 893-898, 1031, 1085-1146, 1157-1171, 1200-1356, 1518, 1526, 1529, 1532, 1725-1740, 1743, 1746, 1749, 1880-1889, 1913-1958, 1961, 2078-2087, 2263-2276, 2279, 2282, 2285, 2444, 2452, 2455, 2458 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/glm.py 158 122 23% 32-35, 40-48, 133-141, 161-298, 313-317, 333-335, 371-376, 380-388, 458, 465, 469-470, 540, 547, 551-552, 654, 664-666, 670-673 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/link.py 40 13 68% 68, 71, 74, 77, 84, 87, 90, 93, 100, 103, 106, 109-110 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_huber.py 88 74 16% 52-122, 229-234, 255-307 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_least_angle.py 435 384 12% 166-171, 301, 442-798, 917-926, 930-936, 940-994, 1017-1035, 1177-1188, 1195-1197, 1283-1310, 1438-1442, 1449, 1467-1529, 1685-1695, 1823-1832, 1835, 1858-1904 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py 546 500 8% 75-82, 114-133, 162-169, 202-246, 286-302, 343-355, 396-428, 432-458, 462-475, 632-819, 957-1009, 1261-1275, 1306-1435, 1463-1478, 1499, 1751-1767, 1789-2062, 2085-2088, 2091 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_omp.py 273 245 10% 72-138, 194-264, 349-408, 490-544, 632-636, 655-687, 735-764, 870-876, 894-919 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_passive_aggressive.py 34 20 41% 173-191, 216-228, 254-256, 401-418, 435-437, 464-466 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_perceptron.py 6 1 83% 164 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ransac.py 153 133 13% 47-54, 215-226, 256-464, 480-482, 502-504, 507 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py 614 520 15% 41-114, 118-132, 137-156, 161-217, 221-228, 232-235, 366, 385-518, 527-534, 539-600, 737, 762, 895-899, 924-946, 950, 954-966, 982-984, 995-999, 1002-1003, 1008, 1014, 1025-1029, 1032-1040, 1043-1050, 1057, 1060, 1070, 1073, 1122-1130, 1135, 1140-1143, 1176-1192, 1223-1236, 1261-1276, 1281-1290, 1297-1313, 1319-1338, 1347-1355, 1370-1385, 1394-1397, 1402-1412, 1421-1433, 1454-1581, 1590-1597, 1627-1665, 1917-1920, 1943-1959, 1963, 1966 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_sag.py 75 63 16% 67-85, 234-344 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py 446 345 23% 56-62, 65-68, 80-102, 117-119, 127-156, 160-168, 171-174, 178-182, 187-241, 258-281, 285-288, 298, 307, 315, 323, 331-355, 413-450, 478-488, 494-535, 539-578, 583-605, 616-649, 684-695, 729, 975, 986-987, 1027-1028, 1031-1069, 1096-1097, 1100, 1103, 1129, 1141-1166, 1192-1193, 1201-1225, 1252, 1270-1276, 1290, 1294-1363, 1582, 1593 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_theil_sen.py 113 89 21% 57-74, 112-128, 147, 178-193, 298-306, 309-343, 359-400 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_isomap.py 54 38 30% 131-141, 144-169, 190-193, 211-212, 229-230, 254-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_locally_linear.py 225 201 11% 49-73, 108-114, 159-189, 292-521, 644-655, 658-665, 688-689, 705-706, 725-734 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_mds.py 111 89 20% 71-132, 234-276, 379-387, 390, 398, 418-419, 439-461 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_spectral_embedding.py 179 148 17% 52-72, 89-95, 120-141, 221-360, 463-469, 472, 481, 503-536, 560-579, 601-602 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py 258 224 13% 58-64, 94-121, 163-193, 255-272, 345-401, 453-477, 664-679, 684-841, 854-910, 932-934, 950-951 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/__init__.py 78 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_base.py 78 71 9% 67-131, 175-202, 234-251 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py 511 452 12% 48-52, 83-128, 132-137, 202-210, 296-355, 456-557, 618-639, 758-785, 852-875, 935-946, 1068, 1192-1200, 1214-1247, 1251-1261, 1269-1299, 1458-1540, 1653-1660, 1771-1778, 1846-1858, 1966-2060, 2135-2152, 2225-2281, 2365-2403, 2477-2506 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/base.py 37 33 11% 26-45, 81-114 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/confusion_matrix.py 61 47 23% 71-72, 107-162, 255-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/det_curve.py 44 36 18% 65-68, 88-129, 210-229 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/precision_recall_curve.py 47 35 26% 77-81, 107-140, 203-225 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/roc_curve.py 47 35 26% 73-77, 100-132, 210-230 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_ranking.py 336 292 13% 83-106, 199-224, 294-317, 326-349, 522-547, 595-648, 688-730, 811-823, 913-956, 1005-1046, 1090-1106, 1149-1191, 1238-1250, 1290-1299, 1303-1307, 1407-1411, 1458-1466, 1564-1569, 1646-1717 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_regression.py 168 137 18% 88-122, 182-194, 257-271, 335-351, 408-416, 477-492, 552-584, 676-723, 753-756, 808-821, 857, 896 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py 226 132 42% 52-60, 77, 81-92, 107-122, 133-134, 155-166, 169-171, 199, 204, 236-242, 276-288, 291, 326-362, 365, 383-392, 397, 426-459, 485-530, 614 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/__init__.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_bicluster.py 32 22 31% 12-17, 22-28, 38-45, 80-86 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_supervised.py 170 139 18% 43-69, 74-83, 127-149, 214-229, 289-299, 383-389, 453-473, 542, 611, 710, 768-798, 889-919, 998-1020, 1091-1100, 1115-1123 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_unsupervised.py 93 76 18% 33-34, 109-117, 135-149, 214-248, 281-298, 339-363 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/pairwise.py 412 339 18% 45-61, 135-164, 194-198, 272-323, 399-439, 451-508, 512-514, 587-601, 670-673, 722-723, 782-804, 833-841, 861-862, 880-886, 910-911, 967-978, 1004-1005, 1033-1041, 1067-1075, 1101-1108, 1136-1142, 1180-1191, 1241-1251, 1296-1298, 1342, 1347, 1354-1373, 1379-1405, 1422-1434, 1443-1470, 1594-1635, 1747-1790, 1845, 1937-1954 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/__init__.py 32 1 97% 37 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py 342 251 27% 96-116, 127-136, 141-142, 160-184, 244-265, 268, 274-306, 310-314, 380-386, 390-406, 421-429, 433, 437, 450, 473-489, 510-511, 514-523, 539-540, 556-557, 573-574, 590-591, 607-608, 624-625, 631-639, 643-644, 704, 708-721, 747-892, 896-968, 1278-1284, 1288, 1608-1611, 1619 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_split.py 467 353 24% 78-83, 92-95, 99, 106, 155-161, 183-185, 235, 238-245, 262-264, 273-298, 324-333, 354, 428, 432-444, 499, 502-536, 562, 636, 640-690, 693-695, 731-732, 831-834, 859-887, 934-944, 968-971, 997, 1057, 1060-1075, 1099-1102, 1128, 1156-1169, 1195-1202, 1226-1229, 1232, 1284, 1340, 1350-1354, 1386-1388, 1413, 1416, 1484-1489, 1492-1503, 1575-1580, 1583-1594, 1626, 1691-1696, 1699-1757, 1793-1794, 1803-1864, 1906-1909, 1933-1937, 1941-1945, 1966, 1972, 1993, 2017-2018, 2058-2073, 2168-2199, 2211-2241, 2250-2252 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py 380 335 12% 231-279, 288-304, 309-313, 438-446, 543-657, 666-709, 838-892, 937-964, 980-1021, 1039-1045, 1165-1182, 1189-1197, 1202-1209, 1353-1417, 1444-1476, 1483-1518, 1625-1645, 1671 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/__init__.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_base.py 443 394 11% 61-66, 86-111, 135-139, 160-197, 223-248, 274-296, 307-315, 318-358, 361-525, 529, 538, 547, 581-594, 649-765, 816-846, 855, 889-901, 978-1083, 1139-1170 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_classification.py 158 136 14% 153-159, 179, 195-221, 239-275, 414-421, 441-487, 504-529, 548-616 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_graph.py 60 35 42% 16-21, 29-36, 106-113, 187-194, 307-311, 327, 345-347, 371, 374, 490-494, 510, 528-529, 553, 556 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_kde.py 86 66 23% 101-119, 124-139, 164-179, 197-211, 233, 256-283, 286 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_lof.py 80 54 32% 184-190, 218-223, 246, 265-299, 320-327, 346-356, 384-393, 421, 450-458, 486-498, 521-526 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nca.py 157 129 18% 169-176, 197-244, 265-268, 304-377, 400-443, 453-456, 483-524, 527 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nearest_centroid.py 70 54 23% 91-92, 107-181, 202-205 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_regression.py 62 40 35% 152-157, 161, 170, 190, 206-229, 352-358, 378, 395-426 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_unsupervised.py 10 2 80% 118, 142 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/__init__.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_data.py 810 681 16% 71-80, 161-217, 323-325, 335-341, 362-363, 386-417, 432-441, 456-463, 466, 545-561, 683-685, 695-699, 726-727, 762-860, 877-897, 914-937, 940, 1007, 1017-1020, 1040-1041, 1065-1085, 1100-1110, 1125-1134, 1137, 1198-1215, 1319-1323, 1344-1387, 1402-1416, 1431-1444, 1447, 1536-1555, 1632-1635, 1639-1641, 1646-1651, 1668-1681, 1701-1708, 1739-1837, 1891-1937, 2001-2002, 2023-2024, 2043-2045, 2048, 2083-2098, 2157-2158, 2179-2180, 2200-2205, 2208, 2253, 2272-2282, 2299-2310, 2313, 2321, 2350-2378, 2484-2489, 2499-2519, 2531-2568, 2589-2625, 2630-2694, 2699-2717, 2737-2750, 2768-2771, 2789-2793, 2796, 2920-2931, 3020-3022, 3043-3044, 3047, 3050-3077, 3092-3106, 3139-3152, 3158-3163, 3169-3185, 3192-3207, 3217-3219, 3228-3243, 3267-3290, 3293, 3394-3395 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_discretization.py 117 102 13% 131-134, 153-237, 242-271, 288-318, 337-353 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py 272 241 11% 42-67, 70-74, 77-110, 113-156, 159, 318-322, 325-333, 339-397, 416-420, 442-443, 459-505, 524-600, 617-635, 721-724, 743-771, 787-793, 809-844 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_function_transformer.py 41 26 37% 11, 91-97, 100-102, 106-109, 128-132, 147, 162, 166-171, 174 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_label.py 274 229 16% 100-102, 116-118, 132-138, 152-163, 166, 262-274, 289-298, 321, 343-350, 387-403, 406, 471-569, 577-613, 619-657, 725-726, 742-754, 773-798, 816-824, 827-831, 847-864, 881-898, 902 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_base.py 362 291 20% 39-60, 81-104, 108, 117, 152-240, 249-250, 253-255, 262-287, 291-323, 342-344, 347-361, 370-378, 393-400, 417-430, 433-440, 449-457, 471-496, 500-514, 517, 521-532, 542-544, 552-564, 592-595, 614-625, 632-636, 666-667, 670-676, 706-707, 710, 713-727, 730-738, 752-764, 768, 772, 791-830, 930-995 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_bounds.py 21 14 33% 54-74 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_classes.py 124 65 48% 187-198, 224-246, 249, 382-391, 417-432, 435, 657, 667, 877, 887, 1042, 1054, 1062, 1065, 1211, 1218, 1346, 1376-1379, 1396-1397, 1412, 1431-1432, 1440, 1448, 1451 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_classes.py 291 224 23% 103-115, 128-129, 139-140, 145-397, 401-411, 436-463, 489-491, 515-516, 520-539, 576-578, 598-600, 846, 898-903, 929-951, 970-979, 1197, 1247-1252, 1271-1277, 1511, 1732 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_export.py 377 339 10% 44-70, 75, 181-194, 203-214, 218-237, 241-262, 266-366, 376-406, 412-427, 431-436, 439-463, 466-524, 534-560, 565-574, 577-625, 628-662, 769-795, 802-815, 876-972 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_reingold_tilford.py 131 110 16% 9-22, 25, 28, 31-38, 41-44, 48, 51, 54-56, 60-64, 68-70, 74-95, 99-132, 136-144, 148-154, 162-165, 169-178, 183-188 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/__init__.py 366 299 18% 84, 87, 90, 93-96, 107, 125-132, 165-167, 172-179, 184-193, 198-205, 224-268, 312-346, 355-409, 502-563, 631, 651-661, 668-672, 708-722, 755-768, 778-783, 817-819, 845-851, 868-878, 898-903, 933-944, 1019-1045, 1059-1062, 1080-1084, 1113-1182 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_arpack.py 5 3 40% 28-30 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_encode.py 115 99 14% 30-50, 60-65, 84-102, 108-112, 115-117, 122-123, 128-144, 176-187, 215-269 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_estimator_html_repr.py 76 62 18% 39-50, 53, 61-76, 82-102, 111-143, 303-311 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_joblib.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_mask.py 20 14 30% 9-21, 41-54 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_pprint.py 243 172 29% 79, 99, 101, 105, 185-199, 203, 207, 222-268, 276-317, 323-332, 354-379, 383-413, 418, 420, 427, 441, 446-447 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_show_versions.py 33 26 21% 24-32, 44-73, 82-93 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_tags.py 16 13 19% 50-67 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/class_weight.py 61 55 10% 41-72, 115-181 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/deprecation.py 56 11 80% 67-68, 86-87, 101-102, 117-123 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/extmath.py 223 184 17% 41-46, 69-78, 92-95, 111-115, 135-157, 211-242, 327-374, 424-448, 488-501, 543-546, 579-592, 619-626, 649-657, 687, 752-789, 841-867, 886-889, 907-914 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/fixes.py 82 53 35% 29-31, 44, 48, 57-60, 84-108, 171-201, 207-210, 216-218, 221-222 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/graph.py 22 17 23% 54-71 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/metaestimators.py 92 63 32% 26, 29-38, 43-55, 59-64, 67-76, 105-123, 141, 143, 199-218 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/multiclass.py 148 127 14% 24-27, 31, 74-106, 110, 141-165, 180-183, 250-309, 326-344, 370-420, 442-464 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/optimize.py 84 74 12% 40-52, 80-111, 161-204, 231-257 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/random.py 39 32 18% 40-94 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/sparsefuncs.py 206 178 14% 19-21, 25-26, 45-46, 63-64, 103-120, 186-217, 237-242, 259-264, 283-294, 313-346, 369-374, 393-402, 406-413, 417-436, 440-455, 459, 464, 494-500, 519-549, 558-568, 574-578, 596-610 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/stats.py 23 19 17% 33-61 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/validation.py 397 261 34% 66-74, 80, 89, 99-103, 110-111, 124, 163-177, 182, 192, 195-198, 202, 209-212, 238, 241-242, 259-262, 277-283, 298-300, 348-394, 400, 511-540, 545, 552, 560, 563, 568, 578-584, 592-593, 610-614, 617-618, 630, 637, 645-655, 659, 669, 677, 683, 691-702, 811-833, 852-864, 880-886, 914, 948-974, 1024, 1030, 1033-1035, 1041, 1057-1068, 1102-1110, 1194-1273, 1304-1326, 1351-1361, 1384-1400 /home/admin/.local/lib/python3.8/site-packages/tensorboard/__init__.py 22 7 68% 71-73, 78-80, 85-87, 98 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/__init__.py 25 15 40% 46-57, 71-76 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/allocation_description_pb2.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/attr_value_pb2.py 63 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/cluster_pb2.py 24 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/config_pb2.py 124 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/cost_graph_pb2.py 33 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/debug_pb2.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/event_pb2.py 91 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/function_pb2.py 65 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/graph_pb2.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/meta_graph_pb2.py 129 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/node_def_pb2.py 25 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/op_def_pb2.py 38 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/resource_handle_pb2.py 22 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/rewriter_config_pb2.py 62 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/saved_object_graph_pb2.py 100 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/saver_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/step_stats_pb2.py 56 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/struct_pb2.py 100 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/summary_pb2.py 70 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_description_pb2.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_pb2.py 36 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_shape_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/trackable_object_graph_pb2.py 30 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/types_pb2.py 62 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/variable_pb2.py 38 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/verifier_config_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/versions_pb2.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/__init__.py 22 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/app.py 62 40 35% 34-60, 70, 75-79, 93, 96-98, 106-110, 118-127 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/compat/__init__.py 35 17 51% 55-60, 78-83, 90, 109-112, 126-128 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/compat/v1/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py 227 94 59% 85, 93, 98-101, 106-109, 114-120, 124, 134, 139, 144, 154, 162, 167, 179-182, 191-209, 218-236, 250-255, 276-277, 284-290, 294, 299, 302, 305, 308, 314, 318-323, 658-694 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/error_codes.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/errors.py 139 64 54% 45-49, 54, 70, 75, 80, 83-143, 204, 222, 242, 258, 278, 295, 314, 330, 347, 366, 386, 404, 424, 441, 455, 473, 502, 507, 511-516, 528, 531 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/flags.py 58 23 60% 49-60, 79, 82-87, 90, 93, 96, 99, 102, 105, 108, 111, 114 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/io/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/io/gfile.py 363 285 21% 43-44, 49, 68, 74-82, 94, 98, 120-137, 148, 158, 161-164, 168-175, 186, 190-195, 199-202, 210-214, 222, 227-233, 237-242, 246, 268-315, 325-333, 338-361, 365-372, 376-390, 394-402, 408-417, 429-447, 450, 453-456, 459, 462-465, 477-510, 519-543, 546-569, 572, 575-589, 592-597, 613, 628, 640, 658, 673, 693-723, 738, 752-753, 772-776 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/pywrap_tensorflow.py 98 69 30% 35, 43-44, 48, 136-145, 157, 168, 175-203, 207-253, 270-278, 281, 288 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/tensorflow_stub/tensor_shape.py 320 236 26% 30-42, 45, 48-49, 54-60, 64-70, 73, 78, 82, 87, 101-102, 119-120, 149-154, 174-178, 189, 209-213, 224-228, 248-256, 267, 287-294, 305-309, 325, 345-352, 363-367, 388-392, 413-417, 438-442, 464-468, 471, 487-490, 526, 528, 533-536, 542, 546-548, 555, 558-563, 569, 573-574, 579-584, 589-593, 597, 605-608, 626-652, 657-663, 682-693, 714-718, 731-734, 748-749, 766-769, 785-790, 806-811, 850-857, 872-873, 898-910, 914, 925-926, 937-941, 945-948, 959-963, 967-977, 980, 985-988, 1000-1003, 1011, 1023, 1036 /home/admin/.local/lib/python3.8/site-packages/tensorboard/errors.py 27 10 63% 38, 54-55, 71-72, 88-89, 93-96 /home/admin/.local/lib/python3.8/site-packages/tensorboard/lazy.py 45 19 58% 47-58, 68, 71, 74-76, 97-101 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/audio/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/audio/metadata.py 23 10 57% 43-53, 66-79 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/audio/plugin_data_pb2.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/audio/summary.py 54 42 22% 96-128, 187-230 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/audio/summary_v2.py 31 22 29% 77-126 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/custom_scalar/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/custom_scalar/layout_pb2.py 41 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/custom_scalar/metadata.py 9 1 89% 35 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/custom_scalar/summary.py 19 11 42% 42-46, 68-83 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/histogram/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/histogram/metadata.py 23 11 52% 40-41, 60-77 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/histogram/plugin_data_pb2.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/histogram/summary.py 83 71 14% 58-112, 145-154, 184-234 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/histogram/summary_v2.py 86 72 16% 70-107, 121-174, 195-232 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/image/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/image/metadata.py 22 10 55% 40-48, 61-74 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/image/plugin_data_pb2.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/image/summary.py 38 27 29% 72-102, 137-164 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/image/summary_v2.py 23 15 35% 60-104 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/pr_curve/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/pr_curve/metadata.py 28 10 64% 53-57, 76-89 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/pr_curve/plugin_data_pb2.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/pr_curve/summary.py 92 78 15% 86-167, 211-244, 309-366, 428-441, 496-523, 550-574 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/scalar/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/scalar/metadata.py 22 10 55% 40-48, 61-74 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/scalar/plugin_data_pb2.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/scalar/summary.py 32 21 34% 55-64, 89-114 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/scalar/summary_v2.py 26 15 42% 54-64, 87-100 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/text/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/text/metadata.py 22 10 55% 40-48, 60-73 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/text/plugin_data_pb2.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/text/summary.py 29 19 34% 61-70, 98-119 /home/admin/.local/lib/python3.8/site-packages/tensorboard/plugins/text/summary_v2.py 24 13 46% 50-60, 81-90 /home/admin/.local/lib/python3.8/site-packages/tensorboard/summary/__init__.py 14 4 71% 26-27, 33-34 /home/admin/.local/lib/python3.8/site-packages/tensorboard/summary/_tf/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/summary/_tf/summary/__init__.py 32 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/summary/v1.py 29 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/summary/v2.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/util/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/util/encoder.py 41 18 56% 50-55, 58-64, 91-99, 107-117 /home/admin/.local/lib/python3.8/site-packages/tensorboard/util/lazy_tensor_creator.py 39 28 28% 50-55, 58-67, 71-82, 103-107 /home/admin/.local/lib/python3.8/site-packages/tensorboard/util/op_evaluator.py 28 14 50% 73-83, 90, 103, 106-108 /home/admin/.local/lib/python3.8/site-packages/tensorboard/util/tb_logging.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/util/tensor_util.py 238 193 19% 28, 32, 38, 44, 50, 54, 58, 62, 66, 70, 74, 78, 84, 90, 94, 128-134, 142-144, 149-154, 158-163, 188, 191, 195-198, 202-205, 209-219, 223-225, 233-235, 239-241, 245-250, 254-256, 283-288, 337-498, 515-621 /home/admin/.local/lib/python3.8/site-packages/tensorboard/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/__init__.py 45 5 89% 29, 50-59, 76 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/compressed_module_resolver.py 39 12 69% 39, 48, 54, 61-64, 75, 82, 85-91 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/config.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/estimator.py 50 37 26% 53-58, 108-113, 118, 149-171, 192-217 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/feature_column.py 222 136 39% 37, 89, 103-130, 140-142, 146, 151, 156-159, 166-168, 172, 175, 180, 185, 190, 195, 198-199, 203-207, 211-213, 216-222, 226-227, 276-278, 294-326, 337-340, 344, 349, 354-357, 362-363, 367, 370, 375, 380-385, 390, 395, 398, 401-404, 407-409, 412-418, 422-423, 483-487, 504, 509, 514-517, 521, 524, 529, 534, 539, 544, 547-557, 565-567, 572-573 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/feature_column_v2.py 58 32 45% 37-49, 73-80, 95, 100, 104, 109-112, 119-124, 127, 132, 137, 141-144, 147-148, 152-153 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/file_utils.py 36 29 19% 30-42, 47-57, 65-80 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/image_module_info_pb2.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/image_util.py 44 35 20% 40, 54, 77-101, 125-142 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py 172 105 39% 148, 158-160, 164, 167, 184, 194, 212-252, 256-285, 291-307, 310-319, 323-369, 374, 381-392, 398-401, 406-412, 418, 427 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/meta_graph_lib.py 59 49 17% 29-39, 44-49, 66-75, 93-113, 118-124, 128-131 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module.py 156 127 19% 27-32, 64-65, 70-79, 156-176, 249-270, 274, 290, 306, 310, 327-333, 357, 362-368, 389-404, 415-417, 438-456, 480-481, 500-505, 519-530, 574-628 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_attachment_pb2.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_def_pb2.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_impl.py 17 6 65% 46, 50-52, 57, 62 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_spec.py 39 20 49% 40, 74-78, 83, 88, 109, 129, 162-171, 185, 200 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_v2.py 20 5 75% 91, 96, 99-103 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/native_module.py 440 365 17% 50-54, 58-82, 124, 127-131, 134-136, 139-151, 212-233, 262-278, 325-328, 348-354, 359, 362-363, 366-367, 370-371, 374-380, 383, 386-387, 402-411, 428-451, 454-468, 483-532, 536-632, 636-643, 648, 653-654, 669-670, 676-688, 693-707, 719, 727-732, 750-759, 776-816, 821-826, 831-835, 841, 854-863, 921-922, 945-947, 951-959, 963-967, 983-1017, 1036-1089, 1094-1111, 1116-1128, 1133-1153, 1158-1173, 1177-1178 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/registry.py 21 5 76% 22-23, 41, 52-54 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/resolver.py 199 100 50% 132-135, 145-156, 164-174, 178, 190-199, 204, 209, 219-227, 249-252, 257-258, 268-273, 278-283, 302-335, 380-426, 436-437, 442-445, 462, 476, 485, 488-490, 506-510, 514, 518-521 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/saved_model_lib.py 205 160 22% 53, 60, 66-75, 79, 86-91, 107-119, 123-126, 133-138, 148, 155-161, 180-192, 197-198, 203-208, 223-240, 263-321, 359, 363-372, 375, 379-382, 386, 390, 394, 405-411, 415-423, 426-429, 432-437, 440-443, 451-458, 463-467 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/saved_model_module.py 16 9 44% 46-49, 86-93 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tensor_info.py 134 107 20% 35-41, 45, 50-56, 61-67, 75, 80, 85, 89, 93-97, 102-115, 132, 139-144, 161-185, 202-206, 223-243, 262-281, 298-307 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tf_utils.py 89 60 33% 32-34, 68-70, 98-115, 132-133, 150-165, 182-195, 227, 232-236, 241-249, 253-258 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tools/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/uncompressed_module_resolver.py 37 24 35% 32-34, 37, 41-47, 65-77, 80-87 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/_VF.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/__config__.py 7 3 57% 9, 16, 20 /home/admin/.local/lib/python3.8/site-packages/torch/__future__.py 5 1 80% 16 /home/admin/.local/lib/python3.8/site-packages/torch/__init__.py 362 162 55% 20, 27, 59-142, 148, 175-188, 207, 214-231, 262-278, 298, 307, 329-331, 377, 502, 508, 515, 533-555, 565-571, 577, 622, 634, 640, 664, 669, 674, 679, 684, 689, 694, 699, 704, 709, 714, 719, 724, 729, 734, 739, 744, 768, 772, 789, 829-833, 896, 949-954 /home/admin/.local/lib/python3.8/site-packages/torch/_classes.py 25 11 56% 6-7, 10-13, 22-24, 28, 48 /home/admin/.local/lib/python3.8/site-packages/torch/_jit_internal.py 591 404 32% 35, 41-43, 59-111, 141-161, 168-174, 227-241, 247-258, 275-289, 297-311, 331-381, 391-404, 414-423, 426-427, 435, 550-556, 564, 567, 639-660, 668-674, 681-684, 688-689, 693, 696, 700-704, 707-710, 738-739, 748-752, 760, 763-766, 780, 783, 832, 840-855, 859-865, 870-875, 880-885, 890-893, 898-910, 913-919, 926-932, 935-940, 943, 990, 993, 998-1001, 1005, 1011, 1016, 1027-1028, 1035, 1042, 1049-1051, 1055-1070, 1075-1080, 1084-1087, 1091-1096, 1099-1101, 1104-1110, 1118, 1122, 1126-1133, 1137-1138, 1148-1206, 1210-1229, 1234-1235, 1238-1257, 1267-1270 /home/admin/.local/lib/python3.8/site-packages/torch/_linalg_utils.py 49 35 29% 13-19, 26-29, 38-42, 50-52, 58-59, 65, 71, 77, 83-88, 94-101, 106 /home/admin/.local/lib/python3.8/site-packages/torch/_lobpcg.py 424 387 9% 18-32, 46-72, 100-103, 112-121, 130-137, 142-249, 253-258, 284-297, 301-337, 500-534, 557-647, 667-686, 689-705, 710-728, 733-734, 743-761, 769, 780-790, 801, 809-846, 852-891, 938-945, 973-1010, 1037-1110, 1117 /home/admin/.local/lib/python3.8/site-packages/torch/_lowrank.py 90 80 11% 60-82, 126-130, 135-177, 242-281 /home/admin/.local/lib/python3.8/site-packages/torch/_masked/__init__.py 510 447 12% 13-14, 36, 53-346, 363-400, 406-423, 428-432, 438-441, 469-535, 543-629, 636, 663-679, 715-758, 764-782, 788-795, 806-828, 839-881, 890-897, 906-913, 933-947, 967-981, 997-1003, 1019-1025, 1050-1063, 1086-1102, 1125-1132, 1143-1178, 1197, 1224, 1241-1248, 1257-1264, 1273-1280, 1291-1303 /home/admin/.local/lib/python3.8/site-packages/torch/_masked/_docs.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/_meta_registrations.py 97 62 36% 7-8, 11-16, 21-24, 28-29, 33-37, 41-42, 46, 50, 53-56, 60-61, 66-73, 77-97, 101-105, 109-116, 121-125, 129-130, 134-135, 139-140 /home/admin/.local/lib/python3.8/site-packages/torch/_namedtensor_internals.py 59 46 22% 11-12, 20, 25-33, 37-40, 44, 47-54, 57, 61-62, 69-72, 77-80, 85-95, 128-142 /home/admin/.local/lib/python3.8/site-packages/torch/_ops.py 117 23 80% 42, 45, 48, 51, 54, 58, 62, 66, 83, 86, 92, 96, 101, 112-118, 133-134, 147, 191, 199-202, 248 /home/admin/.local/lib/python3.8/site-packages/torch/_prims/__init__.py 610 380 38% 177-180, 184-189, 192-194, 219-220, 253-295, 329, 408, 591, 639-641, 645, 665, 678, 705, 718, 737-738, 745-750, 764-769, 781, 851-863, 869, 890-925, 929-938, 947, 974-1026, 1030-1036, 1041-1052, 1086-1098, 1111-1185, 1194-1200, 1231-1274, 1284-1292, 1309-1333, 1337-1340, 1361-1376, 1380-1385, 1404-1420, 1424, 1445, 1449, 1474-1479, 1484-1508, 1518, 1537-1549, 1553, 1570-1571, 1594-1598, 1604, 1630-1631, 1635, 1653-1658, 1663-1670, 1674-1675, 1695-1698, 1702, 1719-1720, 1724, 1745-1762, 1766, 1786, 1790, 1814-1818, 1827, 1907-1908, 1914, 1934-1939, 1945, 1971-1972, 1984, 2011-2015, 2027 /home/admin/.local/lib/python3.8/site-packages/torch/_prims/utils.py 450 367 18% 26, 33, 67-111, 121-137, 147, 151, 155, 173-201, 211-216, 226-239, 255-322, 336-337, 345-347, 355-357, 366-369, 373-374, 383-385, 392-408, 414-417, 425-431, 440, 444, 456-484, 498-519, 528-529, 533-534, 538-539, 543-544, 562, 566, 574-585, 600, 613-625, 641-691, 701-717, 721-727, 742-791, 803, 906-993, 1005-1009, 1013-1027, 1033-1043, 1047-1048, 1052-1056, 1067-1083 /home/admin/.local/lib/python3.8/site-packages/torch/_six.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/_sources.py 59 7 88% 22-28, 85, 102 /home/admin/.local/lib/python3.8/site-packages/torch/_storage_docs.py 11 2 82% 17-18 /home/admin/.local/lib/python3.8/site-packages/torch/_tensor.py 474 346 27% 28-34, 39-44, 47-74, 87, 89, 92, 101-103, 114-136, 143, 151, 153, 155, 157, 160-168, 176-194, 203, 208-312, 315-331, 334-338, 387-396, 427-438, 441-444, 503-505, 513-516, 520-525, 529-531, 534-535, 540-547, 559-565, 574-580, 584-588, 591-595, 600-611, 618-623, 630-635, 639, 643, 653, 656-660, 664-665, 669-674, 678-683, 687, 691, 695, 702-711, 723, 725, 732-734, 737-748, 754-759, 764-769, 778-784, 796-850, 858-861, 902-905, 943-948, 983-992, 998-1006, 1042-1046, 1059, 1062-1069, 1081, 1087-1090, 1094-1097, 1114-1125, 1145-1170, 1174-1188, 1193-1203 /home/admin/.local/lib/python3.8/site-packages/torch/_tensor_docs.py 543 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/_tensor_str.py 301 276 8% 56-81, 84-85, 89-146, 149, 152-163, 167-176, 180-209, 215-234, 237-271, 274-286, 290-304, 307-477, 480-481 /home/admin/.local/lib/python3.8/site-packages/torch/_torch_docs.py 416 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/_utils.py 257 185 28% 26-45, 62-78, 82-89, 162-171, 178-184, 187-193, 197-199, 207, 211, 219-242, 245-251, 255-259, 267-275, 292, 306-308, 324, 342-348, 366-370, 385-402, 408-412, 426, 434-438, 444-461, 465-468, 472-476, 481, 486, 491, 499-501, 520, 523-525, 529-540, 550, 558, 561, 564-568, 575-577 /home/admin/.local/lib/python3.8/site-packages/torch/_utils_internal.py 24 6 75% 15, 18, 27, 31-33 /home/admin/.local/lib/python3.8/site-packages/torch/_vmap_internals.py 85 67 21% 15-21, 24-26, 31-35, 41-85, 92-108, 116-124, 128-132, 138-144, 250-257, 265-278 /home/admin/.local/lib/python3.8/site-packages/torch/amp/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/amp/autocast_mode.py 100 89 11% 9-14, 180-222, 225-248, 251-271, 274-276 /home/admin/.local/lib/python3.8/site-packages/torch/ao/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/dynamic/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/dynamic/linear.py 77 54 30% 20-36, 39, 42, 47, 50, 53-54, 58-73, 78, 81, 84, 88-89, 97-136 /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/linear.py 128 92 28% 11-22, 25, 30-36, 42, 45, 48-50, 54-61, 66-67, 71-73, 76, 87-105, 109, 112, 117, 120, 123-125, 129-141, 146, 149, 152, 156-157, 167-207 /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/utils.py 25 12 52% 4, 21-26, 29, 32-34, 38 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/__init__.py 14 2 86% 20-21 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fake_quantize.py 172 100 42% 22, 25, 28, 31, 48-53, 57, 61, 65, 69, 73, 77, 118-152, 156, 159-178, 182, 192-194, 200-224, 235-241, 246, 250, 280-285, 289, 293, 309, 445-451, 461-462, 472-473, 483-484, 494-495 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fuse_modules.py 61 47 23% 16-20, 24-30, 45-67, 70-82, 85-95, 151, 162 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fuser_method_mappings.py 96 68 29% 26-45, 63-91, 108-119, 135-141, 149, 175-181, 190, 195, 200-201, 244-252, 260-270 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/observer.py 535 396 26% 26-29, 32, 35, 38-40, 81-82, 102-103, 107, 111, 172-202, 216-223, 250-253, 272-327, 331, 422-439, 446-455, 460, 464, 469-470, 528-529, 540-554, 597-615, 620, 623-648, 652, 655, 667-703, 724, 737-738, 781-791, 794-817, 862-877, 890-893, 900-943, 953-1006, 1017-1032, 1045-1068, 1071-1127, 1131-1147, 1150-1154, 1166-1187, 1220-1226, 1229, 1233, 1253-1260, 1263, 1267, 1284-1285, 1288-1289, 1293, 1297, 1315-1317, 1320, 1324, 1340, 1343, 1347, 1351-1356, 1360, 1368-1374, 1382-1393, 1402-1422 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/qconfig.py 141 96 32% 66, 89-93, 197-213, 270-315, 339, 364-372, 375-383, 390-405, 427-451, 458-482, 489-495, 498 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quant_type.py 9 2 78% 13-19 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantization_mappings.py 83 38 54% 177-181, 186, 191, 196-199, 204, 213-222, 230-237, 242, 249-252, 257, 262, 268-274, 280-289, 293, 299-302, 311, 314 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantize.py 251 219 13% 32, 56-72, 92-96, 101, 106, 109-116, 135-203, 206, 224-229, 262-285, 290-306, 316-322, 341-350, 382-435, 452-463, 478-485, 516-524, 542-564, 577-614, 624-631 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantize_jit.py 82 64 22% 8-9, 12-13, 20, 29, 38-45, 48-63, 66-67, 70-71, 75-94, 97-98, 101-102, 107-119, 171-172, 212-213 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/stubs.py 30 17 43% 13-15, 18, 30-32, 35, 54-59, 62-64 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/utils.py 147 113 23% 100-103, 106-108, 111, 115, 123, 126-151, 165-170, 173-175, 178-180, 186, 193-195, 202, 208, 214, 220, 226-228, 239-243, 246-264, 271-295, 304-348, 355-359, 367-372 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/__init__.py 88 60 32% 32-74, 78-82, 150-173, 238-276, 295, 299, 308, 324-326 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/anomaly_mode.py 20 8 60% 74-75, 80, 83, 102-103, 106, 109 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/forward_ad.py 42 25 40% 21-26, 37-43, 68-75, 103-111, 147, 150, 153 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/function.py 191 119 38% 58, 104-109, 144, 147, 182, 226, 235-239, 246-253, 257, 308-309, 315, 340, 366, 391, 398-434, 449-450, 456-457, 462-475, 485-487, 493-511, 523-537, 558-563, 566-571, 574-576, 581-585, 588-589, 593-594, 597, 600, 603, 606 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/functional.py 329 308 6% 9-14, 19-36, 44-53, 63-75, 81-87, 92-103, 109-135, 142-159, 168-203, 266-293, 357-394, 416-425, 429-481, 559-694, 782-810, 868-901, 968-1006 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/grad_mode.py 83 36 57% 22, 32-74, 77, 80, 178-179, 182, 232, 235, 238, 284-289, 292, 295, 298 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/gradcheck.py 759 681 10% 24, 33-37, 46-51, 56-63, 83-115, 141-153, 173-186, 192-204, 214-228, 235-239, 246-262, 268-273, 286-295, 317-380, 386-395, 400-404, 409-411, 416-422, 426-429, 433-440, 445-465, 471-474, 481-499, 526-554, 558-575, 580-605, 611-613, 621-633, 637-641, 645-678, 682-688, 695-700, 705-712, 756, 769-810, 818-852, 857-886, 889-942, 945-992, 996-1001, 1005, 1010-1017, 1023, 1029-1035, 1042-1052, 1058-1103, 1107-1130, 1134-1135, 1139-1142, 1146-1151, 1157-1171, 1175-1176, 1188-1191, 1213-1237, 1241-1244, 1249-1264, 1269-1280, 1286-1310, 1400-1414, 1420-1448, 1522-1570 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/graph.py 22 14 36% 65-66, 69, 72, 116-132 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/profiler.py 252 205 19% 22-39, 137-176, 181, 191-197, 200-201, 204-205, 208-220, 223-225, 228-230, 233-234, 237-239, 246-250, 254-257, 260-262, 266-268, 276-278, 283-399, 437-443, 446-447, 450-451, 472-479, 564-566, 569-586, 589-593, 602, 608, 611-613, 617-677, 684 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/profiler_legacy.py 152 125 18% 29-49, 52, 64-70, 73, 76-89, 92-94, 97-99, 102-103, 106-108, 115-117, 121-124, 127-129, 133-135, 143-145, 149-284 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/profiler_util.py 501 423 16% 17-24, 27-30, 33, 37-51, 69-117, 120-143, 147, 167, 185-230, 233, 236-249, 266-288, 296-301, 306-312, 316-319, 323-333, 353, 357, 362-363, 366, 379-401, 404-405, 413-416, 425-428, 434-436, 442-444, 450-452, 458-470, 474-481, 485-488, 492, 495, 524-544, 547-578, 581, 584, 605-606, 613-618, 621-624, 628-636, 642-651, 658-663, 675-910 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/variable.py 9 1 89% 7 /home/admin/.local/lib/python3.8/site-packages/torch/backends/__init__.py 30 10 67% 11, 14, 19-24, 35-38 /home/admin/.local/lib/python3.8/site-packages/torch/backends/cuda/__init__.py 68 35 49% 12, 23, 26-28, 38, 49, 69-76, 79, 83, 90-94, 97-101, 143-155 /home/admin/.local/lib/python3.8/site-packages/torch/backends/cudnn/__init__.py 78 49 37% 9-10, 23-45, 50-52, 64, 68-84, 88-100, 105-112 /home/admin/.local/lib/python3.8/site-packages/torch/backends/mkl/__init__.py 3 1 67% 6 /home/admin/.local/lib/python3.8/site-packages/torch/backends/mkldnn/__init__.py 23 10 57% 8, 11-13, 17-23 /home/admin/.local/lib/python3.8/site-packages/torch/backends/mps/__init__.py 7 2 71% 9, 14 /home/admin/.local/lib/python3.8/site-packages/torch/backends/openmp/__init__.py 3 1 67% 6 /home/admin/.local/lib/python3.8/site-packages/torch/backends/quantized/__init__.py 41 18 56% 8-19, 23-24, 28, 31, 35-36, 39 /home/admin/.local/lib/python3.8/site-packages/torch/cpu/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/cpu/amp/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/cpu/amp/autocast_mode.py 22 15 32% 10-15, 18-20, 24-26, 29-31 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/__init__.py 385 167 57% 29-30, 51-53, 56-58, 70, 80, 87-93, 96, 123, 125, 134, 137, 144-146, 156, 163, 165, 188, 202, 207, 211, 213, 224-225, 231-234, 241-242, 251-252, 256-257, 269-270, 273-279, 282-284, 298-299, 312-314, 362, 368-375, 392-399, 403-414, 418-427, 439, 450-452, 459, 464, 467, 472-476, 482-483, 494-496, 508-509, 521-522, 535-536, 542-543, 557-568, 573-574, 589-600, 615-626, 640-643, 654-655, 664, 668, 672, 677, 682, 687, 692, 697, 702, 707, 712, 717, 722, 727, 732 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/_utils.py 21 11 48% 24, 26-30, 33, 38-42 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/amp/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/cuda/amp/autocast_mode.py 75 59 21% 7-8, 20-25, 28-30, 34-36, 39-41, 47-63, 92-119, 132-136 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/amp/common.py 4 1 75% 6 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/amp/grad_scaler.py 217 174 20% 14-16, 19-23, 38, 114-133, 136-139, 142-144, 156-188, 191-228, 262-280, 283-286, 309-342, 364-401, 404, 413-416, 422, 429, 435, 442, 448, 455, 458-461, 467, 485, 498-513, 516-527, 530, 533-541, 544 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/graphs.py 146 123 16% 9-11, 24, 35, 47, 50, 67-70, 81, 87, 93, 101, 136-143, 147-155, 159-160, 227-418 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/jiterator.py 41 33 20% 11-38, 87-117 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/memory.py 180 149 17% 18-19, 24-28, 50-62, 79, 96-106, 120-121, 194-209, 214-217, 236-237, 255-256, 278-282, 304-308, 326, 348, 364, 386, 391-394, 399-402, 415, 436-539, 555-574, 589-592 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/nccl.py 65 49 25% 14-31, 35-39, 43, 47, 51-52, 56-60, 72-93, 97-98, 102-104, 111-113 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/nvtx.py 28 17 39% 5-15, 28, 36, 53, 63, 73, 86-88 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/profiler.py 28 19 32% 18-31, 35, 39, 44-48 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/random.py 74 60 19% 22-31, 37-40, 51-64, 72-73, 88-95, 106-113, 125-130, 138-150, 159-162 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/sparse.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/cuda/streams.py 67 36 46% 9-10, 32-33, 50, 64, 76-79, 86, 94, 98, 101-103, 106, 109, 132-133, 160, 167, 174-176, 187-189, 198, 204, 215, 220, 224, 227-230 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/__init__.py 17 1 94% 21 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/algorithms/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/distributed/algorithms/join.py 112 76 32% 27, 39, 52-53, 66, 75, 84, 103, 177-184, 190-198, 214-225, 228, 244-281, 288-290, 298-300, 328-354 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/autograd/__init__.py 14 4 71% 11, 46-47, 50 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/constants.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py 932 805 14% 53-54, 59-60, 76-84, 113-127, 148-156, 181-185, 226-262, 271-273, 277-278, 289-301, 309-317, 324-327, 334-335, 345-348, 358-359, 371-381, 388, 395, 402, 409, 421, 428-433, 440-447, 451, 467-475, 557-630, 656-770, 789-820, 841-848, 864-867, 890-900, 920-937, 952-962, 982-1006, 1029-1033, 1036-1038, 1043-1049, 1091-1106, 1141-1159, 1182-1201, 1238-1256, 1306-1327, 1363-1384, 1420-1440, 1465-1485, 1533-1554, 1558-1566, 1570-1571, 1574-1582, 1633-1676, 1731-1786, 1842-1900, 1956-2004, 2055-2075, 2115-2141, 2192-2220, 2224-2230, 2255-2286, 2312-2359, 2406-2422, 2441-2459, 2478-2497, 2596-2625, 2718-2742, 2764-2791, 2845-2856, 2868-2873, 2937-3006, 3099-3132, 3210-3242 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/remote_device.py 66 54 18% 25-69, 74-78, 85, 92, 98, 101-114, 117-127, 131 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rendezvous.py 134 108 19% 3-4, 48, 56, 59-91, 94-120, 123, 127-155, 159, 179-188, 196-217, 223-262 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/__init__.py 73 42 42% 25, 115-196, 199-210, 225-240, 244-247 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/_utils.py 26 17 35% 11-32, 35-37 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/api.py 273 198 27% 68-72, 78-83, 99-102, 114-120, 124-136, 140-147, 169-176, 187-265, 280-283, 296-302, 354-375, 378-395, 413-416, 420-425, 429-438, 442-451, 463-473, 485, 488, 619-666, 669-727, 798-800, 891-895, 900-901, 907-931 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/backend_registry.py 157 117 25% 20, 69, 96, 101, 105-123, 133-135, 145, 156-172, 175-232, 240-249, 252-258, 261-265, 268-297, 301-389 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/constants.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/functions.py 7 5 29% 160-165 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/internal.py 116 70 40% 49-50, 55, 59-61, 65, 68-69, 72, 80-82, 88-90, 97-143, 151-180, 188, 192, 203-215, 219-220, 239-245, 264-270 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/options.py 37 25 32% 12-18, 24-35, 39, 87-93, 147-158, 170 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/rpc/server_process_global_profiler.py 31 23 26% 96, 103-124, 142-174 /home/admin/.local/lib/python3.8/site-packages/torch/distributed/utils.py 76 63 17% 18-66, 70-86, 89, 104-113, 130-131, 149-156 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/__init__.py 48 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/distributions/bernoulli.py 81 47 42% 36-49, 52-62, 65, 69, 73-75, 79, 83, 87, 91, 94-96, 99-102, 105, 108-112, 116, 119 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/beta.py 60 32 47% 31-37, 40-45, 49, 53, 57-58, 62, 65-68, 71, 75-79, 83-87, 91, 94 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/binomial.py 83 54 35% 9, 40-51, 54-65, 68, 72, 76, 80, 84, 88, 92, 96, 99-101, 104-117, 120-125, 128-135 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/categorical.py 84 55 35% 50-64, 67-79, 82, 86, 90, 94, 98, 102, 106, 110, 113-117, 120-125, 128-131, 134-139 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/cauchy.py 50 26 48% 32-37, 40-46, 50, 54, 58, 61-63, 66-68, 71-73, 76, 79 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/chi2.py 12 4 67% 22, 25-26, 30 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/constraint_registry.py 89 29 67% 113, 139-144, 158, 163-164, 170-171, 180, 188, 195, 205-212, 218, 223, 229, 235, 240, 248, 256, 263 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/constraints.py 290 122 58% 90, 93, 116-118, 122-124, 133, 135, 139, 143, 202, 206, 209-215, 218, 229, 240-242, 252-254, 257, 260-262, 272-273, 276, 279-281, 295, 298-300, 308, 320, 323-325, 337, 340-342, 350-351, 354, 357-359, 372, 375-377, 390, 393-395, 406, 421, 424, 434-435, 445-449, 460-463, 473, 487-490, 498-501, 509-512, 522-529, 533, 537, 540-547, 557-560, 564, 568-571, 574-576 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/continuous_bernoulli.py 120 81 32% 43-63, 66-77, 80, 83, 87, 93-106, 110-114, 118, 122-127, 131, 135, 139, 142-145, 148-150, 153-156, 159-165, 171-172, 179-181, 185, 189-196 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/dirichlet.py 68 38 44% 10-12, 18-20, 25-26, 48-52, 55-60, 63-65, 68-70, 76, 80-84, 88-89, 92-94, 100, 103 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/distribution.py 113 71 37% 30-32, 35-62, 83, 90, 97, 107, 115, 122, 129, 136, 143, 150-151, 159, 166-167, 177, 187, 197, 221, 230, 239, 251-253, 269-293, 302-306, 309-313 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/exp_family.py 20 11 45% 32, 39, 47, 53-60 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/exponential.py 56 28 50% 29, 33, 37, 41, 44-46, 49-54, 57-62, 65-67, 70-72, 75, 78, 82, 85 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/fishersnedecor.py 63 42 33% 29-37, 40-48, 52-54, 58-60, 64-66, 69-78, 81-89 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/gamma.py 52 27 48% 10, 36, 40, 44, 47-52, 55-61, 64-67, 70-73, 78, 83, 86 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/geometric.py 69 45 35% 34-52, 60-68, 72, 76, 80, 84, 88, 91-100, 103-108, 111 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/gumbel.py 43 20 53% 29-38, 41-44, 48-51, 55, 59, 63, 67, 70 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/half_cauchy.py 44 19 57% 32-33, 37-38, 42, 46, 50, 54, 57-63, 66-68, 71, 74 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/half_normal.py 43 18 58% 32-33, 37-38, 42, 46, 50, 54, 57-61, 64-66, 69, 72 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/independent.py 64 37 42% 37-47, 50-57, 61, 65-67, 71-74, 78, 82, 86, 89, 92, 95-96, 99-100, 103-105, 108 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/kl.py 538 329 39% 67, 69, 84, 87, 90-95, 102-117, 124, 131, 138-141, 163-171, 183-189, 194-201, 208-213, 218-221, 226-229, 235-241, 246-248, 253-264, 269-273, 278-284, 289, 294, 300-305, 310-333, 338-358, 363-384, 390-403, 408-410, 415, 421-427, 432, 437-441, 446-448, 454, 459, 464, 469, 474-478, 485-492, 497-499, 506, 511, 519-522, 527-528, 538, 543-545, 550-555, 562-568, 576, 581, 586-591, 598-604, 614, 621-625, 635, 640-646, 656, 661-667, 672-678, 685, 690-696, 701-708, 715-723, 729, 734-741, 746-747, 754-756, 761-768, 773-778, 785-789, 794-799, 804-807, 813-815 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/kumaraswamy.py 42 21 50% 14-16, 40-48, 51-54, 58, 63-66, 70, 73-76 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/laplace.py 56 32 43% 28, 32, 36, 40, 43-48, 51-57, 60-69, 72-74, 77-79, 82-83, 86 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/lkj_cholesky.py 55 43 22% 56-69, 72-79, 88-100, 112-128 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/log_normal.py 31 10 68% 30-31, 34-35, 39, 43, 47, 51, 55, 58 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/logistic_normal.py 22 8 64% 34-37, 42-43, 47, 51 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/lowrank_multivariate_normal.py 107 77 28% 15-19, 29, 39-43, 83-109, 113-126, 130, 134, 138, 148-154, 158-161, 169-173, 177-181, 185-195, 198-205 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/mixture_same_family.py 96 70 27% 56-87, 92-106, 112, 116, 120, 124-125, 131-137, 140-144, 147-153, 156-176, 179, 182-189, 192-194 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/multinomial.py 75 47 37% 52, 56, 59-66, 69-75, 78, 82, 86, 90, 94, 97-106, 109-119, 122-130 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/multivariate_normal.py 121 92 24% 20, 31-66, 71-75, 120-153, 156-172, 176, 181, 187, 192, 196, 200, 204-206, 209-214, 217-222 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/negative_binomial.py 66 39 41% 28-39, 42-53, 56, 60, 64, 68, 72, 76, 80, 85, 90-92, 95-105 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/normal.py 65 34 48% 34, 38, 42, 46, 49-54, 57-63, 66-68, 71-73, 76-81, 84-86, 89, 92, 96, 99 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/one_hot_categorical.py 71 39 45% 41-44, 47-52, 55, 59, 63, 67, 71, 75-77, 81, 85, 88-92, 95-98, 101, 104-109, 122-124 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/pareto.py 33 15 55% 25-28, 31-34, 39-40, 44, 49-50, 54, 57 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/poisson.py 44 23 48% 32, 36, 40, 43-48, 51-56, 59-61, 64-67, 71, 74 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/relaxed_bernoulli.py 78 45 42% 34-48, 51-62, 65, 69, 73, 77, 80-83, 86-90, 118-119, 124-125, 129, 133, 137 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/relaxed_categorical.py 70 36 49% 38-42, 45-51, 54, 58, 62, 66, 69-73, 76-84, 112-113, 118-119, 123, 127, 131 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/studentT.py 55 34 38% 32-34, 38, 42-46, 49-52, 55-63, 73-77, 80-88, 91-92 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/transformed_distribution.py 106 85 20% 46-79, 82-92, 96-101, 105, 114-118, 127-130, 137-151, 158-163, 170-176, 183-187 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/transforms.py 698 437 37% 93-96, 101-103, 111-117, 125, 128-132, 135, 139, 145-152, 158-165, 171, 177, 183, 186, 193, 200, 209-210, 214-215, 219-220, 224-225, 229-230, 234, 237-238, 241-244, 247, 250-251, 254-255, 258, 261, 276, 281-283, 287-298, 302-313, 317, 321-324, 328-335, 338-340, 343-345, 348-363, 366-368, 371-373, 376-379, 399-401, 404-406, 412, 417, 422, 426, 429-431, 434-436, 439-441, 444, 447, 450, 467-471, 475, 479, 482-484, 487-488, 491-492, 495-496, 499-504, 507-512, 525, 528, 531, 534, 547-548, 551-553, 556-558, 561, 564, 567, 570, 573, 577-578, 591, 594, 597-599, 602, 616, 619, 622, 625, 648, 651, 656, 661, 672, 675, 678, 695-698, 702, 706-708, 712-714, 717-719, 722-739, 743-745, 748, 751, 754-764, 767, 772, 798-810, 815-822, 830-836, 840-846, 850-856, 872, 875-877, 880-881, 884-886, 889-891, 912, 915-919, 922-929, 932-936, 939-941, 944-946, 961, 964, 967, 986-995, 999, 1003, 1006-1008, 1011-1019, 1022-1030, 1033-1055, 1059, 1063, 1068, 1086-1091, 1094-1096, 1099, 1102-1107, 1110-1115, 1118-1127, 1131, 1135, 1139, 1165-1166, 1170, 1173, 1176, 1179, 1182-1184 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/uniform.py 60 34 43% 32, 36, 40, 44, 47-56, 59-65, 69, 72-74, 77-81, 84-87, 90-91, 94 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/utils.py 73 51 30% 27-40, 44-48, 59-62, 72-74, 78-79, 89-92, 107-112, 122, 130-136, 145-155 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/von_mises.py 84 53 37% 11-15, 34-49, 54-65, 89-98, 101-105, 114-116, 119-125, 132, 136, 143 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/weibull.py 37 18 51% 28-33, 38-49, 53, 57, 61, 65 /home/admin/.local/lib/python3.8/site-packages/torch/distributions/wishart.py 144 105 27% 18-19, 26, 73-114, 126-155, 159, 164, 170-175, 181, 185-187, 192-194, 197-211, 223-261, 264-268, 276-279, 288-290, 293-294 /home/admin/.local/lib/python3.8/site-packages/torch/fft/__init__.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/functional.py 331 248 25% 71-73, 103-136, 182-189, 317, 328-345, 351, 355-358, 366-368, 463, 467-478, 593-606, 683, 751-771, 826-832, 838-842, 848-852, 858-862, 900-904, 910-914, 920-924, 959, 965, 969, 973, 977, 1029-1066, 1097-1099, 1132-1134, 1179-1189, 1220-1224, 1257-1261, 1302-1306, 1310, 1327, 1332, 1337, 1342, 1438-1502, 1539-1545, 1626, 1629, 1634-1638, 1642-1652, 1657-1667, 1682 /home/admin/.local/lib/python3.8/site-packages/torch/futures/__init__.py 34 16 53% 36-38, 51, 72, 93, 156, 199, 237, 257-263, 291, 309 /home/admin/.local/lib/python3.8/site-packages/torch/fx/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/fx/_compatibility.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/fx/_pytree.py 31 15 52% 12-25, 28, 31, 34, 37 /home/admin/.local/lib/python3.8/site-packages/torch/fx/_symbolic_trace.py 363 272 25% 73-74, 77-93, 97-117, 130, 185-199, 233-291, 313, 328-340, 370-373, 387-470, 474-497, 524-592, 609, 617-625, 629-644, 648-663, 672, 677, 682, 687, 692-694, 701-708, 715-720, 724-728, 731, 737-740, 748-756, 764-767, 805, 820, 877-880, 885 /home/admin/.local/lib/python3.8/site-packages/torch/fx/graph.py 557 426 24% 21-22, 69, 81-88, 92-104, 116-121, 130-159, 166-169, 173-184, 200-207, 211-212, 215, 218, 222-224, 227, 230-235, 238, 251, 260-262, 269, 280, 288, 295, 298-510, 521-522, 525-526, 529-534, 537-547, 550-553, 606-615, 624, 628-630, 645, 665-672, 682-689, 722-736, 743, 747, 760-775, 796-799, 820-823, 851-852, 879-911, 949-956, 991, 1026, 1051-1057, 1078, 1081-1089, 1127-1151, 1154, 1162-1173, 1182-1190, 1205-1262, 1295-1306, 1310, 1386-1396 /home/admin/.local/lib/python3.8/site-packages/torch/fx/graph_module.py 355 283 20% 40-52, 57-59, 62-64, 70-71, 76-80, 84-89, 93-96, 103-105, 112-113, 119-124, 136-178, 183-205, 210-224, 228-229, 243-260, 263-277, 301-309, 336-379, 392, 401-404, 418-468, 493-510, 531-553, 570-612, 620-622, 631-656, 661-667, 670-679, 689-693, 699-701, 704, 707-708, 711-713 /home/admin/.local/lib/python3.8/site-packages/torch/fx/immutable_collections.py 27 5 81% 14, 37, 40, 43, 46 /home/admin/.local/lib/python3.8/site-packages/torch/fx/interpreter.py 144 96 33% 67-89, 108-133, 149-152, 173-185, 203-204, 221-224, 242-246, 266-269, 287, 301-307, 321-325, 338-343, 387-399, 415-417, 433-434, 439-441, 446, 454-459 /home/admin/.local/lib/python3.8/site-packages/torch/fx/node.py 246 180 27% 12, 36-43, 54-62, 66-71, 74-92, 153-200, 211, 222, 237-244, 255, 258-259, 271, 282, 294, 305, 319, 332-334, 347-349, 359, 363, 369-380, 383-385, 394-407, 441-461, 483-503, 515-533, 562-569, 582-589, 597-598, 605-616 /home/admin/.local/lib/python3.8/site-packages/torch/fx/operator_schemas.py 221 176 20% 14, 30, 34, 43-45, 61, 64-84, 88-120, 138-154, 158-182, 186-230, 259-328, 353-367, 392-408 /home/admin/.local/lib/python3.8/site-packages/torch/fx/proxy.py 223 134 40% 40-43, 47, 63-84, 94-105, 115-151, 160, 169, 184, 191-192, 196, 234-238, 241, 246, 249, 252-260, 263-284, 288, 291, 297-319, 327-330, 336-338, 341, 352-355, 358, 362, 365, 368, 372, 375, 378, 384-386, 396-397 /home/admin/.local/lib/python3.8/site-packages/torch/fx/subgraph_rewriter.py 152 133 12% 20-31, 41-42, 49-86, 90-132, 250-428 /home/admin/.local/lib/python3.8/site-packages/torch/hub.py 304 250 18% 20-55, 83-90, 94-98, 103, 108-110, 122-140, 144-145, 150-169, 176-241, 244-290, 294-295, 299-304, 308-322, 336-341, 352, 394-407, 446-458, 530-541, 563-573, 591-633, 640-643, 647-660, 699-731 /home/admin/.local/lib/python3.8/site-packages/torch/jit/__init__.py 52 16 69% 75, 130, 194, 215-217, 220, 223, 230-235, 242, 248, 253 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_async.py 10 2 80% 84, 96 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_builtins.py 42 1 98% 163 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_check.py 87 75 14% 63-82, 85-112, 125-132, 140-181, 195-240 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_decomposition_utils.py 6 3 50% 5-8 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_freeze.py 29 23 21% 101-121, 167-173, 207-220 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_fuser.py 73 61 16% 12-17, 30-63, 69-74, 77, 80-107, 140 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_ir_utils.py 14 7 50% 6-8, 11-12, 15, 18 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_monkeytype_config.py 102 75 26% 15-18, 24-32, 38-53, 61-66, 69, 73-149, 155, 163, 179-183 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_recursive.py 501 443 12% 39-47, 50-52, 55-66, 70-79, 82-93, 102-107, 118, 127-330, 347-362, 368-375, 378-384, 399-414, 423-435, 453-458, 469-595, 602-608, 611-612, 616-638, 643-651, 654-657, 660-669, 672-685, 693-740, 747-776, 784-797, 809-824, 827-845, 851, 857-867, 870-877, 886-910 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_script.py 595 404 32% 62, 71-72, 151, 155, 161-162, 182, 185, 188, 191, 194, 197, 200-205, 208, 211-213, 218-225, 228-229, 232, 244-248, 254, 272, 279-280, 292-318, 326, 334-350, 355, 358, 366-379, 427-434, 437-443, 446-452, 457-461, 464, 467-470, 475, 496, 501-503, 506-523, 526-545, 548, 559-561, 592-598, 614-620, 624-633, 642-665, 673, 682, 691, 705-706, 714, 728, 731, 734, 737, 740, 743, 747-749, 760-761, 764-785, 788-814, 817, 820, 827-832, 835, 838, 841, 844, 849-854, 860-865, 870-874, 943, 959-969, 972-1002, 1006-1007, 1022, 1036, 1239-1351, 1359-1361, 1370-1389, 1394-1416, 1420-1422, 1430-1457, 1461-1466, 1473-1475, 1480-1483, 1486, 1489-1503, 1508-1509, 1512-1531, 1536, 1539, 1542, 1545-1570, 1573, 1577-1578 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_serialization.py 70 57 19% 78-84, 150-169, 173-184, 188-194, 198-209, 251-261, 285-291 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_state.py 63 35 44% 27-37, 47, 51, 66-67, 71-74, 78, 82-83, 98-102, 105, 108-114, 118-119 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_trace.py 453 383 15% 34-54, 61-72, 84-92, 95-140, 144-160, 173-185, 222-277, 281-283, 287, 292-305, 321-535, 554-559, 563-577, 581-584, 734-830, 922-1005, 1014, 1023-1087, 1090, 1093-1095, 1098-1100, 1103, 1106, 1119, 1129-1130, 1171-1176 /home/admin/.local/lib/python3.8/site-packages/torch/jit/annotations.py 254 213 16% 34-37, 54-56, 59-63, 66-91, 97, 101-109, 113-127, 132-144, 154-170, 175-232, 245-250, 255-270, 276-287, 290-301, 306-398, 402-405 /home/admin/.local/lib/python3.8/site-packages/torch/jit/frontend.py 612 469 23% 30-31, 42, 100-105, 108, 118-125, 133-134, 138-139, 153-166, 170-183, 189-216, 234-265, 270-281, 285-288, 292-294, 298-314, 324-350, 355-365, 368-450, 453-458, 477-488, 494-500, 520-526, 530-532, 536-553, 557-559, 563-564, 568-570, 574-577, 581-590, 594-599, 604-608, 614-615, 621-625, 629-630, 634-635, 639-640, 644-652, 698-711, 715-727, 731-732, 736-748, 752-762, 766-779, 783-789, 793-804, 808, 814-835, 839-910, 914, 919, 924-927, 932-934, 938-951, 955-957, 961-978, 982-993, 997-1009, 1013-1014, 1021-1022 /home/admin/.local/lib/python3.8/site-packages/torch/library.py 53 17 68% 28, 38, 46-52, 58, 77, 81-83, 93-96 /home/admin/.local/lib/python3.8/site-packages/torch/linalg/__init__.py 44 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/multiprocessing/__init__.py 22 6 73% 43-44, 58-59, 64, 69 /home/admin/.local/lib/python3.8/site-packages/torch/multiprocessing/_atfork.py 13 5 62% 6-11 /home/admin/.local/lib/python3.8/site-packages/torch/multiprocessing/reductions.py 153 101 34% 18-19, 29-32, 35, 38, 58-59, 62-65, 68-74, 82, 86-87, 91-100, 107-138, 142-275, 285-286, 290-293, 297-306, 310-322, 326, 329, 333, 336, 340, 343-365 /home/admin/.local/lib/python3.8/site-packages/torch/multiprocessing/spawn.py 98 71 28% 16-19, 22, 36, 50-52, 55, 66-76, 81-83, 89, 105-160, 165-166, 179-199, 235-240 /home/admin/.local/lib/python3.8/site-packages/torch/nn/__init__.py 20 13 35% 32-47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/_reduction.py 31 26 16% 8-20, 28-43, 47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/common_types.py 25 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/functional.py 1164 929 20% 13, 461-482, 492-503, 562-587, 597-608, 657-671, 682-696, 743-757, 768-782, 829-843, 854-868, 885-912, 926-948, 962-980, 994-1012, 1027-1037, 1052-1061, 1076-1080, 1084-1088, 1116-1121, 1125-1129, 1157-1162, 1166-1170, 1211-1214, 1228-1231, 1248-1252, 1260-1264, 1282-1302, 1320-1346, 1364-1385, 1408-1414, 1422-1428, 1453, 1455, 1489-1493, 1503-1509, 1529-1535, 1544-1550, 1573-1579, 1600-1606, 1628-1634, 1668-1676, 1733-1735, 1745-1747, 1766-1775, 1793-1801, 1829-1837, 1880-1899, 1918-1926, 1949-1950, 1960-1961, 1979-1983, 2044-2048, 2063-2067, 2087-2091, 2095-2096, 2167-2199, 2293-2383, 2400-2404, 2422-2438, 2445-2449, 2468-2483, 2500, 2513-2516, 2526-2551, 2609-2616, 2675-2689, 2734-2754, 2782-2839, 2889-2921, 2999-3014, 3058-3083, 3130-3150, 3166-3188, 3202-3218, 3234-3249, 3265-3280, 3296-3319, 3334-3349, 3363-3377, 3391-3399, 3414-3446, 3462-3478, 3495-3518, 3657, 3662, 3722-3723, 3732, 3737, 3742, 3755, 3819, 3832-3833, 3839, 3848, 3854, 3863-3880, 3883, 3888, 3893-3902, 3905, 3908, 3910, 3912, 3915, 3917, 3919, 3922-3923, 3925-3926, 3928-3929, 3932-3933, 3937, 3939-3961, 3974, 3979, 4002-4003, 4014, 4021, 4028, 4035, 4058-4059, 4184-4223, 4274-4320, 4524-4543, 4559-4595, 4617-4624, 4628, 4653-4666, 4684-4700, 4741-4760, 4806-4813, 4848-4861, 4871-4909, 4999-5199 /home/admin/.local/lib/python3.8/site-packages/torch/nn/grad.py 87 77 11% 9-37, 65-76, 104-124, 154-165, 193-216, 247-258, 286-309 /home/admin/.local/lib/python3.8/site-packages/torch/nn/init.py 193 102 47% 24-54, 102, 104, 106, 109, 114, 116-119, 136, 154, 176, 190-192, 233-238, 256-281, 287, 323-327, 350-353, 360, 396, 405-406, 444-451, 469-495, 514-526, 535-537 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/modules/__init__.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/modules/fused.py 57 26 54% 13-16, 22-25, 31-34, 40-43, 49-52, 58-61, 67-70, 76-79, 85-88, 94-97, 104-107, 113-116, 123-125 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/conv_fused.py 250 143 43% 44-66, 69, 72-79, 82, 85-87, 90-92, 95-114, 118, 121, 129-133, 158-185, 197-218, 221-258, 296-300, 342, 349, 353, 376-382, 385, 390, 428-432, 474, 481, 485, 508-514, 517, 522, 568-572, 637, 654, 658, 690-704, 707, 713, 716-719, 722-725 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/linear_fused.py 84 65 23% 37-57, 60, 63-65, 68, 71-73, 76-78, 81-118, 126-130, 139-155, 158-167 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/linear_relu.py 20 9 55% 32, 35, 39, 42-47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/modules/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/modules/linear_relu.py 22 10 55% 27, 30-39, 42, 46, 50 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/bn_relu.py 36 14 61% 21, 26-28, 33, 38, 42, 57, 62-64, 69, 74, 78 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/conv_relu.py 74 40 46% 28, 36-43, 47, 51-55, 59-61, 78, 86-92, 96, 100-104, 108-110, 127-128, 136-142, 146, 150-160, 164-166 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/linear_relu.py 17 5 71% 25, 28, 32, 36, 40 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/__init__.py 23 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/_functions.py 137 119 13% 10-103, 107-172, 178-230, 234-265, 270-271, 275 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/activation.py 397 222 44% 47-50, 54, 57-58, 101-102, 157-160, 163, 166-167, 216-227, 230, 233-234, 262, 265-266, 290, 325-326, 329, 354, 387-388, 391, 394-395, 423-424, 427, 430-431, 468-469, 472, 509-511, 514, 517-518, 553-555, 558, 561-562, 603-604, 607, 610-611, 637-638, 641, 644, 684, 719-720, 723, 726, 767-769, 772, 775-776, 799, 833-835, 838, 841, 874-875, 878, 881, 945-983, 986-999, 1003-1006, 1060-1164, 1220-1223, 1226, 1229, 1252, 1275, 1311-1312, 1315-1317, 1320, 1323, 1367-1368, 1371-1373, 1376, 1379, 1405-1406, 1438-1439, 1442-1444, 1447, 1450 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/adaptive.py 114 92 19% 122-161, 164-167, 170-241, 247-259, 278-279, 296-312 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py 197 143 27% 37-63, 66-71, 74-77, 80, 83, 98-107, 129-130, 135-168, 190-209, 213-214, 217-227, 297-298, 332-333, 406-407, 439-440, 512-513, 545-546, 661-665, 668-669, 674-675, 681-748, 795-819 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/channelshuffle.py 13 4 69% 45-46, 49, 52 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/container.py 345 202 41% 19-24, 76, 80, 85-86, 93-98, 102-105, 108-109, 112-117, 121, 125-127, 148-149, 187, 189, 195, 200-201, 204-211, 225-228, 232-234, 243-245, 263, 320-322, 326, 329, 332, 336, 340, 344, 349, 357-359, 365, 371, 377, 391-412, 446-449, 453-458, 462, 466, 469-477, 485-488, 491, 494, 497, 500-502, 510-513, 522-527, 530-543, 546, 587-590, 593-599, 602-603, 611-615, 618-620, 623, 626, 629, 636, 639, 651-653, 658-659, 667-669, 675-680, 690, 699, 704, 709, 714, 729-751, 754-766, 769, 772-774, 777-779, 782-783 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/conv.py 331 168 49% 51, 84, 86, 89-94, 98, 115-122, 152-166, 169-171, 287-294, 299-303, 307, 450, 580-585, 590-602, 607, 616, 632-665, 774-780, 785-795, 940, 1080-1086, 1091-1102, 1124-1127, 1146-1150, 1155-1169, 1173-1180, 1185, 1233-1251, 1254, 1302-1320, 1323, 1371-1389, 1392, 1438-1457, 1460, 1506-1525, 1528, 1574-1593, 1596 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/distance.py 25 9 64% 37-40, 43, 72-74, 77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/dropout.py 33 12 64% 13-18, 21, 58, 100, 149, 191, 233, 282 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/flatten.py 48 27 44% 40-42, 45, 48, 106-116, 119-125, 129-135, 138, 141 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/fold.py 38 15 61% 136-141, 144, 148, 287-291, 294, 298 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/instancenorm.py 72 40 44% 18-19, 23, 26, 29, 32, 38-62, 67-72, 143, 146-147, 182, 185-186, 259, 262-263, 298, 301-302, 375, 378-379, 414, 417-418 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/lazy.py 79 49 38% 15, 18, 23, 26, 29, 33, 37, 41, 45, 49, 175-178, 185-194, 208-217, 224, 231-236, 248-256, 260 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/linear.py 91 36 60% 42, 45, 100, 117, 130, 180-191, 194-197, 200, 203, 243-250, 253-254, 257-263 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py 201 83 59% 16-20, 25-27, 93, 96, 207-208, 211, 217-220, 286-289, 292, 367-369, 372, 461-462, 465, 527, 530, 610, 613, 707-711, 714, 773-774, 777, 838, 841, 918-919, 922, 978-979, 982, 1020, 1023, 1159-1161, 1164, 1210, 1213, 1262-1263, 1266, 1319-1320, 1323, 1392-1397, 1400, 1477-1481, 1484, 1591-1595, 1598, 1739-1741, 1744 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py 660 395 40% 22-24, 30-38, 79-81, 111-113, 131-139, 176-184, 201, 304, 307, 310, 313, 315, 317, 319, 343, 347, 350, 352, 354, 359, 363, 382, 385, 388, 390, 392, 397, 447-465, 489-503, 527-540, 557, 572, 593, 608-611, 614-621, 667-670, 689, 708, 727, 738, 752, 763, 774, 785, 796, 808, 813, 817, 821, 911-915, 923, 943-951, 986-994, 1001-1013, 1016-1053, 1077-1079, 1100-1102, 1105-1122, 1132-1174, 1179-1192, 1217, 1222, 1227-1231, 1236, 1241-1245, 1249-1253, 1258-1266, 1275-1277, 1292-1300, 1308, 1312, 1357-1386, 1403-1407, 1435-1437, 1474, 1485-1489, 1497, 1501-1504, 1508-1515, 1519-1522, 1524, 1532, 1559, 1570, 1583-1584, 1595, 1599, 1604, 1610-1619, 1642-1643, 1665-1669, 1690-1691, 1713-1717, 1772-1773, 1807-1818, 1836, 1880-1882, 1892-1908, 1912, 1915, 1924, 1928-1949, 1952-1962, 1965-1975 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/normalization.py 97 36 63% 48-52, 55, 59, 69-73, 76, 80, 178-179, 193, 248-264, 267-269, 272, 276 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/padding.py 83 28 66% 19-20, 23, 26, 75-76, 125-126, 165-166, 174, 177, 216-217, 267-268, 319-320, 328, 331, 370-371, 421-422, 461-462, 512, 515 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/pixelshuffle.py 23 8 65% 49-50, 53, 56, 99-100, 103, 106 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/pooling.py 241 104 57% 21-27, 30, 88, 162, 240, 248, 312-315, 318, 395-398, 401, 461-464, 467, 475, 534-539, 542, 613-619, 622, 699-705, 708, 712-715, 765-778, 782, 834-847, 851, 865-869, 872, 914, 969, 978-980, 983, 1016, 1058, 1101, 1108-1109, 1112, 1140, 1179, 1218 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/rnn.py 473 382 19% 21, 43-124, 127-131, 140-175, 182-191, 194-196, 199-205, 210-221, 225-226, 229-232, 235-237, 241-254, 257-292, 296, 299-304, 411-420, 425, 430, 433-499, 673, 676-683, 692-695, 703-705, 712, 719, 722-784, 898-900, 905, 910, 913-967, 983-997, 1000-1005, 1008-1010, 1072-1074, 1077-1108, 1173-1174, 1177-1197, 1264-1265, 1268-1288 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/sparse.py 130 71 45% 129-133, 142-144, 154-155, 158, 163-174, 205-218, 318-341, 344-345, 348-350, 383, 390-400, 432-447 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/transformer.py 187 144 23% 57-83, 136-149, 156, 161-163, 186-190, 203-246, 267-270, 288-299, 361-388, 391-393, 410-466, 471-475, 479-480, 523-546, 549-551, 570-580, 585-589, 594-598, 602-603, 607, 611-616 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/upsampling.py 37 17 54% 141-150, 153, 157-162, 207, 253 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/utils.py 35 17 51% 32-38, 57-75 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/__init__.py 10 3 70% 11-14 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_functions.py 88 60 32% 14-30, 34, 41-45, 49, 56-75, 79-82, 89-104, 108, 118-124 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_replicated_tensor_ddp_utils.py 13 7 46% 18-23, 27, 31 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/comm.py 81 70 14% 29-38, 56-58, 76-104, 126-149, 186-199, 228-241 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py 94 76 19% 17-37, 122-145, 148-169, 172, 175, 178, 181, 199-232 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py 440 339 23% 46-53, 57-60, 67-79, 83-137, 159-168, 172-178, 186-193, 200-230, 237, 538-664, 667-672, 675-677, 691-766, 769-776, 780-794, 798-839, 850-860, 865-898, 905-916, 919-927, 952-957, 960-971, 974-1068, 1071, 1075, 1080, 1083-1086, 1091-1099, 1104-1106, 1114-1117, 1128-1145, 1149-1150, 1252, 1286-1287, 1293, 1297, 1341-1342, 1425-1427, 1451-1452, 1513-1519, 1526, 1531, 1539, 1546, 1555-1566, 1569-1585, 1588-1595, 1605-1610, 1617-1620, 1625-1658, 1662, 1688, 1700-1701, 1714-1719, 1727-1736 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py 57 50 12% 9-20, 38-88 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/replicate.py 107 97 9% 8-9, 13-14, 18-19, 23-24, 37-62, 65-75, 79-167 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/scatter_gather.py 49 43 12% 6, 17-39, 44-52, 60-81 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parameter.py 67 34 49% 33, 40-48, 53-58, 61, 65, 106-111, 115, 122, 128, 132, 141-145, 176-178, 200-202 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/modules/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/modules/linear.py 7 3 57% 20-22 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/conv.py 84 50 40% 29-35, 38, 48-65, 71-95, 126-130, 148, 181-185, 202, 206, 239-243, 260, 264 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/embedding_ops.py 56 38 32% 27-36, 39, 50-64, 67-72, 95-104, 107, 120-134, 137-142 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/linear.py 38 26 32% 30-34, 37, 45-69, 72-77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/activation.py 242 222 8% 64-83, 86, 90-150, 160-219, 224-247, 305, 321-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/rnn.py 230 196 15% 34-48, 51-73, 76-80, 83, 93-104, 108-115, 126-128, 131-136, 140-143, 151-157, 160-209, 218-241, 277-312, 315-361, 364, 368-379, 383 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/conv.py 108 58 46% 19-35, 51-54, 67-71, 74, 78, 87-90, 103-107, 110, 114, 123-126, 139-143, 146, 150, 160-177, 195-198, 211-221, 224, 228, 239-242, 254-266, 269, 273, 283-286, 299-309, 312, 316 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/linear.py 23 11 52% 25-26, 29, 42-44, 48-55 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/rnn.py 262 210 20% 11, 14-22, 25-29, 32-36, 41-54, 57-79, 83, 86, 89, 92, 95, 105-107, 110, 115-146, 150-162, 172-173, 176, 179-199, 203-214, 224-225, 228, 231-251, 255-266, 274-289, 292-312, 322, 329-331, 334-341, 350-353, 365-375, 378-388, 391-453, 456, 460-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/sparse.py 29 12 59% 19-21, 24, 27-28, 34, 60-63, 66, 69-70, 78 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/utils.py 73 61 16% 6-41, 52-55, 64-67, 76-77, 83-87, 98-112, 124-133, 136-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/conv.py 111 64 42% 56-67, 72, 77-84, 120-131, 136, 141-147, 184-195, 200, 205-211, 253-259, 264, 269-271, 313-319, 324, 329-331, 373-379, 384, 389-391 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/linear.py 58 43 26% 34-39, 43-55, 58, 61-66, 70-72, 83-112, 122-126 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/rnn.py 506 403 20% 12, 16-33, 37-38, 41-42, 46-47, 59-123, 126, 129-140, 147-170, 173-179, 184-191, 197-198, 202-204, 208-210, 214-216, 221-254, 258-341, 346-364, 367, 370, 391, 394, 401-426, 432-440, 446-455, 461-463, 469-474, 479-482, 486, 490-504, 616, 619, 622-625, 633-670, 677-685, 691-699, 704-706, 710-713, 717, 725-763, 766, 769-774, 777-778, 783-789, 795-841, 845-873, 877-886, 889, 892, 897-901, 907-909, 913-915, 937-938, 941, 944-962, 966, 989, 992, 995-1001, 1008, 1029, 1032, 1035-1039, 1047 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/functional.py 143 114 20% 40-42, 70-72, 89-91, 106-109, 154-168, 213-227, 273-287, 325-327, 358-363, 374-378, 390-394, 409-411, 431-441, 446-450, 460-462, 475-481, 492-494, 499-503, 516-518, 570-571, 592-593, 614-615 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/__init__.py 38 13 66% 44-50, 53, 58-60, 63, 82, 85, 89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/activation.py 94 49 48% 28-29, 32, 35, 39, 49-51, 54, 58, 62-63, 67, 78-80, 83, 87, 91-92, 96, 108-111, 114, 118, 122-123, 127, 138-140, 143, 147-148, 159-162, 165-172, 176, 180-181, 185 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/batchnorm.py 64 38 41% 8-11, 15-26, 30-43, 52-53, 56, 61-62, 67, 73, 82-83, 86, 91-92, 97, 103 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/conv.py 381 265 30% 25-30, 37, 46-80, 83, 86, 89, 92-104, 119-124, 128-129, 152-160, 166-179, 182-186, 189, 195-212, 216-238, 249-265, 316-324, 329, 332-336, 341-342, 345, 348, 353-360, 370, 416-423, 428, 431-435, 439, 442, 445, 450-456, 467, 513-521, 526, 529-533, 537, 540, 543, 548-554, 565, 577-582, 588-592, 602-623, 634-651, 699-706, 711, 714, 719-720, 723-724, 727-728, 733-735, 740, 787-794, 799, 802, 807-808, 811-812, 815-816, 821-823, 828, 876-883, 888, 891, 896-897, 900-901, 904-905, 910-912, 917 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/dropout.py 13 4 69% 15, 18, 22, 26 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/embedding_ops.py 134 95 29% 12-22, 26-29, 34-37, 40, 48-50, 54-61, 65, 93-110, 113-116, 119, 122, 125-129, 132, 135, 145-175, 179-190, 220-225, 229-234, 239, 249-276, 280-292 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/functional_modules.py 105 69 34% 34-35, 38, 43-45, 49-52, 56-58, 62-65, 69-71, 75-78, 93, 98-99, 103-104, 108-109, 113-114, 118-119, 123-125, 154-157, 160-162, 167-169, 173, 176, 181, 186-188, 192-195, 199-201, 205-208, 212-214, 218-220, 224-230 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/linear.py 141 103 27% 16-22, 26-31, 36-41, 44, 63-65, 69-86, 91, 125-147, 150, 153, 158, 161, 194-196, 203-218, 225, 228, 231, 234, 244-276, 288-296 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/normalization.py 100 59 41% 15-22, 25, 30, 34-38, 42, 58-64, 67, 72, 76-80, 93-99, 102, 107, 111-115, 119, 134-140, 143, 148, 152-156, 160, 175-181, 184, 189, 193-197, 201 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/utils.py 50 35 30% 12, 15-31, 37-41, 48-71 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/clip_grad.py 34 26 24% 30-56, 68-70, 85-89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/convert_parameters.py 29 24 17% 16-24, 36-54, 73-84 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/fusion.py 33 27 18% 7-14, 17-33, 36-43, 46-53 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/init.py 10 7 30% 44-51 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/memory_format.py 8 6 25% 64-69 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/parametrizations.py 152 124 18% 13-17, 24-28, 45-65, 68-105, 109-168, 258-281, 292-316, 320-326, 362-370, 374-388, 393, 472-486 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/parametrize.py 223 198 11% 48-54, 58-61, 97-189, 209-254, 258-269, 281-299, 315-346, 471-552, 566-573, 601-656, 665-668, 686-728 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/rnn.py 126 90 29% 22-24, 64, 76, 82-85, 89-92, 95, 98, 101, 104, 107, 110, 113, 116, 134-142, 147, 151, 166-184, 193-195, 199-204, 244-261, 321-335, 379-396, 433-446, 481-482, 511-513 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/spectral_norm.py 138 111 20% 26-32, 35-41, 73-93, 96-102, 105, 111-112, 116-152, 160, 172-198, 206, 209-214, 273-281, 295-314 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/stateless.py 53 41 23% 12-36, 39-48, 52-55, 63-69, 81-84, 125-142 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py 52 36 31% 15-18, 22-24, 28-54, 57-61, 64, 108-109, 123-129 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/__init__.py 63 28 56% 42-47, 51-54, 348-350, 386-388, 392-394, 408-410, 414-416, 420-422, 430-432, 464-466, 473, 480, 487, 498, 509 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_constants.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_globals.py 20 6 70% 33, 37-41 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_patch_torch.py 112 90 20% 49-72, 77-78, 84-99, 103-116, 123, 132-133, 137, 145-167, 184-222, 230-231 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_caffe2.py 151 113 25% 11-35, 39-46, 50-51, 55-56, 63-65, 70-76, 83-85, 92-105, 112-125, 130-136, 141-149, 154-160, 165, 172, 178-191, 196-212, 226-249, 253-262, 267-285, 289-301, 306-318 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_helper.py 624 503 19% 67-112, 121-123, 127-130, 134-140, 144-146, 150-157, 163, 199-232, 284-315, 324-325, 335-343, 347, 351, 355, 362, 366, 370, 379-380, 388, 395-397, 401-409, 413-418, 422-430, 435-440, 444, 453, 462, 471-479, 483-488, 492-507, 511-518, 522-537, 552-555, 559-572, 578-591, 597-604, 608-611, 625-635, 639-659, 663-684, 688-710, 714-726, 730-737, 741-752, 756-785, 790-838, 855-933, 946-952, 956-961, 965-972, 976-1000, 1004-1008, 1012-1015, 1024-1043, 1052-1062, 1066-1113, 1117-1122, 1126-1135, 1149-1165, 1169-1176, 1180-1182, 1186-1193, 1207-1229, 1248-1277, 1286-1297, 1301-1304, 1309, 1313, 1317, 1323, 1432 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset10.py 268 194 28% 24-27, 32-35, 39-55, 60, 65, 74-118, 155-179, 191-202, 218-221, 225-248, 252-287, 300, 311, 327-393, 405-430, 439, 443-447, 451-456, 460, 467-506, 526-535, 539-544, 548-553, 557-561, 576-588, 603-614 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset11.py 585 467 20% 19-38, 44-71, 76-84, 89-97, 101-121, 127, 131-237, 242-245, 264, 271-275, 280-295, 302-311, 315-316, 320-333, 337-343, 347-353, 357-358, 362, 366-378, 382, 386, 390, 394-398, 402-406, 411-414, 429-450, 462-465, 470, 477, 481, 485-487, 492-524, 529, 534-543, 553-600, 604-608, 612-614, 618-620, 632-642, 646, 650, 654-709, 714-720, 724-726, 730-777, 781-784, 788, 792-808, 812-829, 833-839, 845-866, 872-893, 905-936, 943-944, 948-954, 968-1012, 1016-1017, 1025-1045, 1050-1066, 1082-1153, 1158-1185, 1195-1206, 1215-1216, 1224-1240 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset9.py 2398 1890 21% 58-60, 64, 68-70, 74, 78-79, 83-91, 96-98, 102, 106, 110-113, 118-119, 124-131, 137-165, 169-189, 194, 198, 212-225, 230-232, 237-238, 243-247, 251, 257-258, 262, 266, 271-312, 323, 327, 331, 337, 341, 345, 349, 353, 357, 361, 365, 369, 373-376, 380-386, 391-402, 410-416, 428-452, 465-470, 474-481, 485-492, 496, 500-514, 518-529, 534-546, 562-580, 584-591, 596-613, 620-622, 626, 630-631, 636-648, 653-675, 679, 684-688, 692, 697-707, 712-723, 727, 731-789, 793-814, 818, 822, 842-871, 876, 881-882, 886, 890, 894-895, 901-905, 909-912, 917-922, 947-983, 987-990, 994-1030, 1036-1085, 1131-1158, 1180-1210, 1251-1259, 1263-1274, 1278-1288, 1294-1330, 1334-1337, 1343-1346, 1360-1370, 1375-1386, 1402-1405, 1409-1414, 1418-1428, 1434-1436, 1447, 1453-1458, 1462-1468, 1473, 1477, 1481-1489, 1493, 1497-1505, 1510, 1515, 1519-1522, 1529-1532, 1539-1542, 1550, 1555, 1560, 1566-1584, 1590-1608, 1614-1623, 1631-1655, 1677-1722, 1727, 1747, 1767, 1789, 1811, 1833, 1864-1903, 1908-1937, 1953-2028, 2033-2060, 2065-2072, 2076, 2084, 2088-2104, 2108-2126, 2130-2136, 2141-2168, 2172-2185, 2194-2208, 2212-2225, 2230, 2234, 2238, 2242, 2248-2249, 2253-2262, 2268-2283, 2288-2295, 2300-2307, 2314-2325, 2329, 2334-2345, 2349, 2354, 2359, 2364-2369, 2375, 2380-2389, 2396-2398, 2416-2422, 2427-2438, 2443-2452, 2457-2467, 2481, 2488, 2492-2498, 2502-2506, 2510-2535, 2539, 2545-2550, 2563-2566, 2576-2582, 2587-2592, 2605-2608, 2618-2624, 2628-2639, 2658-2669, 2679-2685, 2689-2708, 2712-2779, 2784, 2791-2792, 2801, 2806, 2811-2813, 2818-2833, 2837, 2843-2862, 2867-2880, 2884-2885, 2890-2897, 2901-2967, 2971-2974, 2978-3067, 3072-3121, 3141-3187, 3220-3426, 3442-3445, 3473-3476, 3492-3495, 3499-3519, 3538-3539, 3566-3567, 3582-3585, 3597-3605, 3610, 3615-3617, 3625-3634, 3644-3647, 3651-3668, 3672-3689, 3695-3698, 3706-3709, 3716-3717, 3721-3741, 3746-3747, 3752, 3758-3775, 3781, 3786, 3791-3792, 3797-3809, 3813-3819, 3824, 3830-3838, 3842-3850, 3855-3868, 3873-3889, 3893-3894, 3898-3900, 3904-3908, 3913, 3917-3923, 3928-3941, 3946-3976, 3980-3981, 3985-3986, 3995-3998, 4002-4003, 4008, 4012-4087, 4093-4099, 4104, 4108-4110, 4114-4271, 4277-4298, 4304-4326, 4332-4379, 4384, 4389-4391, 4396-4407, 4416-4426, 4431-4455, 4459-4461, 4466-4484, 4493-4550, 4559-4578, 4586-4587, 4591, 4595, 4599-4604, 4608-4611, 4615-4621, 4626-4638, 4645-4685, 4689, 4701-4703, 4707-4718, 4732-4755, 4759, 4763, 4768-4776, 4780-4842, 4847-4862, 4867-4880, 4890-4893, 4899-4908, 4916-4925, 4933-4939, 4943, 4947, 4951-4955, 4959, 4963, 4967, 4971, 4975, 4983, 4987-4993, 5000-5003, 5010-5015, 5021-5063, 5067-5152, 5156-5174, 5189-5193 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_registry.py 93 65 30% 29-36, 40-43, 47-49, 70-78, 82-102, 112, 122-125, 130-135, 139-145, 149-154, 159-175 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/utils.py 679 596 12% 40, 50-91, 100-118, 123-130, 135-142, 163, 183-189, 197-215, 229-335, 345-364, 369-380, 405-423, 427, 433-447, 451-454, 458-493, 498-509, 515-528, 532-540, 546-562, 570-613, 617-620, 624-636, 648-656, 665-669, 680-691, 723-811, 833-864, 895-913, 917-972, 976-977, 982-985, 1009-1203, 1207-1215, 1219-1246, 1254-1261, 1265, 1269, 1273-1274, 1304-1308, 1313-1320, 1327-1337, 1343-1346, 1366-1441, 1448, 1457, 1462, 1485-1491, 1496-1541 /home/admin/.local/lib/python3.8/site-packages/torch/optim/__init__.py 30 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/optim/_functional.py 43 28 35% 36-74 /home/admin/.local/lib/python3.8/site-packages/torch/optim/_multi_tensor/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/optim/adadelta.py 93 81 13% 57-68, 71-74, 84-133, 154-166, 188-205, 219-242 /home/admin/.local/lib/python3.8/site-packages/torch/optim/adagrad.py 129 115 11% 63-98, 103-114, 117-120, 130-167, 191-208, 223-226, 243-281, 299-342 /home/admin/.local/lib/python3.8/site-packages/torch/optim/adam.py 165 152 8% 77-90, 93-103, 113-173, 198-213, 245-309, 327-411 /home/admin/.local/lib/python3.8/site-packages/torch/optim/adamax.py 108 96 11% 54-67, 70-78, 88-140, 163-178, 204-229, 245-273 /home/admin/.local/lib/python3.8/site-packages/torch/optim/adamw.py 165 152 8% 78-91, 94-104, 114-177, 203-218, 250-313, 331-415 /home/admin/.local/lib/python3.8/site-packages/torch/optim/asgd.py 108 95 12% 32-39, 42-57, 67-113, 136-148, 174-203, 219-247 /home/admin/.local/lib/python3.8/site-packages/torch/optim/lbfgs.py 255 240 6% 9-31, 46-180, 223-240, 243-245, 248-257, 260-266, 269, 272-273, 276-280, 290-475 /home/admin/.local/lib/python3.8/site-packages/torch/optim/lr_scheduler.py 596 506 15% 27-77, 85, 94, 99, 103, 108-115, 122-168, 196-205, 218-225, 237-245, 248-252, 279-288, 298-305, 314-322, 325-333, 365-367, 370-376, 380, 412-414, 417-423, 427-428, 462-467, 470-482, 485, 524-533, 536-546, 551, 569-570, 573-579, 583, 616-638, 641-647, 656-662, 671-678, 722-724, 727-743, 749, 779-787, 790-792, 801-807, 816-823, 879-911, 915-917, 921-943, 946-954, 959, 962-974, 977-989, 992, 995-996, 1110-1161, 1165-1171, 1174, 1177, 1180, 1190-1222, 1255-1264, 1267-1271, 1300-1343, 1464-1563, 1567-1573, 1577-1578, 1582, 1585-1616 /home/admin/.local/lib/python3.8/site-packages/torch/optim/nadam.py 123 110 11% 59-74, 77-88, 98-148, 172-190, 218-247, 264-299 /home/admin/.local/lib/python3.8/site-packages/torch/optim/optimizer.py 160 139 13% 13, 34-59, 62, 69-70, 73-81, 85-97, 100-115, 128-143, 156-210, 227-251, 264, 276-314 /home/admin/.local/lib/python3.8/site-packages/torch/optim/radam.py 114 101 11% 64-76, 79-86, 96-141, 163-178, 202-236, 251-286 /home/admin/.local/lib/python3.8/site-packages/torch/optim/rmsprop.py 105 93 11% 69-82, 85-89, 99-154, 176-188, 214-235, 251-275 /home/admin/.local/lib/python3.8/site-packages/torch/optim/rprop.py 90 78 13% 58-64, 67-69, 79-125, 145-157, 177-198, 211-237 /home/admin/.local/lib/python3.8/site-packages/torch/optim/sgd.py 106 94 11% 93-105, 108-112, 122-163, 185-197, 220-241, 256-301 /home/admin/.local/lib/python3.8/site-packages/torch/optim/sparse_adam.py 58 51 12% 26-51, 61-111 /home/admin/.local/lib/python3.8/site-packages/torch/optim/swa_utils.py 100 76 24% 99-110, 113, 116-132, 161-187, 233-246, 250-257, 261, 265, 269-271, 274-286 /home/admin/.local/lib/python3.8/site-packages/torch/overrides.py 212 145 32% 71-72, 303-304, 337-1330, 1355-1366, 1400-1431, 1472-1510, 1562-1634, 1645, 1662-1664, 1669-1671, 1695, 1737-1738, 1817, 1820, 1824, 1829-1831, 1839-1844, 1849, 1853, 1856, 1889, 1908, 1912, 1915 /home/admin/.local/lib/python3.8/site-packages/torch/package/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/package/_digraph.py 97 78 20% 14-24, 33-40, 48-53, 57-60, 64-67, 72-74, 79, 83, 87-90, 95-103, 108-116, 121-140, 144-158, 166-167 /home/admin/.local/lib/python3.8/site-packages/torch/package/_directory_reader.py 31 17 45% 11, 14, 28, 31-33, 36-39, 42-43, 48-52 /home/admin/.local/lib/python3.8/site-packages/torch/package/_importlib.py 44 36 18% 22-24, 29-33, 38-50, 60-82, 90-94 /home/admin/.local/lib/python3.8/site-packages/torch/package/_mangling.py 25 14 44% 16-22, 25-26, 34-38, 41, 53-58, 62 /home/admin/.local/lib/python3.8/site-packages/torch/package/_package_pickler.py 57 48 16% 18-28, 34-98, 102-107 /home/admin/.local/lib/python3.8/site-packages/torch/package/_package_unpickler.py 15 9 40% 15-16, 20-26 /home/admin/.local/lib/python3.8/site-packages/torch/package/_stdlib.py 19 12 37% 14-15, 19-29 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/find_first_use_of_broken_modules.py 10 7 30% 21-29 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/is_from_package.py 7 3 57% 13-16 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/trace_dependencies.py 24 21 12% 17-60 /home/admin/.local/lib/python3.8/site-packages/torch/package/file_structure_representation.py 60 50 17% 13-15, 27-32, 41-43, 53-61, 64-66, 72-101, 126-132 /home/admin/.local/lib/python3.8/site-packages/torch/package/find_file_dependencies.py 70 55 21% 15-18, 21-23, 26-28, 31-32, 35-43, 46-49, 52-55, 59-99 /home/admin/.local/lib/python3.8/site-packages/torch/package/glob_group.py 31 19 39% 42-45, 48, 51, 54-55, 61-64, 70-82 /home/admin/.local/lib/python3.8/site-packages/torch/package/importer.py 101 75 26% 52, 73-133, 143-162, 169, 172, 185, 197-203, 206-224, 227-232 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_exporter.py 441 342 22% 74, 107-109, 128-152, 200-241, 256-290, 299-301, 320-350, 368-381, 393-396, 399-408, 411-415, 418-428, 434-485, 498-503, 513-559, 586-686, 696, 706-707, 724-726, 743-745, 762-764, 788, 829, 860, 874, 879-930, 933, 939-944, 947-961, 965-972, 977-979, 985-1035, 1039, 1048-1052, 1056-1058, 1061-1063, 1066-1067, 1078, 1083-1089, 1098, 1107, 1116, 1125, 1133-1136, 1146, 1161-1163 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_importer.py 346 274 21% 70-111, 132-134, 147-148, 168-169, 183-264, 273, 290, 303-304, 311, 320-356, 359-371, 374-376, 382-383, 388-394, 397-402, 406-425, 429-446, 457-461, 472-502, 505-527, 535-545, 548-555, 560-574, 582-595, 598-602, 616-617, 624, 641-642, 657-658, 661-663, 668-676, 679-680, 683-704 /home/admin/.local/lib/python3.8/site-packages/torch/profiler/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/profiler/profiler.py 171 127 26% 27, 70-77, 80-81, 84, 87-98, 101-107, 110-111, 117-128, 144-145, 155-156, 163-164, 171-172, 179, 182-186, 211-232, 240, 249-266, 393-424, 464-465, 468, 471-474, 477-479, 485-497, 500-501, 504-507 /home/admin/.local/lib/python3.8/site-packages/torch/quantization/__init__.py 14 2 86% 18-19 /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fake_quantize.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuse_modules.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuser_method_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/observer.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/qconfig.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quant_type.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantization_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize_jit.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/stubs.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quasirandom.py 66 54 18% 48-68, 85-104, 120-129, 135-137, 148-153, 156-171, 174-179 /home/admin/.local/lib/python3.8/site-packages/torch/random.py 46 33 28% 18, 23, 36-42, 49-55, 62, 85-129 /home/admin/.local/lib/python3.8/site-packages/torch/return_types.py 18 2 89% 11, 14 /home/admin/.local/lib/python3.8/site-packages/torch/serialization.py 527 356 32% 38-40, 93-114, 118-119, 123-124, 133-147, 151-157, 165-169, 178, 184, 188-190, 211, 214, 225, 230, 233, 237, 247, 250, 255-256, 259-260, 265-269, 273-277, 286-293, 299-305, 310-312, 323-325, 374-381, 385-525, 529-604, 707-711, 713, 721-726, 734-946, 957, 964-979, 987, 1013, 1039-1040 /home/admin/.local/lib/python3.8/site-packages/torch/sparse/__init__.py 24 9 62% 11-12, 209-218 /home/admin/.local/lib/python3.8/site-packages/torch/special/__init__.py 37 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/storage.py 475 260 45% 13-14, 31, 70-71, 76, 79, 82, 87, 93-95, 98, 106, 110-113, 116-121, 125, 129, 133, 137, 141, 145, 149, 153, 157, 161, 165, 169, 173-177, 188-195, 200-207, 210, 218, 259-278, 281-284, 292-293, 297, 303-357, 373, 379, 384, 389, 396, 403-424, 431, 442, 445, 448-470, 473-490, 497-516, 519-520, 523, 526-535, 538-541, 544, 547, 550-551, 556, 559, 562, 568, 572, 576, 580, 584, 595-596, 600-604, 610, 614, 617, 620-623, 626-628, 631, 634, 638, 641, 645-665, 668-673, 677, 681, 685, 689, 693, 697, 701, 705, 709, 713, 717, 721, 742-749, 753, 756, 759, 762, 765, 768, 771, 775, 778-779, 782-783, 787, 790, 793-794, 797-810, 819-822, 828-829, 833, 837-838, 843-844 /home/admin/.local/lib/python3.8/site-packages/torch/testing/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/testing/_comparison.py 359 289 19% 13-14, 21-29, 32-39, 72-81, 98-110, 142-166, 188-190, 222-254, 292-295, 300-301, 311, 324, 327-338, 352-360, 367-370, 373-374, 387-388, 392-395, 398-400, 403-408, 411-412, 463-470, 474-477, 482-484, 487-492, 495-513, 518, 568-577, 582-592, 595-601, 604-610, 613-620, 645-669, 688-702, 705-714, 727, 746-769, 788-814, 832, 845-854, 866-872, 875, 925-1016, 1048-1095, 1342-1344 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_creation.py 69 62 10% 84-164 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_deprecated.py 59 30 49% 32-36, 55-57, 72-80, 116-141 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_legacy.py 80 38 52% 45-47, 57, 61, 65, 68, 72, 75, 79, 83, 86, 90, 93, 97, 100, 104, 107, 111, 123-130, 133, 137, 141, 145-150, 154, 158 /home/admin/.local/lib/python3.8/site-packages/torch/torch_version.py 36 20 44% 21-27, 30, 33, 58-71, 74-81 /home/admin/.local/lib/python3.8/site-packages/torch/types.py 44 11 75% 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75 /home/admin/.local/lib/python3.8/site-packages/torch/utils/__init__.py 11 2 82% 10, 15 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_crash_handler.py 17 8 53% 9, 12-17, 21, 25 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_mode_utils.py 60 38 37% 27-33, 48, 52, 59, 66-102, 110-132 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_pytree.py 118 79 33% 43, 46, 49, 52, 55, 58, 61, 64, 74-81, 84-86, 90, 100-103, 106, 109-113, 116, 120-121, 124, 130-145, 152-175, 178-179, 190-216 /home/admin/.local/lib/python3.8/site-packages/torch/utils/backcompat/__init__.py 15 2 87% 13, 16 /home/admin/.local/lib/python3.8/site-packages/torch/utils/checkpoint.py 155 140 10% 7-19, 24-25, 38-46, 50-52, 59-97, 101-150, 230-237, 286-307, 321-392 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/__init__.py 15 3 80% 41-42, 47 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py 72 64 11% 49-76, 131-183 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py 36 27 25% 9-12, 15, 20-22, 25-40, 45, 48-52 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/pin_memory.py 50 42 16% 20-45, 49-72 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/serialization.py 6 2 67% 10-11 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py 23 16 30% 49-72 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py 167 140 16% 17-47, 51-52, 55-57, 66-69, 72-74, 77-80, 109, 152-200, 208-323 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/backward_compatibility.py 3 1 67% 4 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/communication/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/communication/eventloop.py 41 29 29% 15-16, 25-37, 41-45, 53-70 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/communication/iter.py 122 98 20% 20, 39-40, 44-50, 53, 57, 62, 66-85, 93-138, 147-152, 155-164, 167-181 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/communication/map.py 109 87 20% 19, 31-35, 38-42, 45, 49, 54, 58-72, 80-119, 127-132, 135-147, 150-159 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/communication/messages.py 39 5 87% 37, 44, 51-52, 63 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/communication/protocol.py 152 114 25% 8-9, 19-21, 24, 27, 30-32, 35-38, 48-50, 53, 56-64, 68-74, 79-82, 85-88, 91-94, 98-102, 105-109, 112-121, 124-133, 142-148, 151-154, 157-160, 163-166, 171-175, 178-182, 185-192, 195-205 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/communication/queue.py 38 24 37% 12-15, 18-20, 24-29, 34-36, 39-40, 45-51 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py 502 414 18% 73-76, 88, 92, 96-99, 103-113, 220-377, 380-384, 388, 392-413, 416-421, 431-438, 442, 451-454, 457-483, 512-557, 563-588, 593-626, 629, 632-639, 642, 645, 648-665, 670, 678, 683-687, 691-695, 1009-1086, 1089-1119, 1133-1168, 1278-1298, 1309-1347, 1350-1367, 1370-1374, 1381-1400, 1406-1469, 1474-1478, 1481 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/dataloader_experimental.py 80 66 18% 19-29, 32-46, 49, 52-58, 78-138 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/_decorator.py 96 58 40% 27-33, 52-53, 56, 60, 70-83, 88-101, 104-114, 122-141, 153-154, 157, 161, 169-186 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/_typing.py 332 246 26% 58-87, 91-105, 114-173, 177-199, 219, 222-224, 227, 230-237, 240, 265-273, 281-321, 328-332, 337, 357-361, 374-376, 389-392, 399-407, 411, 415, 429-445, 452-463, 472, 477-480, 483, 488-494, 497, 505-536, 547-550, 560-563, 570-604, 614-623 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/dataframe_wrapper.py 76 47 38% 5-11, 17-19, 24-26, 30-32, 36-38, 42-45, 49-51, 55-57, 61-63, 70, 75, 79-80, 84-85, 89-90, 94-95, 99-100, 104-105, 109-110 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/dataframes.py 198 106 46% 33-34, 37-38, 51, 54, 57-62, 65-67, 70, 73, 77-81, 84-88, 91-95, 100-103, 108, 111, 116, 119, 129-133, 136, 139, 143-146, 154-156, 159, 162, 171-174, 177, 180, 189-191, 194, 197, 205-207, 210, 213, 221-223, 226, 229, 237-239, 242, 245-246, 250-255, 261-263, 272, 276, 279, 282-285, 294-297, 300, 303, 306-308, 316-317 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/datapipes.py 100 70 30% 21, 24-26, 32, 35-37, 43-44, 47-54, 60, 63-78, 84-85, 88-106, 112-114, 117-120, 123-130 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/structures.py 12 7 42% 13-15, 18-21 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/datapipe.py 179 100 44% 21-22, 116-123, 127, 132, 135-141, 152-154, 157-162, 166-168, 172-174, 177-180, 183-186, 195, 200, 242-249, 253, 258, 261-262, 273-275, 278-283, 287-289, 293-295, 298-301, 304-307, 312, 315-324, 327-331, 334-337, 344, 349, 354-355, 358-359, 362-363, 366-367 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/__init__.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/callable.py 58 40 31% 64-77, 80-112, 115-116, 119-121, 171 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/combinatorics.py 83 52 37% 35-42, 45, 49-51, 99-111, 114-115, 118, 121-136, 139-141, 144-146, 149-158, 161-170, 173 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/combining.py 265 203 23% 42-47, 50-52, 55-63, 88-93, 103-117, 120, 123-149, 152, 155-160, 163-171, 174-184, 187, 218-223, 228, 231, 240-254, 261, 300-309, 321-335, 339-357, 361-374, 377, 380-383, 386-396, 399-409, 412-413, 433-435, 438-449, 452-460, 463, 466-473, 476-480, 483, 505-510, 513-514, 517-525 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/filelister.py 27 16 41% 45-55, 58-59, 62-64 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/fileopener.py 28 15 46% 49-62, 68, 71-73, 83-89 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/grouping.py 149 112 25% 29-31, 34, 37-38, 41-43, 46-49, 84-90, 93-101, 104-112, 140-141, 144-146, 149-166, 218-234, 237-254, 257-276, 279-280, 283-294, 297-307, 310 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/routeddecoder.py 27 14 48% 41-46, 54, 57-60, 63-65 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/selecting.py 53 36 32% 48-65, 68-74, 77-80, 83-99, 102-106 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/streamreader.py 15 7 53% 27-28, 31-36 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/utils.py 19 11 42% 29-30, 33-47, 50 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/callable.py 21 7 67% 15, 49-52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combinatorics.py 24 11 54% 44-49, 52-54, 59-60, 63 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combining.py 54 35 35% 35-42, 45-51, 54-56, 80-87, 90-96, 99-101 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/grouping.py 39 25 36% 40-46, 49-59, 62-70 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/utils.py 17 9 47% 32-42, 45, 48 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/common.py 116 91 22% 21-22, 30-39, 50-76, 80-90, 94-101, 138-173, 183, 186-187, 190-192, 195-198, 201-202, 205, 208, 211, 214 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/decoder.py 160 130 19% 33-60, 98-111, 140-141, 144-185, 188, 195-209, 216-230, 238-245, 248-251, 254, 262-266, 277-278, 283-285, 289-290, 293-307, 310-324, 327 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/dataset.py 94 53 44% 51, 54, 164, 167, 184-185, 188, 191, 207-212, 215-220, 223, 226-235, 239-241, 255-256, 259-262, 265-269, 284-285, 288-290, 293, 310-314 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/distributed.py 50 38 24% 64-94, 97-121, 124, 135 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/graph.py 63 51 19% 16, 21-70, 74-75, 80-90 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/graph_settings.py 45 37 18% 14-20, 24-35, 39-56, 60-68 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/sampler.py 111 74 33% 31, 34, 73, 76, 79, 97-107, 113-115, 118-133, 136, 149-150, 153-154, 157, 183-193, 196-197, 200, 223-232, 236-255, 262-265 /home/admin/.local/lib/python3.8/site-packages/torch/utils/dlpack.py 27 10 63% 98-119 /home/admin/.local/lib/python3.8/site-packages/torch/utils/hooks.py 121 96 21% 16-18, 21-23, 26, 29-35, 38, 41, 50-51, 55-59, 75-82, 85-89, 92-96, 99-118, 123-151, 154-161, 164-193 /home/admin/.local/lib/python3.8/site-packages/torch/utils/model_zoo.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/throughput_benchmark.py 51 33 35% 6-23, 28-29, 33, 37, 44, 48, 53, 96-99, 108, 117, 150-156 /home/admin/.local/lib/python3.8/site-packages/torch/version.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/__init__.py 36 16 56% 16-17, 23-28, 45-47, 54, 74-80, 91, 95 /home/admin/.local/lib/python3.8/site-packages/torchvision/_internally_replaced_utils.py 38 20 47% 12, 16, 21-22, 30-49 /home/admin/.local/lib/python3.8/site-packages/torchvision/_utils.py 20 11 45% 11-16, 24-32 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/__init__.py 45 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/_optical_flow.py 194 147 24% 34-38, 41-44, 49, 53-72, 75, 78, 123-141, 156, 159, 185-202, 217, 220, 250-270, 285, 288, 321-361, 376, 379, 406-426, 431, 446, 455-463, 468-474, 480-502 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/caltech.py 87 68 22% 43-74, 84-117, 121, 124, 127-137, 145, 170-191, 201-218, 222, 225, 228-232 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/celeba.py 92 73 21% 69-110, 117-130, 133-142, 145-152, 155-182, 185, 188-189 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/cifar.py 80 50 38% 60-92, 95-101, 111-123, 126, 129-135, 138-141, 144-145 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/cityscapes.py 80 64 20% 114-172, 183-199, 202, 205-206, 209-211, 214-221 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/clevr.py 46 30 35% 39-59, 62, 65-76, 79, 82-85, 88 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/coco.py 27 15 44% 33-37, 40-41, 44, 47-54, 57, 104 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/country211.py 24 14 42% 37-50, 53, 56-58 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/dtd.py 51 36 29% 44-74, 77, 80-89, 92, 95, 98-100 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/eurosat.py 24 15 38% 29-40, 43, 46, 50-54 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/fakedata.py 27 19 30% 34-38, 49-64, 67 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/fer2013.py 31 18 42% 37-51, 60, 63-72, 75 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/fgvc_aircraft.py 46 33 28% 51-88, 91, 94-103, 109-111, 114 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/flickr.py 78 58 26% 16-26, 29-32, 35-38, 41-52, 74-83, 93-105, 108, 130-140, 150-163, 166 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/flowers102.py 55 40 27% 51-74, 77, 80-89, 92, 95-102, 105-114 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/folder.py 100 77 23% 21, 33, 41-46, 62-105, 144-154, 185-190, 219, 229-236, 239, 247-249, 254-260, 264-269, 310-318 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/food101.py 46 31 33% 43-65, 70, 73-82, 85, 88, 91-93 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/gtsrb.py 44 31 30% 35-60, 63, 67-76, 79, 82-99 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/hmdb51.py 58 40 31% 78-112, 116, 119-138, 141, 144-151 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/imagenet.py 108 87 19% 43-55, 58-65, 69, 72, 76-87, 91-96, 108-150, 164-176, 194-212 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/inaturalist.py 113 95 16% 75-109, 114-133, 139-168, 179-196, 199, 210-219, 222, 225-241 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kinetics.py 100 72 28% 20, 116-156, 160-167, 176-197, 211-229, 236, 239, 242-248, 312-323 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kitti.py 58 39 33% 61-84, 105-109, 112-128, 131, 135, 139-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lfw.py 125 96 23% 42-58, 61-63, 66-72, 75-82, 85, 88, 91, 123-126, 129-144, 147-151, 161-170, 173, 205-207, 210-235, 245-255 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lsun.py 94 75 20% 18-31, 34-50, 53, 79-93, 96-136, 146-161, 164, 167 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py 243 152 37% 65-66, 70-71, 75-76, 80-81, 91-104, 107-111, 118-119, 122-128, 138-150, 153, 157, 161, 165, 168, 176-195, 198-199, 294-298, 302, 306, 310, 314, 318, 321, 324, 329-339, 421-428, 432-433, 437-438, 441, 444-461, 467-474, 478-486, 489, 493, 511-531, 535-540, 544-549 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/omniglot.py 47 32 32% 42-60, 63, 73-83, 86-89, 92-99, 102 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/oxford_iiit_pet.py 63 47 25% 50-87, 90, 93-112, 115-119, 122-126 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/pcam.py 47 35 26% 77-95, 98-100, 103-116, 119-121, 124-130 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/phototour.py 99 73 26% 92-109, 119-129, 132, 135, 138, 141-161, 165-174, 177-178, 184-206, 213-215, 223-228 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/places365.py 83 55 34% 73-83, 86-92, 95, 99, 103-108, 111-123, 126-139, 142-143, 146-156, 159, 162, 165-170 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/rendered_sst2.py 42 27 36% 44-57, 60, 63-72, 75, 78-81, 84-86 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sbd.py 59 39 34% 61-96, 99-100, 103-104, 110-116, 119, 122-123 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sbu.py 59 45 24% 36-58, 68-77, 81, 85-89, 93-114 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/semeion.py 43 28 35% 37-51, 61-73, 76, 79-83, 86-91 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/stanford_cars.py 49 38 22% 41-74, 77, 81-88, 91-111, 118-121 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/stl10.py 104 79 24% 55-88, 91-100, 111-126, 129, 132-145, 148-154, 157-161, 164, 168-176 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sun397.py 36 23 36% 36-51, 56, 59-68, 71, 74-76 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/svhn.py 44 30 32% 61-91, 101-113, 116, 119-122, 125-126, 129 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/ucf101.py 45 32 29% 71-101, 105, 108-118, 121, 124-130 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/usps.py 34 23 32% 52-70, 80-92, 95 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/utils.py 235 185 21% 38-45, 49-50, 54-63, 70-74, 78, 82-86, 90-100, 106-115, 130-167, 178-182, 195-199, 203-215, 229-280, 288-289, 299-302, 333-361, 377-393, 411-429, 440-450, 454, 466-485 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/video_utils.py 199 164 18% 28-29, 41-49, 62, 65, 68, 75, 122-140, 143-166, 169-173, 177-182, 185-193, 212-232, 245-255, 258, 261, 267, 274-279, 283-291, 306-379, 382-407, 411-423 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/vision.py 60 42 30% 38-54, 64, 67, 70-78, 81-82, 85, 90-91, 94-98, 101-102, 105-111 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/voc.py 94 61 35% 9-10, 79-127, 130, 158, 168-174, 203, 213-219, 223-237 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/widerface.py 95 76 20% 65-81, 94-103, 106, 109-110, 113-158, 161-169, 173-180, 183-194 /home/admin/.local/lib/python3.8/site-packages/torchvision/extension.py 48 12 75% 15, 27-28, 33, 49, 56-57, 66, 84-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/__init__.py 12 2 83% 9-10 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/_load_gpu_decoder.py 6 1 83% 6 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/_video_opt.py 154 124 19% 13, 31-32, 58-65, 70-71, 87-105, 111-120, 188-217, 226-252, 259-262, 335-369, 380-408, 418-423, 432-484, 490-505 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/image.py 72 48 33% 12-13, 45-48, 60-62, 82-85, 103-106, 121-124, 157-164, 182-188, 202-205, 227-230, 249-252, 256-257 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/video.py 207 181 13% 21-31, 41-42, 46, 81-139, 151-219, 225-234, 263-347, 351-356, 360-364, 384-415 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/video_reader.py 48 33 31% 9-10, 17-18, 24, 94-111, 125-133, 136, 151-152, 160, 179-181 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/__init__.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_api.py 66 38 42% 53-60, 63, 66, 86-107, 121-142 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_meta.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_utils.py 104 67 36% 51-64, 67-73, 83-89, 125-126, 132-142, 174-228, 236-240, 244-247, 252-256 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/alexnet.py 37 17 54% 19-37, 48-52, 106-116 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/convnext.py 131 84 36% 32-35, 46-60, 63-66, 77-79, 82-87, 101-167, 170-173, 176, 186-194, 299-308, 331-340, 361-370, 393-402 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/densenet.py 158 98 38% 36-46, 49-51, 55-58, 62-65, 69, 73, 78-94, 109-118, 121-125, 130-134, 164-211, 214-219, 227-238, 249-257, 360-362, 385-387, 410-412, 435-437 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/_utils.py 231 193 16% 22-23, 41-71, 87-119, 136-137, 140-144, 155-160, 163-181, 193-224, 238, 253-271, 286-301, 341-346, 359-386, 397-415, 420, 423-431, 447-449, 464-479, 484, 506-510, 521-538 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/anchor_utils.py 125 105 16% 40-50, 65-74, 77, 80, 85-113, 116-133, 163-182, 187-202, 206, 212-236, 239-247, 250-268 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/backbone_utils.py 82 64 22% 42-54, 57-59, 112-113, 125-146, 158-174, 193-194, 205-242 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/faster_rcnn.py 188 134 29% 38-40, 201-280, 293-296, 299-304, 322-341, 355-357, 360-369, 547-569, 613-644, 656-685, 739-751, 812-820 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/fcos.py 305 257 16% 49-52, 62-122, 129-131, 160-182, 185-199, 223-244, 247-268, 381-426, 432-435, 444-484, 489-553, 571-649, 747-769 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/generalized_rcnn.py 58 47 19% 29-36, 41-44, 60-118 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/image_list.py 10 4 60% 20-21, 24-25 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/keypoint_rcnn.py 93 63 32% 204-268, 273-283, 288-301, 304-305, 441-467 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/mask_rcnn.py 116 82 29% 203-265, 279-300, 312-323, 336-348, 481-503, 544-577 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/retinanet.py 321 258 20% 37-40, 44-49, 53-56, 71-75, 79, 86, 110-133, 145-150, 162-191, 195-209, 229-247, 259-264, 276-304, 308-322, 436-482, 487-490, 494-505, 509-569, 585-673, 794-819, 862-891 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/roi_heads.py 481 441 8% 28-49, 69-79, 91-94, 109-126, 131-162, 168-213, 220-231, 244-295, 300-326, 331-342, 347-360, 368-383, 389, 394-400, 405-425, 429-461, 466-471, 476-489, 522-548, 551-557, 560-566, 570-601, 605-610, 614-616, 620-628, 636-666, 676-725, 742-876 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/rpn.py 175 149 15% 29-41, 53-62, 73-79, 83-86, 90-111, 161-181, 184-186, 189-191, 197-229, 232-240, 250-297, 314-337, 363-391 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssd.py 273 223 18% 49-53, 58-60, 63, 71-73, 80-87, 90-103, 108-112, 117-121, 201-242, 248-251, 260-319, 327-410, 415-461, 466-535, 539-548, 552-568, 643-679 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssdlite.py 119 79 34% 33, 49-51, 74-78, 85-87, 90, 100-104, 109-113, 125-148, 152-161, 169-184, 262-328 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/transform.py 185 159 14% 14-16, 22, 32-71, 96-105, 110-146, 149-157, 165-166, 173-194, 200-218, 221-225, 228-245, 253-267, 270-275, 279-293, 297-309 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/efficientnet.py 265 159 40% 59, 76-81, 85, 100-102, 113-162, 165-169, 179-223, 226-230, 255-342, 345-352, 355, 366-374, 382-429, 754-757, 782-785, 810-813, 838-841, 866-869, 894-897, 930-933, 966-969, 1003-1006, 1040-1043, 1077-1080 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/googlenet.py 185 143 23% 42-100, 103-108, 112-163, 167-170, 173-181, 196-212, 218-224, 227-228, 239-246, 250-263, 268-270, 273-275, 319-342 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/inception.py 299 243 19% 38-93, 96-101, 105-155, 159-162, 165-173, 180-192, 195-208, 211-212, 217-224, 227-236, 239-240, 247-263, 266-282, 285-286, 291-300, 303-313, 316-317, 322-336, 339-360, 363-364, 371-378, 382-395, 400-402, 405-407, 457-475 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mnasnet.py 119 73 39% 38-45, 60-63, 70-77, 84-87, 93-94, 113-156, 159-162, 174-209, 306-314, 339-341, 366-368, 393-395, 420-422 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenet.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv2.py 105 72 31% 23-32, 43-78, 81-84, 112-181, 186-191, 194, 265-275 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv3.py 138 97 30% 29-33, 54-61, 65, 76-124, 127-130, 155-223, 226-233, 236, 242-285, 295-303, 398-401, 428-431 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/_utils.py 25 18 28% 10-17, 21-23, 33-45 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/raft.py 288 219 24% 31-59, 62-68, 75-92, 103-110, 120-143, 146-148, 151-159, 169-190, 193-202, 209-212, 215-220, 225, 236-255, 258-260, 270-273, 276, 286-291, 294-299, 310-320, 323-325, 339-349, 359-370, 374-400, 403-409, 449-460, 464-511, 747-798, 824-826, 878-880 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/googlenet.py 95 59 38% 27-28, 31-34, 37, 42-43, 46-47, 53-54, 58-71, 77-81, 84-94, 104-106, 178-207 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/inception.py 130 81 38% 28-29, 32-35, 38, 44-45, 48-49, 55-56, 59-60, 66-67, 70-71, 77-78, 81-82, 88-91, 94-112, 115-116, 122, 132-147, 150-160, 170-172, 250-276 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenet.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv2.py 62 33 47% 26-27, 30-33, 36-38, 49-51, 54-57, 60-64, 136-152 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv3.py 88 53 40% 34-36, 39, 42, 54-72, 86-87, 90-93, 104-106, 109-112, 115-122, 133-158, 231-234 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/resnet.py 120 70 42% 39-40, 43-57, 60-62, 67-70, 73-88, 91-95, 100-103, 106-112, 121-124, 135-149, 315-317, 364-366, 413-417, 456-460 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/shufflenetv2.py 86 43 50% 37-38, 41-49, 55-57, 60-63, 75-82, 99-113, 253-254, 306-307, 351-352, 396-397 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/utils.py 30 24 20% 8-18, 22-42, 48-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/regnet.py 273 142 48% 62, 81-105, 122-141, 144-148, 168-182, 195-200, 234-266, 276, 287-293, 307-374, 377-384, 393-402, 1123-1126, 1148-1151, 1173-1178, 1200-1205, 1227-1232, 1254-1259, 1281-1286, 1308-1313, 1335-1338, 1360-1363, 1389-1392, 1418-1421, 1447-1450, 1476-1479, 1505-1508 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/resnet.py 236 150 36% 42, 56, 73-87, 90-105, 128-141, 144-163, 178-223, 233-264, 268-282, 285, 295-303, 668-670, 693-695, 724-726, 755-757, 786-788, 813-817, 842-846, 870-874, 904-907, 937-940 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/_utils.py 27 18 33% 14-18, 21-37 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/deeplabv3.py 127 81 36% 50, 61-66, 71, 79-82, 87-101, 109-113, 121-128, 203-218, 257-273, 312-328, 365-381 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/fcn.py 64 36 44% 38-47, 103-110, 152-168, 210-226 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/lraspp.py 70 44 37% 38-41, 44-51, 56-68, 71-79, 83-93, 157-175 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/shufflenetv2.py 118 73 38% 29-40, 45-68, 90, 93-101, 112-151, 155-163, 166, 175-183, 304-306, 334-336, 364-366, 394-396 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/squeezenet.py 71 40 44% 20-27, 30-31, 38-92, 95-97, 106-114, 186-187, 216-217 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/swin_transformer.py 196 155 21% 36-40, 49-60, 94-160, 182-212, 222-227, 271-291, 294-296, 333-393, 396-402, 416-432, 530-532, 566-568, 602-604 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/vgg.py 118 58 51% 39-63, 66-70, 74-87, 99-106, 308-310, 333-335, 358-360, 383-385, 408-410, 433-435, 458-460, 483-485 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/video/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/video/resnet.py 153 96 37% 30, 41, 46, 64, 72, 83, 98-107, 110-120, 135-154, 157-169, 176, 187, 218-248, 251-263, 273-288, 300-308, 396-398, 432-434, 468-470 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/vision_transformer.py 223 155 30% 46-52, 64-75, 98-108, 111-119, 136-152, 155-157, 178-266, 269-287, 291-305, 318-337, 618-620, 651-653, 684-686, 717-719, 749-751, 784-838 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/__init__.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_box_convert.py 28 22 21% 17-25, 39-47, 61-63, 77-81 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_register_onnx_ops.py 34 16 53% 16-21, 25-44, 57-60 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_utils.py 59 49 17% 13-15, 19-25, 29-38, 45-69, 74-77, 82-84, 92-106 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/boxes.py 162 123 24% 73, 90, 106-112, 128-133, 148-165, 189-216, 232-235, 241-252, 269-273, 292-304, 320-337, 356-362, 367-381, 398-415 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ciou_loss.py 25 20 20% 45-71 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/deform_conv.py 65 50 23% 63-92, 126-151, 154-159, 170, 182-195 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/diou_loss.py 32 26 19% 45-57, 66-87 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/drop_block.py 68 52 24% 28-52, 74-102, 114-119, 129, 132-133, 145, 155 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/feature_pyramid_network.py 100 80 20% 33, 84-110, 122-134, 149-156, 163-170, 184-204, 218-220, 229-235, 243-249 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/focal_loss.py 18 14 22% 35-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/giou_loss.py 24 20 17% 43-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/misc.py 93 67 28% 27-33, 45-49, 56-62, 65, 85-114, 153, 202, 237-243, 246-250, 253-254, 282-298, 309-310, 313 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/poolers.py 123 92 25% 20-34, 45, 68-72, 80-85, 89-96, 101-107, 114-135, 140-144, 169-228, 278-288, 291-292, 295-296, 303-304, 325-331, 341 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_align.py 29 18 38% 46-57, 71-75, 78, 81-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_pool.py 28 17 39% 40-49, 58-61, 64, 67-68 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_align.py 31 18 42% 53-61, 78-83, 86, 89-97 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_pool.py 30 17 43% 42-51, 60-63, 66, 69-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/stochastic_depth.py 34 24 29% 26-44, 56-59, 62, 65-66 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/_pil_constants.py 18 8 56% 17-25 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/_presets.py 119 91 24% 24-26, 29, 32, 48-53, 56-62, 65-72, 75, 93-98, 101-118, 121-128, 131, 149-153, 156-162, 165-171, 174, 184-199, 202, 205 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/autoaugment.py 207 167 19% 16-90, 127-131, 136-221, 224, 249-253, 262-281, 284, 314-319, 322, 347-365, 368-377, 402-405, 408, 433-453, 456-463, 497-507, 510-531, 535, 539, 543, 552-601, 604-615 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional.py 505 409 19% 39-47, 71-76, 88-93, 105-110, 115, 120, 135-174, 193-209, 234-239, 259, 262-271, 275, 279, 283, 287-289, 292, 295-307, 310-315, 318-323, 327, 332, 355-360, 417-421, 424, 428, 432, 476-481, 501-506, 523-547, 581-585, 600-605, 623-633, 668-688, 703-708, 730-753, 779-797, 814-819, 836-841, 858-863, 897-902, 930-935, 961-995, 1042-1081, 1132-1214, 1232-1237, 1259-1264, 1283-1288, 1317-1355, 1370-1375, 1391-1399, 1414-1419, 1436-1441, 1458-1463, 1481-1486 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional_pil.py 261 192 26% 19, 26-33, 38-40, 45-50, 55-58, 63-66, 71-76, 81-86, 91-96, 101-120, 130-142, 153-222, 234-237, 249, 251, 254, 256-275, 278, 293-311, 322-327, 340-344, 355-360, 365-378, 383-385, 390-392, 397-399, 404-409, 414-416, 421-423 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py 555 501 10% 10, 14-15, 19-21, 25-28, 33-34, 38-44, 48-59, 63-65, 69-117, 121-123, 127-129, 133-142, 146-162, 166-173, 177-190, 194-219, 223-233, 237-253, 257-259, 263-298, 302-319, 326-350, 354-370, 374-426, 436-503, 515-542, 546-558, 562-571, 576-603, 619-629, 635-642, 653-675, 685-693, 704-722, 728-745, 749-755, 761-764, 768-790, 795-803, 808-817, 822-832, 836-855, 859-869, 874-891, 899-912, 916, 921-933, 937-960, 964-970 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/transforms.py 745 571 23% 88-90, 93-95, 98-103, 124, 134, 137, 147, 161, 164, 187-189, 192, 214-215, 226, 229-233, 255-259, 269, 272, 321-339, 349, 352-353, 369-371, 381, 384, 426-444, 454, 457, 468-471, 474, 477, 488-491, 494, 497-502, 526-529, 532-536, 539-545, 552-556, 563-566, 569-570, 573, 631-642, 645-653, 663-678, 681, 695-697, 707-709, 712, 726-728, 738-740, 743, 765-785, 796-807, 822-842, 845, 878-899, 914-943, 953-954, 957-962, 994-996, 1006, 1009, 1043-1046, 1056, 1059, 1082-1102, 1112-1130, 1133-1138, 1166-1171, 1175-1191, 1216-1223, 1233-1247, 1250-1257, 1294-1326, 1335-1336, 1346-1355, 1358-1367, 1424-1481, 1496-1519, 1528-1540, 1543-1552, 1572-1574, 1584, 1587, 1607-1609, 1619-1622, 1625, 1657-1678, 1697-1720, 1730-1750, 1753-1761, 1782-1799, 1812, 1822-1823, 1826-1827, 1831-1840, 1844-1848, 1852-1859, 1873-1875, 1885-1887, 1890, 1905-1908, 1918-1920, 1923, 1938-1941, 1951-1953, 1956, 1971-1974, 1984-1986, 1989, 2003-2005, 2015-2017, 2020, 2034-2036, 2046-2048, 2051 /home/admin/.local/lib/python3.8/site-packages/torchvision/utils.py 276 245 11% 57-132, 154-160, 201-266, 295-346, 378-418, 436-453, 468-488, 501-535, 539-540, 562 /home/admin/.local/lib/python3.8/site-packages/torchvision/version.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_dist_ver.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_monitor.py 45 12 73% 42-45, 63, 72-86, 95 /home/admin/.local/lib/python3.8/site-packages/tqdm/_tqdm_pandas.py 10 6 40% 12-24 /home/admin/.local/lib/python3.8/site-packages/tqdm/asyncio.py 53 36 32% 24-34, 37, 40-52, 55, 62-67, 75-81, 88 /home/admin/.local/lib/python3.8/site-packages/tqdm/auto.py 14 3 79% 27-28, 37 /home/admin/.local/lib/python3.8/site-packages/tqdm/autonotebook.py 12 5 58% 16-20 /home/admin/.local/lib/python3.8/site-packages/tqdm/cli.py 188 173 8% 17-40, 54-97, 151-311 /home/admin/.local/lib/python3.8/site-packages/tqdm/gui.py 10 1 90% 181 /home/admin/.local/lib/python3.8/site-packages/tqdm/std.py 696 347 50% 46-49, 127-128, 154-161, 165, 169-184, 187-211, 390-398, 418, 437-439, 538, 542-547, 565-566, 573-574, 581-582, 587-588, 615-646, 650-660, 702-705, 715-717, 722-726, 735-756, 761, 805-950, 969, 975, 984, 987-995, 998-1002, 1031, 1034, 1037, 1044, 1047, 1080-1083, 1107-1111, 1114, 1122-1130, 1133-1134, 1137, 1140-1146, 1170-1172, 1226, 1229, 1251, 1259, 1280, 1284, 1292-1294, 1307-1308, 1312-1324, 1340, 1344-1345, 1355-1359, 1371-1381, 1393-1395, 1399-1401, 1416-1432, 1438-1440, 1444-1445, 1451, 1454, 1483-1486, 1489, 1495, 1498, 1515-1520, 1525 /home/admin/.local/lib/python3.8/site-packages/tqdm/utils.py 175 92 47% 22, 28-31, 70, 81-96, 108-109, 112-113, 119, 122, 128, 131, 134, 142, 146-149, 169-170, 176, 179, 196-209, 231-248, 254-260, 268-269, 273-278, 389-398 /home/admin/.local/lib/python3.8/site-packages/tqdm/version.py 8 6 25% 4-9 /home/admin/.local/lib/python3.8/site-packages/typing_extensions.py 1102 646 41% 142, 153, 156, 160-165, 170-171, 175-176, 215, 220-222, 225-227, 239-241, 249, 255, 282-289, 293, 298, 305, 316-320, 323, 332, 339-341, 345-351, 374-376, 413-421, 426-432, 436, 479, 482, 500-503, 509, 516, 519-520, 542, 559-580, 585-611, 618, 629-650, 680-686, 695, 704, 713, 722, 730, 741, 752, 758-763, 781-783, 797-861, 867, 921-955, 960, 974-976, 980, 996, 1000, 1005-1025, 1059-1067, 1072-1075, 1087-1091, 1094-1096, 1099, 1103, 1108-1112, 1115, 1153, 1157-1168, 1171, 1179-1180, 1209-1216, 1229-1238, 1243, 1246-1258, 1277-1286, 1291-1293, 1306, 1318-1331, 1334, 1339-1340, 1348, 1351, 1366, 1369, 1372-1374, 1389, 1392, 1395-1397, 1403-1428, 1486, 1490, 1494-1508, 1511-1519, 1522, 1525, 1528, 1532, 1547-1549, 1552-1553, 1557, 1561, 1565, 1573-1582, 1587-1588, 1591-1603, 1608, 1625, 1628-1673, 1678-1680, 1738-1741, 1744, 1747, 1750, 1753, 1756, 1759, 1762, 1765, 1769, 1773, 1792, 1796, 1813, 1817, 1843, 1847-1848, 1850-1885, 1890-1892, 1970-1973, 1976-1990, 1998-2000, 2011-2023, 2074, 2077-2085, 2088, 2091, 2094, 2097, 2100-2101, 2105, 2123-2124, 2128, 2150, 2155, 2230-2240, 2244, 2274-2281, 2285, 2334-2370, 2391, 2394-2404, 2411-2447, 2452-2453, 2476-2519, 2523, 2553, 2576-2579, 2587, 2604, 2607-2614, 2619-2630, 2633, 2636, 2642-2646, 2650, 2654, 2690-2706, 2709-2711, 2714, 2718-2726, 2731, 2734-2742, 2745, 2748, 2755, 2758-2768, 2772-2773, 2789, 2810-2814, 2818, 2838, 2841, 2844, 2847-2849 /home/admin/.local/lib/python3.8/site-packages/uritemplate/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/uritemplate/api.py 11 3 73% 43, 66, 85 /home/admin/.local/lib/python3.8/site-packages/uritemplate/orderedset.py 59 40 32% 28-32, 35, 38, 42-47, 52-55, 59-63, 67-71, 74-78, 81-83, 86, 89-92 /home/admin/.local/lib/python3.8/site-packages/uritemplate/template.py 48 33 31% 30-34, 72-83, 86, 89, 92-94, 97, 102-120, 147, 169 /home/admin/.local/lib/python3.8/site-packages/uritemplate/variable.py 195 171 12% 56-71, 74, 77, 89-122, 130-143, 153-190, 204-240, 250-295, 304-325, 356-386, 392-399, 403, 407, 411-413, 417-419 /home/admin/.local/lib/python3.8/site-packages/zipp.py 123 73 41% 28, 47-50, 62, 73-75, 78-79, 82, 89-92, 100-111, 121-124, 127-130, 223-224, 232-242, 246, 250, 254, 258, 262, 265-266, 269-270, 273, 276, 279, 282, 285, 288-291, 294, 297, 300-301, 307-312 /home/admin/mtr/.credentials/credentials.py 1 0 100% /home/admin/workarea/git/Velours/python/dev/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/dev/angular_coefficients_to_crops.py 371 201 46% 12-41, 49-51, 66-68, 79-91, 97-104, 109-179, 185-186, 188-189, 194, 232, 237-238, 271, 290-304, 312-313, 316-317, 331, 346, 351-354, 365-367, 406, 417-504 /home/admin/workarea/git/Velours/python/dev/conditional_crop_copy.py 414 151 64% 14, 24, 29, 35-39, 51, 55-56, 83, 93-94, 103-104, 106, 113-114, 118, 121, 126-129, 136, 150, 157, 163, 167, 172-173, 177, 179, 182-231, 240-241, 270-274, 276-279, 289-295, 298-299, 304-306, 311-312, 321-323, 340, 343-344, 358-370, 400, 408, 418-419, 422-423, 435, 437, 442-445, 456-458, 471-473, 506, 509, 512, 524-540 /home/admin/workarea/git/Velours/python/dev/generate_new_image.py 477 245 49% 27-28, 31-36, 72-77, 83-84, 87-88, 94, 99-106, 135-147, 156-157, 162-175, 223-227, 245-248, 251, 254-261, 268, 279-280, 296-297, 302-312, 316-333, 337-357, 362-370, 404-405, 407-408, 411-412, 414, 420, 423-431, 437-438, 440, 459-466, 498-508, 543-578, 585-586, 593, 611, 655-656, 664-671, 678-771 /home/admin/workarea/git/Velours/python/dev/poly_crop_reduction.py 238 157 34% 9-20, 40, 45, 54-56, 58-59, 117, 119-120, 127-168, 172-226, 229-244, 260-310, 330-381 /home/admin/workarea/git/Velours/python/file_uploader.py 73 35 52% 14-15, 23-24, 28-30, 36-37, 54-56, 62-64, 70-80, 84-95, 98 /home/admin/workarea/git/Velours/python/misc/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/misc/split_time_score.py 928 711 23% 30-47, 51-60, 67-224, 311, 314-329, 353-354, 368-376, 386-387, 412-413, 420-424, 432, 441-442, 447-448, 457-463, 468-499, 515, 528, 533-535, 560-594, 600-644, 775-796, 801, 812-814, 837, 839, 842, 860, 862, 882-943, 957-1805 /home/admin/workarea/git/Velours/python/mtr/Gan2/pre_ops.py 265 175 34% 14-16, 19-21, 24, 27, 30, 33-35, 51-52, 76-86, 89-105, 141-201, 215-293, 317-320, 322-325, 327-334, 337, 361-415 /home/admin/workarea/git/Velours/python/mtr/Rubbia_Report.py 577 544 6% 95-134, 141-202, 211-225, 234-252, 260-300, 309-315, 323-338, 352-379, 387-394, 397-429, 437-446, 450-465, 471-476, 481-527, 530-541, 545-607, 612-673, 677-771, 774-791, 797-804, 808-828, 832-930 /home/admin/workarea/git/Velours/python/mtr/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/cnn/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/cnn/classifier_new.py 289 77 73% 24-39, 124, 184, 213-215, 221, 225-227, 240, 245-254, 263-270, 277, 291, 337-338, 354, 356-357, 365-369, 378-379, 395, 427-430, 456-457, 465, 485, 507, 522-523, 536-550 /home/admin/workarea/git/Velours/python/mtr/cnn/ordonner.py 73 39 47% 20-29, 36, 44, 54, 66-101, 104 /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelConfig.py 63 45 29% 15-18, 23-26, 30, 35-48, 54-68, 73-77, 81-85, 88-95, 98, 101 /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelData_queries.py 180 77 57% 19, 26, 35-42, 61, 66-82, 102, 111-133, 144, 148, 150, 154-159, 161-162, 164, 166, 168, 171-172, 205-206, 226-227, 231-232, 240-256, 293 /home/admin/workarea/git/Velours/python/mtr/database_queries/CachePhotoData_queries.py 364 90 75% 35-37, 56, 58, 63, 88, 101-110, 117, 119, 133, 139-141, 149-150, 160, 169, 182-184, 201-202, 225-227, 253-255, 275-277, 288-289, 304, 327-333, 337, 350-352, 378-387, 397-398, 489-490, 501, 517, 524-529, 537, 594-597, 618-620, 634-654 /home/admin/workarea/git/Velours/python/mtr/database_queries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/admin_queries.py 457 380 17% 32-39, 44-50, 56-66, 71-87, 92, 96-99, 102-116, 120-135, 138-143, 146-148, 156-165, 168-177, 180-187, 192-206, 211-227, 232-250, 254-271, 275-291, 294-295, 298-299, 302-308, 326-331, 334-337, 340-349, 353-357, 360-367, 370-376, 379-389, 392-399, 402-404, 407-414, 417-430, 433-444, 447-469, 476, 485, 488-495, 498-504, 507-510, 514-518, 522-540, 543-548, 551-556, 559-564, 568-577, 580-588, 591-600, 603-612, 615-621, 625-648 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_admin_tools.py 87 53 39% 27-28, 30-34, 45, 61, 64, 76-92, 97-105, 110-137, 142, 147-163, 166-171 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_queries.py 291 200 31% 22-42, 45-49, 52-71, 74-82, 85-91, 94-98, 101-106, 109-117, 124-134, 139-148, 152-159, 162-172, 176-197, 200-220, 223-233, 236-248, 253-261, 267-283, 301-363, 379, 382, 390, 411, 414, 423, 489-511, 514-528 /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/objet_thcl.py 146 114 22% 32-50, 56-65, 70-77, 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, 120, 123, 126, 129-132, 138-139, 143-147, 152-171, 177-196, 200-202, 205-212, 226-232, 237-269 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_queries.py 1475 763 48% 44, 57, 69, 87, 95-97, 102, 121-127, 131-133, 149-153, 166-170, 186-295, 305-308, 311-316, 319-326, 329-348, 351-359, 364, 369, 391, 394-397, 406-409, 420-461, 481, 484-487, 503-523, 528, 534-541, 551-572, 577, 584, 587, 590, 597, 630, 646, 663, 744, 751, 764, 801, 831, 868, 873-874, 894-897, 901-906, 909-914, 918-921, 960, 964, 968-969, 977, 979, 989-990, 994, 1006, 1011, 1016-1017, 1024-1044, 1083, 1090, 1135, 1144, 1158, 1164, 1179, 1191-1195, 1206, 1217, 1228-1232, 1250-1310, 1342-1349, 1366-1374, 1384-1396, 1400-1407, 1410-1416, 1419-1427, 1432-1439, 1449, 1456, 1460-1473, 1483, 1486-1493, 1505-1506, 1516-1517, 1524-1534, 1541, 1548-1554, 1556, 1568-1573, 1585-1590, 1602-1607, 1613-1617, 1624-1630, 1635-1649, 1655-1663, 1671, 1678-1680, 1685, 1691-1710, 1714-1729, 1733-1753, 1757-1775, 1781-1803, 1808-1818, 1821-1829, 1835-1852, 1858-1868, 1877, 1881, 1891-1908, 1912-1920, 1923-1927, 1945, 1950, 1966, 1979, 1990, 2022, 2047, 2075-2083, 2087-2095, 2100-2109, 2112-2121, 2124-2129, 2137, 2157, 2175, 2200, 2204-2211, 2257-2281, 2293-2295, 2299-2308, 2313-2331, 2334-2342, 2364, 2366, 2369-2371, 2385-2386, 2394, 2413-2444, 2449-2477, 2481-2487, 2491-2510, 2514-2523, 2527-2531, 2540, 2547, 2575, 2578, 2582-2609, 2644, 2650, 2654-2690, 2706-2708, 2713-2728, 2739-2775, 2784-2820 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_utils/util_portfolio_hashtag_ids.py 213 95 55% 18-19, 23-24, 29-128, 143, 146-147, 150, 154, 160-161, 169, 217-218, 223-226, 238-239, 276-277, 301, 304-305, 310, 313-314, 328, 331-332, 336, 339-340 /home/admin/workarea/git/Velours/python/mtr/database_queries/descriptor_queries.py 354 238 33% 23-42, 56, 63, 67, 73, 77, 82-103, 106-145, 163, 166, 169, 184, 218, 226-264, 270-301, 304-321, 333, 338, 349-352, 360-387, 390-400, 404-407, 412-435, 444-471, 474-477, 480-495, 499-556 /home/admin/workarea/git/Velours/python/mtr/database_queries/general_queries.py 148 50 66% 12-13, 33-34, 36-37, 45-46, 49, 59-61, 74, 83-95, 107, 122-133, 137-140, 151, 163-167, 182 /home/admin/workarea/git/Velours/python/mtr/database_queries/graph_nodes_queries.py 77 60 22% 28-34, 38-54, 59-130 /home/admin/workarea/git/Velours/python/mtr/database_queries/hashtag_queries.py 158 103 35% 46-50, 64-65, 72, 80-91, 94-110, 113-125, 128-133, 136-142, 145-155, 158-165, 168-183, 188, 196-207, 211-218, 221-226, 229-235 /home/admin/workarea/git/Velours/python/mtr/database_queries/mission_queries.py 520 478 8% 26-38, 42-250, 255-272, 275-314, 317-414, 418-430, 433-445, 448-460, 463-475, 479-491, 495-507, 510-522, 525-548, 551-552, 555-567, 570-582, 586-622, 625-644, 647-662, 665-671, 674-681, 697-741, 747-756, 773-799, 803-810, 815-822, 828-838, 841-843, 848-855, 859-873 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_insert_queries.py 105 81 23% 30-71, 79, 84-91, 94-103, 106-113, 118-138, 141-145, 149-163, 173-192, 203-218 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_retrieval_queries.py 558 401 28% 12, 51-61, 71-75, 96-101, 107-123, 129-142, 148-161, 180-181, 188, 199-200, 212, 217, 221, 224-226, 229-231, 234-237, 248, 254, 261-266, 269, 271, 274, 277-278, 288-326, 332-348, 351-425, 428-475, 481-492, 495-544, 547-548, 555-605, 608-631, 634-668, 675, 683, 695, 703, 711, 714, 719, 724-742, 750, 756-758, 761, 770, 774-776, 781-787, 790, 796-800, 805-826, 832-849, 852-864, 868-922, 946, 968, 975-986, 1002 /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py 511 261 49% 39, 41, 56-72, 88, 97-114, 122, 130, 134, 140-158, 164, 170, 172-174, 176, 179, 188, 192, 198, 202-212, 215-222, 225-235, 240-255, 261, 270, 274-284, 287-299, 302-311, 321, 325, 339, 351, 354, 360-361, 365, 369-375, 378-383, 389, 393-397, 400-410, 425, 441, 447, 473-497, 516-517, 525, 548-571, 576-584, 589, 594, 598-608, 616, 620-622, 630, 637, 642-662, 684, 717-748, 750-759, 780-781, 783-786, 793-794, 796-799 /home/admin/workarea/git/Velours/python/mtr/datou/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/calcul_brightness_image.py 76 46 39% 4, 7-8, 22-24, 42-68, 71-78, 81-88, 91-98 /home/admin/workarea/git/Velours/python/mtr/datou/count_refus.py 64 7 89% 15, 61-69, 72-73, 94 /home/admin/workarea/git/Velours/python/mtr/datou/darker_image.py 39 4 90% 15, 19, 24, 60 /home/admin/workarea/git/Velours/python/mtr/datou/data_augmentation_imgaug.py 244 194 20% 16, 19-22, 25, 27-176, 188-193, 203, 241-303 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py 1753 999 43% 43-44, 75-101, 108-109, 112-113, 117-118, 153, 158, 188-189, 206-209, 212-213, 227-229, 241-243, 246-247, 277, 302-306, 311, 328-332, 335, 375, 397-398, 434, 462-463, 481-495, 509-516, 522-573, 580-624, 652-653, 660-663, 674-677, 700-702, 721, 729-730, 738-739, 773-776, 810, 818, 821, 833-853, 863-870, 886-942, 968-1161, 1165, 1171-1174, 1178-1180, 1185-1186, 1191-1192, 1196-1198, 1201-1281, 1326-1334, 1348, 1351-1353, 1365-1376, 1379-1397, 1401-1450, 1454-1500, 1505-1545, 1550-1556, 1564, 1567, 1570-1571, 1574-1577, 1580, 1583, 1586-1588, 1596, 1605, 1613, 1621, 1635, 1657, 1662-1672, 1675-1678, 1682-1735, 1739-1742, 1747-1785, 1791, 1795-1804, 1811, 1820, 1828-1865, 1878-1894, 1903-1919, 1924-1941, 1945-1957, 1966-1969, 1975-1985, 1993-1996, 2002-2006, 2010, 2016, 2021-2024, 2032-2034, 2037-2041, 2045-2068, 2108, 2119, 2126-2129, 2148-2149, 2154-2203, 2206-2241, 2258-2264, 2267-2277, 2280-2282, 2313, 2316-2328, 2334-2369, 2371, 2376, 2422, 2428, 2432, 2440, 2442, 2448, 2451, 2453, 2455, 2461, 2463, 2465, 2467, 2469, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2505, 2511, 2515, 2519, 2521, 2525, 2528, 2534, 2536, 2538, 2540, 2542, 2550, 2552, 2555, 2559, 2562, 2564, 2570, 2572, 2574, 2577, 2584, 2586, 2588, 2590, 2592, 2594, 2596, 2598, 2603, 2605, 2607, 2609, 2611, 2617, 2619, 2621, 2635, 2638, 2640, 2642, 2644, 2648, 2650, 2656, 2658, 2660, 2663-2667, 2701-2703, 2718-2720, 2736, 2742-2744, 2746, 2751, 2760-2762, 2771-2774, 2783-2787, 2803, 2808, 2811, 2819-2833, 2837-2843, 2855-2873 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_object.py 478 150 69% 16-23, 35-37, 46, 51-62, 101-122, 178, 194, 212, 215, 222-246, 252-290, 317, 335, 363-369, 374, 376, 385, 389, 396, 400, 412, 495, 499-500, 512-513, 522-523, 570, 579, 589, 616, 635-652, 660, 675-679, 688-689, 694, 723-743, 747-771 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_data_increase.py 204 104 49% 32, 34-35, 93, 102, 125-162, 214-216, 221-294, 297-339 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_save.py 1287 805 37% 24, 33-38, 45, 49-96, 101-145, 150, 155, 160-178, 183, 200-260, 269-305, 316, 324-325, 339-344, 347, 349, 361, 368-369, 374-436, 445-486, 489-553, 569, 580, 582, 593, 595, 604-608, 613-619, 655, 669-670, 674, 676, 695, 712-717, 722, 726-728, 738-744, 747-761, 764-779, 783-809, 813-864, 883, 885, 902, 917, 919, 936, 962-965, 971-976, 979, 981, 1006-1008, 1012-1014, 1047-1078, 1086-1087, 1095, 1098, 1102, 1106-1116, 1138-1140, 1155-1157, 1172-1175, 1194-1198, 1223-1253, 1257-1279, 1295-1332, 1338-1357, 1362-1387, 1393-1457, 1472-1500, 1523-1534, 1538-1619, 1625, 1631-1632, 1654-1655, 1667, 1671, 1675, 1677, 1683, 1689-1690, 1694, 1696, 1698, 1703, 1705, 1708, 1710, 1712, 1716, 1719, 1721, 1730-1739, 1743-1745, 1749-1769, 1775-1783, 1786-1818, 1821-1836, 1850, 1854, 1858-1861 /home/admin/workarea/git/Velours/python/mtr/datou/datou_local_cache_db.py 157 117 25% 62-70, 73-84, 88-102, 105-113, 117-122, 126-136, 139-143, 167-175, 178-194, 197-201, 204-205, 214-218, 233-257, 287-301, 304-307, 311 /home/admin/workarea/git/Velours/python/mtr/datou/datou_step_finale.py 325 253 22% 9-77, 82-130, 135-351, 370-372, 375-376, 395, 413-426, 430, 435, 439-443, 473 /home/admin/workarea/git/Velours/python/mtr/datou/detect_blur_image.py 109 72 34% 12-15, 18-20, 24-34, 55-64, 77, 87-145 /home/admin/workarea/git/Velours/python/mtr/datou/image_blanchir.py 30 2 93% 27, 31 /home/admin/workarea/git/Velours/python/mtr/datou/image_temperature.py 22 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_deprecated.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_end_or_aggreg.py 484 469 3% 17, 19, 21, 24, 27, 34-274, 288-767, 908-918 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_initialisation.py 372 326 12% 15-244, 249-267, 284-289, 304, 313-314, 317-331, 337-346, 358-360, 376-558 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_post_processing.py 1061 486 54% 52, 61-65, 72-77, 83-95, 99, 115-119, 141-142, 149-153, 161, 163, 179, 197-198, 228, 234-235, 256, 259-264, 273-279, 327-328, 356-363, 369, 371-449, 454-458, 481-482, 497-503, 506, 511-513, 538, 541-542, 547, 555, 568, 600-601, 604-605, 610-612, 615, 638-647, 653-655, 665, 675-681, 684-685, 689-694, 700-701, 704-705, 714-721, 724-732, 735-736, 752-757, 763-797, 802-815, 821-823, 843-845, 850-857, 874-924, 932-1006, 1011-1081, 1122-1124, 1128-1129, 1189, 1210-1231, 1243, 1249-1255, 2536-2538, 2541-2542, 2545-2552, 2562, 2569-2581, 2584, 2588-2592, 2599-2606, 2667-2669, 2762-2763, 2777-2778, 2789-2791, 2798-2808, 2816, 2848, 2859 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py 1402 793 43% 34-89, 124, 127, 133, 136-137, 150, 157-161, 172-177, 188-193, 233-235, 260-275, 281, 283, 289, 350, 354, 362-376, 391-399, 408-422, 425-426, 428-430, 432, 438, 440, 447, 485, 490-491, 497, 506, 510-696, 703-838, 843-885, 898-899, 908, 913-920, 928-929, 932-934, 956-960, 1003-1010, 1014-1021, 1024-1026, 1034, 1076-1077, 1087, 1095-1105, 1109-1111, 1119-1133, 1142-1143, 1156, 1174-1176, 1180, 1199-1200, 1204, 1220-1231, 1242, 1245-1246, 1251, 1255, 1259-1306, 1322-1323, 1344-1345, 1362-1459, 1485, 1502, 1508-1551, 1580-1581, 1586, 1596, 1620, 1623-1624, 1663-1666, 1670, 1677-1678, 1683, 1686, 1693-1699, 1703-1708, 1719-1733, 1779, 1812-1815, 1854-1856, 1929-1930, 1933-1934, 1939, 1948, 1954, 1959-1960, 1980-1985, 1989-2177 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py 1963 1250 36% 40, 47-59, 75, 84-86, 89, 98, 104, 139, 143, 149-150, 162-174, 211, 232, 253-257, 280-308, 325-326, 331, 334-335, 338, 350, 386-387, 392-393, 401, 410-411, 415, 427-556, 560-628, 637, 651-652, 663-664, 669, 697, 702, 710, 724, 729, 736, 745, 749, 755-761, 764, 803-807, 816, 819-823, 827, 830, 832-845, 855-889, 924, 927-929, 937-943, 970, 982, 992-1000, 1005, 1009, 1016-1026, 1032-1077, 1098, 1103-1106, 1109-1111, 1118-1137, 1161, 1163, 1166-1187, 1196-1272, 1279-1466, 1470-1499, 1503-1579, 1586-1674, 1678-1855, 1867, 1914, 1921-1924, 1931-1933, 1936, 1967-1968, 1972, 1977-1987, 1994-1995, 2016-2017, 2031-2078, 2132, 2165-2168, 2213-2215, 2222-2236, 2239-2248, 2253-2254, 2257-2266, 2269-2290, 2292-2293, 2357-2369, 2373-2418, 2440-2441, 2456, 2458, 2460, 2462, 2468, 2476, 2479, 2495, 2506, 2510, 2515-2619, 2626-2814, 3022-3033, 3038, 3041-3042, 3047-3049, 3052, 3076-3078, 3087, 3098-3110, 3121-3122, 3135, 3165, 3181, 3186-3193, 3452-3526, 3530-3569 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_send_or_copy.py 554 378 32% 19-195, 200-268, 273-332, 336-379, 397, 415, 424, 427-428, 430, 437, 444-449, 456, 462, 485-486, 493-623, 665-666, 671, 675-676, 680, 689-692, 700-716, 719-720, 728-741, 749, 751-754, 770, 809, 814-816, 833-834, 839 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_sort.py 193 163 16% 12-115, 125-127, 143-144, 161, 178-183, 189-287, 291-305 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_util.py 298 130 56% 16-17, 21, 26, 40, 47-48, 70, 97-98, 106, 125-126, 143-155, 179-183, 194-196, 214, 219, 224-231, 241-257, 261-284, 289, 293-294, 297-300, 303-305, 307-309, 319-324, 327-333, 366-371, 395, 405, 409-411, 423-467 /home/admin/workarea/git/Velours/python/mtr/datou/merge_rubbia.py 50 46 8% 12-36, 40-86 /home/admin/workarea/git/Velours/python/mtr/datou/send_mail_dechet.py 227 129 43% 19, 21, 27-28, 60, 66-119, 126-131, 146, 155, 157, 164-170, 175-179, 183-188, 191-193, 195-197, 207-221, 230, 234, 249-251, 255-259, 263, 269, 282-340, 344-345, 350, 354-355 /home/admin/workarea/git/Velours/python/mtr/lib/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/fotonower_connect.py 322 216 33% 73, 78-85, 88-90, 96-119, 123-184, 187-213, 220-221, 225, 230-231, 233-240, 252-253, 258-261, 267-279, 282, 285, 288-290, 307, 318, 323-325, 329, 332-335, 338-384, 389-412, 415-433, 436-461 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py 299 238 20% 35-43, 49-298, 304-342, 359, 373-374, 383-387, 402, 423-429, 444-549 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_segment.py 67 16 76% 40, 87, 107-117, 173, 190-191, 196-197, 222 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/prepare_maskdata.py 359 81 77% 28, 51, 62, 78, 83, 120-121, 140-141, 145-146, 148-149, 154-155, 164, 176-177, 187, 195, 198, 202, 218-220, 241-243, 246, 269-270, 290-293, 315, 317-318, 321, 326-332, 335-342, 347-348, 360-369, 391-393, 400-403, 411-414, 424-427, 429, 460-462, 502-504, 513-514, 545 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/svm_subroutines.py 69 63 9% 21-43, 50-99, 104-136 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/class_split_time_score.py 506 157 69% 48-49, 54, 73, 77, 85, 96, 114, 138-139, 145-146, 155, 237, 261, 267, 270, 283-292, 295, 298, 322, 325, 328, 331-342, 352-356, 367, 380-385, 412, 442, 444, 486, 498, 501, 513, 561-566, 570, 579, 595-599, 604-610, 615-621, 626-642, 645, 648, 651-667, 670-673, 676-679, 682-685, 688-695, 698-732 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/lib_split_time_score.py 1938 923 52% 19-36, 42, 78, 84-85, 96-104, 108-115, 125-136, 139-141, 182-185, 207-231, 236-411, 416-647, 654-729, 745-747, 752-776, 784-864, 868-886, 897-1001, 1006-1088, 1103, 1108, 1115, 1121-1134, 1139-1206, 1209-1250, 1255-1264, 1267-1342, 1345-1363, 1368-1405, 1410-1434, 1439-1458, 1686-1691, 1694-1712, 1759-1763, 1812, 1835-1855, 1939-1940, 1990, 2019, 2041-2096, 2166, 2169-2173, 2185, 2208, 2221, 2242-2243, 2265-2269, 2351, 2417, 2422, 2448-2452, 2538-2540, 2575-2576, 2607-2608, 2622-2624, 2641-2668, 2750-2751, 2768, 2815-2817, 2834-2836, 2870-2871, 2879-2892, 2907-2908, 2915, 2937-2938, 2945, 2953-2957, 2963-2967, 2977, 2990-2994, 3005, 3049-3100, 3108-3116, 3143-3145, 3164, 3170-3171, 3180-3182, 3184, 3211-3213, 3219, 3231-3232, 3240-3261, 3268, 3314, 3320-3389, 3398-3456, 3487-3488, 3534-3536, 3570, 3625, 3638, 3671-3716, 3748-3754, 3822-3823, 3848, 3854-3855, 3861, 3874-3875, 3889, 3896, 3911-3929, 3939-3957, 3964, 3968-4002 /home/admin/workarea/git/Velours/python/mtr/mem_info.py 76 30 61% 33-34, 41, 49, 59-63, 72, 95-124 /home/admin/workarea/git/Velours/python/mtr/monitor_sys.py 131 53 60% 40, 44, 47-50, 52, 61, 65-68, 98, 102, 104, 108, 110, 112, 114, 116, 124, 131, 143-144, 150, 162, 164-167, 170-194 /home/admin/workarea/git/Velours/python/mtr/ses_mailer.py 55 34 38% 35-36, 42-44, 47-85 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/flip_images.py 241 88 63% 20-91, 98-105, 111-114, 138-139, 155, 164-171, 177-180, 204-205, 220, 236, 243, 246, 253-254, 263, 268, 306-307, 311, 333, 338, 349, 378 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/image_utils.py 328 225 31% 21-28, 37-52, 88, 91-113, 121, 129, 142, 144-162, 181-191, 194-236, 242-253, 265-298, 301-314, 343, 348-354, 363-365, 368-381, 385-397, 401-441, 446-465, 470-473, 476-484 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/rotate_crop_and_images.py 894 306 66% 62, 65, 80-81, 97, 120, 139-140, 150-151, 176, 208, 212, 217-218, 223, 227-230, 239-241, 262, 268, 275, 297-310, 318-319, 333-334, 359-360, 370, 400-401, 412-413, 442, 457, 460, 481-482, 494, 497-498, 501, 521-524, 528, 531, 534, 543-546, 553-554, 571-572, 579-580, 584, 603, 606, 609, 618, 624, 634, 679-681, 690, 712-725, 733, 751, 794-831, 835, 884, 904-906, 911, 913-914, 920-924, 957-961, 964-967, 989, 1005-1013, 1062-1070, 1074, 1093, 1098, 1102-1109, 1140, 1180-1190, 1198, 1200, 1204, 1207-1212, 1235, 1259, 1274, 1282-1283, 1300-1304, 1308, 1323-1383, 1394-1512 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/simple_image_editor.py 2091 1596 24% 24-25, 43-51, 60-81, 86-126, 131-134, 140-324, 329-332, 335-359, 365-387, 391-422, 429-446, 451-469, 475-485, 492-598, 605-613, 619-793, 798-815, 821-853, 859-907, 910-911, 916-936, 942-972, 979-1100, 1109-1145, 1151-1183, 1189-1227, 1232-1251, 1259-1567, 1575-1639, 1643-1654, 1660-1683, 1690-1756, 1762-1828, 1832-1907, 1913-1990, 2023-2024, 2035, 2041-2042, 2044-2045, 2057-2064, 2077, 2081, 2097, 2102, 2113-2120, 2127-2128, 2137, 2145-2146, 2172, 2176, 2181-2194, 2216, 2223, 2233-2234, 2239, 2244, 2250, 2262, 2303, 2315, 2328-2331, 2335, 2349-2355, 2379-2384, 2395-2421, 2431-2465, 2479-2741, 2852-2854, 2868, 2939, 2944, 2949, 2952-2953, 2959, 2961, 2964, 2979, 3007-3008, 3041-3042, 3080, 3102, 3140-3156, 3164-3189, 3200-3304, 3339, 3359-3360, 3362-3363, 3387, 3409-3417, 3508-3540, 3562, 3579-3590, 3594-3600, 3603-3682, 3685-3688, 3691-3723, 3726-3752, 3757-3819, 3825-3877 /home/admin/workarea/git/Velours/python/mtr/split_time_gps_score.py 723 514 29% 14-26, 36-68, 78-89, 106-107, 119-141, 151-182, 232, 236, 244-256, 299-300, 311, 314, 360-379, 399-401, 451-484, 488-509, 526-554, 558-596, 614, 631-634, 646, 649-650, 658, 665-671, 691-700, 713-721, 732-799, 812-865, 869-877, 881-893, 903-938, 948-987, 999-1034, 1046-1070, 1079, 1092-1093, 1097-1310 /home/admin/workarea/git/Velours/python/mtr/tfhub2/data_ops.py 228 196 14% 18-26, 29-37, 41-49, 52-60, 63-68, 71-82, 85-93, 96-117, 120-129, 132-149, 152-183, 187-189, 193-211, 215-229, 232-249, 271-302 /home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py 162 51 69% 42, 44-52, 86, 99, 110-111, 115, 128-131, 139, 173-181, 186-231, 246, 281-282, 293, 295 /home/admin/workarea/git/Velours/python/mtr/tfhub2/foto_datasets.py 242 131 46% 23-25, 41-51, 64-66, 82, 85, 95-108, 117-119, 122-123, 131, 136-137, 139-140, 153-168, 173-240, 246, 251, 256, 264, 271, 287-322, 341, 343, 366, 379-381, 386, 391, 395-401 /home/admin/workarea/git/Velours/python/mtr/tfhub2/fotonower_data_ops.py 111 94 15% 19-23, 26-30, 33-38, 41-44, 48-84, 91-144, 148-182, 186-192 /home/admin/workarea/git/Velours/python/mtr/tfhub2/ops.py 201 170 15% 29-31, 40, 44, 48, 60, 65-72, 76-129, 139-151, 155-167, 171-177, 182-186, 191-202, 207-210, 219-244, 254-280, 290-319, 324-326, 334-343, 346-348, 351-358, 362-373, 377-394 /home/admin/workarea/git/Velours/python/mtr/utils/MTRMongoClient.py 99 87 12% 21-92, 97-208, 213-241 /home/admin/workarea/git/Velours/python/mtr/utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cd.py 11 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cdn/copy_to_ovh.py 14 2 86% 16, 27 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/s3_bucket_manager.py 112 88 21% 33-40, 43-48, 51-54, 57-69, 75-84, 97-104, 119-125, 128-132, 140-159, 162-166, 169-175, 179-182, 185-186, 189-190 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/swift_upload_manager.py 151 73 52% 44, 47-48, 54, 63, 72-73, 76-79, 105, 107, 125-127, 130, 133-142, 145-156, 163, 180-184, 187-193, 197-210, 213, 216-217, 223-239 /home/admin/workarea/git/Velours/python/mtr/utils/general_util.py 57 32 44% 11-12, 20-27, 30, 33-57, 61-63, 69-70, 75, 86-90 /home/admin/workarea/git/Velours/python/mtr/utils/kmean_cloud_storage.py 15 5 67% 19-20, 23, 26, 29 /home/admin/workarea/git/Velours/python/mtr/utils/load_caffe.py 61 26 57% 23, 29, 43, 48, 55, 61-62, 65, 70, 76-94 /home/admin/workarea/git/Velours/python/mtr/utils/prepare_photo_learning.py 201 125 38% 14-15, 62-81, 89, 94-97, 103, 114, 123-124, 131, 137, 140, 154, 156-158, 176, 189-232, 238-365 /home/admin/workarea/git/Velours/python/mtr/utils/upload_batch.py 58 19 67% 37-39, 46-47, 55-58, 68-78 /home/admin/workarea/git/Velours/python/mtr/utils/utils_timer.py 11 3 73% 13-15 /home/admin/workarea/git/Velours/python/prod/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/prod/caffe_vision.py 1390 1154 17% 47, 68-72, 77-81, 110-112, 118-121, 131-132, 143, 149, 156-170, 173-175, 181, 184, 189, 192, 213-214, 220-274, 285, 313-315, 321, 326, 333-337, 340-344, 347-351, 360-367, 381, 394-395, 400, 410-416, 419-421, 431-573, 577-592, 595-602, 606-759, 763-782, 787-810, 814-877, 883-889, 894-901, 907-926, 934-947, 954-958, 964-981, 986-1001, 1008-1028, 1039, 1052, 1074-1076, 1082, 1085, 1095, 1099, 1103, 1116-1121, 1126-1333, 1401-2359 /home/admin/workarea/git/Velours/python/prod/cod/main_cod.py 559 276 51% 16-24, 27-43, 46, 49-50, 54, 60-90, 95-96, 107-108, 129, 134-137, 147-150, 156, 165-167, 169-171, 176-178, 184, 198, 206-209, 215, 219-220, 258-259, 269, 316-336, 342-359, 364, 370-372, 375-378, 383-416, 440-450, 471, 483-505, 524-525, 527-529, 533-538, 541-542, 569, 580, 596-597, 607, 619-625, 630-647, 656-657, 666-667, 670-671, 696-801 /home/admin/workarea/git/Velours/python/prod/memo/SLA_RUBBIA.py 546 200 63% 13-14, 115-116, 125-135, 149-164, 185-188, 196-223, 227, 229-230, 234-238, 257-258, 267-268, 270-273, 276, 287-295, 308-310, 317-320, 363, 383-427, 444-450, 452-453, 460-470, 484, 492, 505, 515-517, 525-527, 536, 540, 543-547, 549-550, 553, 561, 568-573, 620-622, 625-628, 643-646, 661-662, 666-667, 671-672, 675, 681-683, 693-695, 719, 725, 727-728, 734, 745, 749, 760-761, 774 /home/admin/workarea/git/Velours/python/prod/memo/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/prod/memo/example_of_unwanted_materials.py 90 58 36% 13-15, 43-62, 86-100, 104-126, 130-163, 167-170, 174-179 /home/admin/workarea/git/Velours/python/prod/memo/lib_sla.py 333 75 77% 19, 49, 68, 75, 79, 99-115, 132, 135, 185, 194-195, 230-231, 234, 240, 249-250, 257-258, 261-262, 280-282, 286-288, 293, 306-308, 368, 376-381, 385-399, 419, 453, 487, 504, 521-525, 544-547, 550-554, 585, 596-601 /home/admin/workarea/git/Velours/python/prod/memo/memo.py 833 286 66% 46, 52, 76, 82, 110, 159, 230-231, 236-238, 245-247, 249-252, 255-257, 260, 285-286, 304, 307, 310-311, 316-318, 321-329, 336-337, 342-343, 349-350, 358-361, 371-372, 407, 433, 441, 443, 455-535, 591-593, 607-610, 613-617, 679-714, 762-764, 778-781, 848-850, 864-874, 915-917, 924-925, 957-963, 986, 1022-1027, 1041, 1060, 1073, 1079, 1082, 1088, 1094, 1099, 1104, 1109, 1111-1112, 1118-1172, 1198-1249, 1254-1261, 1292-1293, 1315-1319, 1322-1336 /home/admin/workarea/git/Velours/python/prod/non_supervised_algorithm.py 131 59 55% 40, 58, 72-73, 155, 160, 165-166, 175-264 /home/admin/workarea/git/Velours/python/prod/vision_faster_rcnn.py 244 171 30% 13-16, 61-89, 98, 133-137, 145-172, 178-219, 223-360, 378-380, 398, 401, 415-458 /home/admin/workarea/git/Velours/python/tests/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/tests/cache_photo_data_test.py 74 20 73% 41, 49-62, 84, 90-94 /home/admin/workarea/git/Velours/python/tests/cod_main_test.py 75 12 84% 32, 57, 92, 98-101, 122, 128, 131, 134, 138 /home/admin/workarea/git/Velours/python/tests/datou_test.py 1923 636 67% 37, 43-45, 51-52, 58-60, 66-70, 101-104, 108, 122-126, 157-159, 186-188, 194-197, 203-205, 213-215, 221-224, 230-232, 241, 271-276, 283, 302-304, 317, 333, 352-354, 360-364, 378-416, 448-450, 457, 464-466, 499-501, 510-513, 540-542, 546, 549, 553, 568-569, 579, 582-583, 588, 626-628, 631, 645-646, 656, 660-661, 674-675, 681, 707-709, 729, 731, 737-739, 743-745, 777-779, 790-791, 814, 816, 822-824, 828-830, 866-868, 886-887, 915-917, 935-936, 962, 968-970, 983-987, 998, 1025, 1031-1033, 1046-1050, 1066, 1071, 1099-1101, 1111, 1118, 1174, 1291, 1298-1301, 1317, 1323, 1333, 1374, 1380-1383, 1807-1809, 1846, 1852-1855, 1874-1878, 1920, 1926-1929, 1935, 1948, 1959-2067, 2100-2102, 2119-2123, 2128-2131, 2163-2165, 2171-2172, 2192, 2194, 2200-2202, 2206-2208, 2246, 2255-2257, 2267, 2287, 2293, 2295, 2301-2302, 2305-2306, 2311-2312, 2315-2320, 2326-2330, 2357-2361, 2367-2369, 2382-2386, 2399, 2427, 2433-2435, 2444-2446, 2448-2450, 2459, 2517-2520, 2542-2546, 2552-2553, 2556-2558, 2583, 2594-2596, 2601-2602, 2610-2611, 2636, 2656-2659, 2663-2665, 2669-2671, 2675-2677, 2711-2713, 2717, 2723, 2731-2733, 2743, 2777-2779, 2782-2784, 2794-2796, 2798-2800, 2803-2805, 2837-2839, 2842-2844, 2854-2856, 2858-2860, 2863-2865, 2881-2935, 2960, 2966-2968, 2981-2983, 2985-2987, 2996, 3042-3044, 3051, 3066-3121, 3149-3151, 3154-3156, 3166-3168, 3170-3172, 3175-3177, 3180-3182, 3211-3213, 3247, 3253-3255, 3261, 3264-3266, 3303-3304, 3311-3313, 3319, 3322-3324, 3360-3362, 3365-3367, 3407-3409, 3412, 3415-3417, 3423-3425, 3428, 3431-3433, 3460-3462, 3465, 3472, 3476-3477, 3483-3490, 3517, 3521-3525, 3532, 3537-3540, 3567, 3571-3575, 3582, 3587-3590, 3682-3684, 3700-3701 /home/admin/workarea/git/Velours/python/tests/mask_test.py 167 107 36% 27-29, 38, 63-68, 79-80, 85-87, 91-130, 135-227, 249-250, 265-282, 288 /home/admin/workarea/git/Velours/python/tests/python_tests.py 221 37 83% 37-39, 94-95, 99, 105, 107, 112, 124, 128, 130, 134, 141, 143, 147, 149, 151, 153, 155, 157, 163, 165, 167, 172, 188, 202, 208, 222-225, 247, 288-289, 370, 377 /home/admin/workarea/git/raspi-fotonower-x/python/__init__.py 0 0 100% /home/admin/workarea/git/raspi-fotonower-x/python/lib/__init__.py 0 0 100% /home/admin/workarea/git/raspi-fotonower-x/python/lib/conn_sqlite.py 669 587 12% 22-34, 37-51, 54-67, 71-86, 93-113, 120-130, 133-134, 137-138, 141-147, 150-153, 156-160, 165-179, 182-198, 201-220, 223-225, 228-236, 240-247, 254-268, 272-273, 276-281, 285-291, 299-339, 342-355, 361-392, 404-419, 426-457, 461-462, 465-467, 473-481, 489-495, 499-505, 508-519, 522-533, 536-542, 546-553, 556-623, 626-647, 650-651, 654-659, 662-679, 682-704, 707-713, 716-726, 730, 735-750, 754-766, 769-780, 783-796, 799-809, 815-833, 837-848, 852-866, 869-873, 876-884, 889, 892-893, 896-898, 902-904, 915-917, 921-976, 980-990, 993-1007, 1011-1013 /home/admin/workarea/git/raspi-fotonower-x/python/to_upload_status.py 2 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py 8 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py 40 20 50% 36, 44, 64-98 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/detector.py 98 88 10% 38-54, 72-99, 115-123, 140-179, 191-216 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/io.py 177 101 43% 9-14, 24-34, 41-46, 53-55, 61-63, 71-81, 88-92, 119, 151, 161, 168-185, 200, 218, 251, 255-263, 279-280, 307-309, 311, 339-346, 364-392 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/net_spec.py 131 95 27% 47-53, 64-79, 87-88, 94, 97, 105-119, 122-127, 130-133, 136-164, 174, 177, 180, 183, 186, 189-196, 205-213, 222-225 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/proto/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/proto/caffe_pb2.py 506 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/pycaffe.py 156 61 61% 41-44, 52-54, 64-69, 110, 115-116, 123, 128, 154-182, 234-258, 266-269, 322-328 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/bbox_transform.py 44 15 66% 11-28, 32 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/config.py 111 43 61% 218-223, 229-255, 259-263, 267-285 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/nms_wrapper.py 9 2 78% 16, 20 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/test.py 155 89 43% 70-72, 85-100, 107, 130-135, 148, 155, 167, 179, 183-184, 190-205, 211-227, 231-298 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/nms/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/rpn/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/rpn/generate_anchors.py 38 6 84% 100-105 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/rpn/proposal_layer.py 79 10 87% 36-38, 47, 79-80, 86, 162-163, 167 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/utils/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/utils/blob.py 24 10 58% 33-45 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/utils/timer.py 18 1 94% 32 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/tools/_init_paths.py 11 0 100% /home/admin/workarea/install/segment-anything/segment_anything/__init__.py 3 0 100% /home/admin/workarea/install/segment-anything/segment_anything/automatic_mask_generator.py 128 35 73% 107-110, 118, 121, 167, 175, 179, 212-220, 314, 335-372 /home/admin/workarea/install/segment-anything/segment_anything/build_sam.py 23 2 91% 15, 28 /home/admin/workarea/install/segment-anything/segment_anything/modeling/__init__.py 5 0 100% /home/admin/workarea/install/segment-anything/segment_anything/modeling/common.py 23 0 100% /home/admin/workarea/install/segment-anything/segment_anything/modeling/image_encoder.py 118 2 98% 308-313 /home/admin/workarea/install/segment-anything/segment_anything/modeling/mask_decoder.py 59 2 97% 105, 175 /home/admin/workarea/install/segment-anything/segment_anything/modeling/prompt_encoder.py 94 16 83% 95-100, 104-105, 118-123, 158-159, 162 /home/admin/workarea/install/segment-anything/segment_anything/modeling/sam.py 47 13 72% 97-131 /home/admin/workarea/install/segment-anything/segment_anything/modeling/transformer.py 98 0 100% /home/admin/workarea/install/segment-anything/segment_anything/predictor.py 78 29 63% 53, 133-166, 214, 219, 241, 251-256 /home/admin/workarea/install/segment-anything/segment_anything/utils/__init__.py 0 0 100% /home/admin/workarea/install/segment-anything/segment_anything/utils/amg.py 197 46 77% 47, 51, 53, 57, 66, 70, 216, 219-232, 242, 251, 262-264, 274-291, 295-300, 310, 318, 344 /home/admin/workarea/install/segment-anything/segment_anything/utils/transforms.py 42 12 71% 52-53, 62-63, 74-81, 90-91 /usr/lib/python3/dist-packages/babel/__init__.py 3 0 100% /usr/lib/python3/dist-packages/babel/_compat.py 51 26 49% 34-56, 63-67, 77-80 /usr/lib/python3/dist-packages/babel/core.py 329 213 35% 27, 70-77, 102-105, 157-168, 192-193, 216-219, 261-331, 334-337, 343, 346, 349-355, 358, 363-365, 378-393, 418-421, 432-435, 446-449, 468, 482, 494, 506, 515, 531, 542, 554, 566, 580, 592, 604, 615-618, 626, 632, 641, 650, 659, 673, 687, 704, 720, 731, 740, 749, 759, 773, 787, 801, 814, 836, 851, 867, 884, 896, 907, 918, 932, 962-977, 1026-1040, 1083-1115, 1131-1133 /usr/lib/python3/dist-packages/babel/localedata.py 112 86 23% 34-39, 49-55, 66, 96-123, 138-156, 167, 170, 181-189, 198-201, 204, 207, 210-221, 224, 227, 230 /usr/lib/python3/dist-packages/babel/plural.py 280 181 35% 43-73, 112-119, 122-123, 137-139, 149-150, 158, 161, 164-166, 184-189, 211-229, 242-252, 272, 292, 306-315, 334-349, 353, 358-359, 363, 367, 371, 375, 413-421, 425-432, 435-438, 441-444, 447-461, 464-471, 474-478, 481-484, 487-495, 498, 520-521, 538, 550-553, 567-583, 598-603, 620, 623-629 /usr/lib/python3/dist-packages/bcrypt/__about__.py 12 0 100% /usr/lib/python3/dist-packages/bcrypt/__init__.py 58 43 26% 43-53, 60-94, 98-112, 116-146, 150-151 /usr/lib/python3/dist-packages/certifi/__init__.py 2 0 100% /usr/lib/python3/dist-packages/certifi/core.py 5 0 100% /usr/lib/python3/dist-packages/chardet/__init__.py 11 7 36% 31-39 /usr/lib/python3/dist-packages/chardet/big5freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/big5prober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/chardistribution.py 117 83 29% 49-59, 65-68, 72-82, 88-98, 103, 110, 115-118, 125-129, 134-137, 144-148, 153-156, 163-167, 172-175, 182-189, 194-197, 204-214, 219-222, 229-233 /usr/lib/python3/dist-packages/chardet/charsetgroupprober.py 72 61 15% 34-37, 40-47, 51-55, 59-63, 66-83, 86-106 /usr/lib/python3/dist-packages/chardet/charsetprober.py 55 36 35% 40-42, 45, 49, 52, 56, 59, 63-64, 81-101, 115-145 /usr/lib/python3/dist-packages/chardet/codingstatemachine.py 28 18 36% 56-61, 64, 69-78, 81, 84, 88 /usr/lib/python3/dist-packages/chardet/compat.py 10 4 60% 26-29 /usr/lib/python3/dist-packages/chardet/cp949prober.py 16 6 62% 36-41, 45, 49 /usr/lib/python3/dist-packages/chardet/enums.py 35 1 97% 62 /usr/lib/python3/dist-packages/chardet/escprober.py 58 45 22% 43-56, 59-67, 71, 75, 78-81, 84-101 /usr/lib/python3/dist-packages/chardet/escsm.py 17 0 100% /usr/lib/python3/dist-packages/chardet/eucjpprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/euckrfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euckrprober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/euctwfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euctwprober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/gb2312freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/gb2312prober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/hebrewprober.py 77 48 38% 155-162, 165-171, 175-176, 179, 193, 223-253, 259-280, 284, 289-292 /usr/lib/python3/dist-packages/chardet/jisfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/jpcntx.py 81 61 25% 124-129, 132-141, 144-168, 171, 175-178, 181, 185-186, 190, 193-210, 214-231 /usr/lib/python3/dist-packages/chardet/langbulgarianmodel.py 5 0 100% /usr/lib/python3/dist-packages/chardet/langcyrillicmodel.py 13 0 100% /usr/lib/python3/dist-packages/chardet/langgreekmodel.py 5 0 100% /usr/lib/python3/dist-packages/chardet/langhebrewmodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/langthaimodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/langturkishmodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/latin1prober.py 52 29 44% 98-101, 104-106, 110, 114, 117-128, 131-145 /usr/lib/python3/dist-packages/chardet/mbcharsetprober.py 44 33 25% 40-43, 46-51, 55, 59, 62-88, 91 /usr/lib/python3/dist-packages/chardet/mbcsgroupprober.py 14 3 79% 43-54 /usr/lib/python3/dist-packages/chardet/mbcssm.py 41 0 100% /usr/lib/python3/dist-packages/chardet/sbcharsetprober.py 75 60 20% 40-51, 54-61, 65-68, 72-75, 78-122, 125-132 /usr/lib/python3/dist-packages/chardet/sbcsgroupprober.py 19 8 58% 45-73 /usr/lib/python3/dist-packages/chardet/sjisprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/universaldetector.py 124 104 16% 82-92, 100-109, 125-218, 229-286 /usr/lib/python3/dist-packages/chardet/utf8prober.py 43 29 33% 39-42, 45-47, 51, 55, 58-74, 77-82 /usr/lib/python3/dist-packages/chardet/version.py 2 0 100% /usr/lib/python3/dist-packages/dbus/__init__.py 17 2 88% 67, 95 /usr/lib/python3/dist-packages/dbus/_compat.py 3 0 100% /usr/lib/python3/dist-packages/dbus/_dbus.py 63 24 62% 46, 95-100, 112-115, 123, 136, 147, 159, 164-173, 195, 231 /usr/lib/python3/dist-packages/dbus/_expat_introspect_parser.py 38 29 24% 34-37, 40-45, 48-56, 59-65, 84-87 /usr/lib/python3/dist-packages/dbus/bus.py 133 72 46% 64-86, 95-100, 137-164, 168-171, 180, 183-186, 225, 229-236, 238, 254-255, 302-303, 319-320, 330-333, 343-346, 359, 375, 385, 399, 416, 430, 445 /usr/lib/python3/dist-packages/dbus/connection.py 327 269 18% 45, 52, 63, 73-124, 128, 132, 136, 141-157, 160, 164, 168-180, 183-238, 241-244, 292, 314-328, 402-429, 432-458, 466-517, 521, 528-551, 566-613, 624, 627, 633, 635, 644-649, 654-660, 669 /usr/lib/python3/dist-packages/dbus/exceptions.py 59 25 58% 52, 60-68, 72-76, 79-90, 100, 107, 114, 121, 129, 136 /usr/lib/python3/dist-packages/dbus/lowlevel.py 3 0 100% /usr/lib/python3/dist-packages/dbus/mainloop/__init__.py 9 0 100% /usr/lib/python3/dist-packages/dbus/mainloop/glib.py 8 3 62% 41-43 /usr/lib/python3/dist-packages/dbus/proxies.py 200 134 33% 32-33, 57-61, 64-72, 75, 87-103, 106-141, 150-163, 211-215, 220-225, 229, 236, 253-268, 365, 374-377, 388-390, 393-405, 408-420, 423-430, 433-442, 445-448, 473-486, 489, 511-515, 542-545, 549-552, 562-564, 567 /usr/lib/python3/dist-packages/dbus/types.py 6 2 67% 15-16 /usr/lib/python3/dist-packages/debtcollector/__init__.py 6 2 67% 44-47 /usr/lib/python3/dist-packages/debtcollector/_utils.py 93 59 37% 55, 59, 66-69, 80, 94-101, 111-124, 129-134, 142-180 /usr/lib/python3/dist-packages/debtcollector/removals.py 170 112 34% 25, 30-34, 82-93, 96-107, 110-115, 118-123, 126-133, 136-142, 192-242, 258-261, 271-296, 316-333 /usr/lib/python3/dist-packages/debtcollector/renames.py 16 1 94% 42 /usr/lib/python3/dist-packages/entrypoints.py 148 68 54% 37, 40, 47-48, 53-54, 57, 73, 82-83, 100, 103, 111, 127-156, 160-181, 197, 208-214, 221-225, 242-243 /usr/lib/python3/dist-packages/idna/__init__.py 2 0 100% /usr/lib/python3/dist-packages/idna/core.py 282 244 13% 37-41, 44, 47, 50, 55-57, 62-64, 70-124, 129-131, 136-140, 145-146, 151-194, 199-235, 240-267, 272-292, 297-313, 318-339, 346-372, 377-400 /usr/lib/python3/dist-packages/idna/idnadata.py 4 0 100% /usr/lib/python3/dist-packages/idna/intranges.py 29 24 17% 18-29, 32, 35, 40-53 /usr/lib/python3/dist-packages/idna/package_data.py 1 0 100% /usr/lib/python3/dist-packages/iso8601/__init__.py 1 0 100% /usr/lib/python3/dist-packages/iso8601/iso8601.py 79 50 37% 22, 76-134, 146, 148-149, 163-172, 192, 195, 213-214 /usr/lib/python3/dist-packages/keyring/__init__.py 3 0 100% /usr/lib/python3/dist-packages/keyring/backend.py 84 20 76% 85-87, 90-91, 99, 108, 119, 133-140, 151, 157, 165, 168, 196-198 /usr/lib/python3/dist-packages/keyring/backends/OS_X.py 46 25 46% 30, 33-43, 47-58, 62-69 /usr/lib/python3/dist-packages/keyring/backends/SecretService.py 62 40 35% 10, 13-15, 31-39, 46-59, 64-72, 77-84, 89-94 /usr/lib/python3/dist-packages/keyring/backends/Windows.py 95 59 38% 16-18, 22-24, 56, 60, 64-71, 74-84, 87-94, 97-103, 106-114, 117-126, 129-138, 151, 155, 159, 163-165 /usr/lib/python3/dist-packages/keyring/backends/_OS_X_API.py 151 136 10% 24-345 /usr/lib/python3/dist-packages/keyring/backends/__init__.py 0 0 100% /usr/lib/python3/dist-packages/keyring/backends/chainer.py 36 18 50% 47-50, 53-57, 60-64, 67-70 /usr/lib/python3/dist-packages/keyring/backends/fail.py 8 2 75% 19-25 /usr/lib/python3/dist-packages/keyring/backends/kwallet.py 95 55 42% 14-18, 35, 38-39, 46-48, 51-52, 55-75, 78-95, 100-107, 112-115, 121-126 /usr/lib/python3/dist-packages/keyring/core.py 80 42 48% 27, 34, 41-51, 57, 63, 69, 75, 79, 124-127, 135-138, 159-176, 181-185 /usr/lib/python3/dist-packages/keyring/credentials.py 37 14 62% 16, 20, 28-29, 33, 37, 46-47, 52-55, 59, 63 /usr/lib/python3/dist-packages/keyring/errors.py 26 1 96% 60 /usr/lib/python3/dist-packages/keyring/py27compat.py 35 6 83% 7-8, 17, 42, 49-50 /usr/lib/python3/dist-packages/keyring/py32compat.py 5 2 60% 3-4 /usr/lib/python3/dist-packages/keyring/py33compat.py 10 4 60% 28-31 /usr/lib/python3/dist-packages/keyring/util/__init__.py 13 2 85% 34-35 /usr/lib/python3/dist-packages/keyring/util/platform_.py 31 7 77% 8, 12, 16-18, 47-50 /usr/lib/python3/dist-packages/keyring/util/properties.py 14 6 57% 51-53, 56-58 /usr/lib/python3/dist-packages/keystoneauth1/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneauth1/_fair_semaphore.py 43 28 35% 38-39, 47-54, 58-60, 63-77, 87-88, 93-104 /usr/lib/python3/dist-packages/keystoneauth1/_utils.py 33 18 45% 30-33, 38-43, 57-59, 73-75, 82-83 /usr/lib/python3/dist-packages/keystoneauth1/adapter.py 167 106 37% 133, 167, 169, 175, 181-196, 199-248, 263, 278-282, 298-303, 323, 341-345, 349, 366, 383, 386, 389, 392, 395, 398, 401, 412-447, 463-515, 537-552, 558, 564 /usr/lib/python3/dist-packages/keystoneauth1/discover.py 534 450 16% 48, 58, 99-162, 202-252, 269-340, 352-355, 371-391, 410-417, 421-435, 445-470, 480-495, 519-524, 534-535, 554-576, 589-657, 670-675, 693-703, 719-720, 745-787, 804-806, 823-832, 837, 842, 850, 858, 866, 874, 879, 910-930, 934-956, 960-965, 970, 999-1001, 1048-1062, 1073-1086, 1097-1106, 1112-1190, 1194-1249, 1261-1370, 1380, 1419-1461, 1493-1498, 1533 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/__init__.py 10 0 100% /usr/lib/python3/dist-packages/keystoneauth1/exceptions/auth.py 15 8 47% 25-32 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/auth_plugins.py 23 10 57% 43-45, 59-62, 89-93 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/base.py 6 2 67% 23-24 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/catalog.py 8 0 100% /usr/lib/python3/dist-packages/keystoneauth1/exceptions/connection.py 16 2 88% 50-51 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/discovery.py 16 1 94% 39 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/http.py 147 55 63% 72-83, 254-259, 394-460 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/oidc.py 14 0 100% /usr/lib/python3/dist-packages/keystoneauth1/exceptions/response.py 7 2 71% 24-25 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/service_providers.py 7 3 57% 22-24 /usr/lib/python3/dist-packages/keystoneauth1/plugin.py 48 29 40% 33, 62, 95-100, 124-131, 149-154, 176-180, 193, 209, 224, 239, 254, 268, 287, 304, 315 /usr/lib/python3/dist-packages/keystoneauth1/session.py 539 443 18% 36-37, 65-70, 81-86, 91-106, 114, 118, 124-131, 138-139, 142-153, 162-195, 208-222, 238-240, 247-248, 251-254, 257-260, 352-388, 392-399, 403, 407, 410, 413-429, 434-441, 452-459, 464-517, 522-580, 593-623, 751-983, 1001-1107, 1115, 1123, 1131, 1139, 1147, 1155, 1158-1165, 1182-1183, 1205, 1220-1225, 1241-1242, 1257-1258, 1284-1285, 1325-1343, 1353-1354, 1371-1372, 1389-1390, 1397, 1401, 1416-1451 /usr/lib/python3/dist-packages/keystoneclient/__init__.py 15 0 100% /usr/lib/python3/dist-packages/keystoneclient/_discover.py 137 102 26% 36-70, 78-80, 86-88, 95-106, 125-132, 142, 161-183, 199-241, 255-262, 274-275, 307-312, 329 /usr/lib/python3/dist-packages/keystoneclient/access.py 435 199 54% 52-85, 88-89, 121, 128, 146-149, 157, 165, 177, 185, 196, 207, 215, 223, 231, 239, 247, 268, 276, 284, 292, 302, 310, 318, 328, 344, 355, 372, 388, 396, 404, 412, 420, 428, 440, 449, 456-458, 465-470, 473, 477-480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524-544, 553-561, 565, 569, 573, 577, 581, 586, 590-610, 614-615, 619-620, 629-638, 647-656, 660, 664, 668, 672-675, 679-682, 700-705, 708, 712, 720, 728-733, 737-742, 746, 750, 754, 758-760, 766, 776-778, 782-784, 799-803, 815, 819, 823, 827, 836-845, 854-864, 868, 872, 876-879, 883-886 /usr/lib/python3/dist-packages/keystoneclient/auth/__init__.py 4 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/base.py 82 44 46% 46-48, 63-67, 86-93, 126, 159-164, 186, 215, 230, 245, 257, 267, 285-297, 315-318, 328-329, 344-347, 366-374 /usr/lib/python3/dist-packages/keystoneclient/auth/cli.py 29 21 28% 43-65, 87-95 /usr/lib/python3/dist-packages/keystoneclient/auth/conf.py 28 14 50% 41, 57, 83-93, 123-132 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/__init__.py 12 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/identity/base.py 123 69 44% 29, 74-78, 86-90, 98-102, 110-114, 122-126, 134-138, 146-150, 158-162, 206, 215-228, 250-254, 270-274, 312-360, 363, 366, 395-414, 418-420 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/__init__.py 4 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/base.py 74 44 41% 30, 63-73, 78, 83, 112, 118, 125, 134-180, 183-186, 190-192 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/password.py 33 19 42% 23, 46-52, 55-67, 77-79, 83-86 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/token.py 21 10 52% 22, 34-35, 38-42, 46-48 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v2.py 108 62 43% 41-49, 56-61, 66, 71, 74-94, 131-144, 149, 154, 159, 164, 167-174, 178-181, 186-197, 213-214, 219, 224, 227-229, 233-239 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/__init__.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/base.py 107 24 78% 78, 91-105, 149, 158, 163, 165, 167, 174-179, 185, 194-195, 221-222, 227, 261-263 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/federated.py 35 17 51% 46-48, 52-65, 70-79, 82, 106-116 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/password.py 29 9 69% 42, 48-49, 78-89, 93-96 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/token.py 17 6 65% 30-31, 53, 57-65 /usr/lib/python3/dist-packages/keystoneclient/base.py 274 193 30% 38-49, 58-61, 66, 72-86, 116-119, 122-124, 138-156, 167-168, 177-178, 192-195, 208-220, 232-237, 246-247, 251-265, 282-296, 304-323, 364-378, 382-383, 390, 396, 399-409, 418, 423-459, 463, 469-471, 479, 485-508, 528-531, 535-539, 544-548, 551-564, 568-576, 585-591, 595-600, 604, 607, 610, 613, 616 /usr/lib/python3/dist-packages/keystoneclient/baseclient.py 20 10 50% 19-24, 27-28, 31, 34, 37, 40, 43, 46 /usr/lib/python3/dist-packages/keystoneclient/exceptions.py 92 17 82% 75-78, 85-87, 113-115, 366-368, 375-377, 428-431, 438-439 /usr/lib/python3/dist-packages/keystoneclient/httpclient.py 331 123 63% 45-48, 80, 127-143, 276-301, 311, 318, 322, 326, 330, 332, 336, 338, 340, 344, 348, 354, 358, 399, 406, 411, 414, 416-417, 420-423, 426, 429, 438, 442-443, 450-451, 455, 466-471, 482-487, 561, 588, 594-596, 611, 626-638, 644-650, 658-659, 671, 678, 696, 720, 723, 739, 742-743, 752-757, 774, 791, 808, 825, 842, 859, 870-893, 903-907, 914-918 /usr/lib/python3/dist-packages/keystoneclient/i18n.py 4 0 100% /usr/lib/python3/dist-packages/keystoneclient/service_catalog.py 152 64 58% 53-56, 60, 80-85, 88, 146-147, 157-165, 175, 194-195, 198, 256, 267-294, 315-316, 323, 326-329, 332, 335, 338-346, 351-362, 392-393, 399-412, 427 /usr/lib/python3/dist-packages/keystoneclient/session.py 348 203 42% 48-58, 62, 66-82, 146-149, 160, 164, 169-178, 182-222, 225-255, 340, 343-349, 352, 359-372, 375, 378, 381, 383, 388, 398, 401, 418-423, 426, 433-438, 441-443, 463-481, 488, 492-517, 527, 535, 551, 559, 567, 589-593, 620, 625-630, 639, 658-660, 686, 701-703, 755, 767-769, 785-787, 803-805, 834-837, 882-885, 901-909, 917-943, 961-967, 979-1018 /usr/lib/python3/dist-packages/keystoneclient/utils.py 56 41 27% 27-46, 50-52, 60-71, 80-91, 111-118, 122-123 /usr/lib/python3/dist-packages/keystoneclient/v2_0/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneclient/v2_0/certificates.py 9 5 44% 19, 28-29, 38-40 /usr/lib/python3/dist-packages/keystoneclient/v2_0/client.py 53 33 38% 150-176, 193-219 /usr/lib/python3/dist-packages/keystoneclient/v2_0/ec2.py 17 7 59% 22, 25, 36-38, 46, 54, 59 /usr/lib/python3/dist-packages/keystoneclient/v2_0/endpoints.py 13 5 62% 24, 34, 39-44, 48 /usr/lib/python3/dist-packages/keystoneclient/v2_0/extensions.py 8 2 75% 21, 31 /usr/lib/python3/dist-packages/keystoneclient/v2_0/roles.py 42 29 31% 25, 28, 37, 41-42, 46, 50, 53-59, 67-75, 83-91 /usr/lib/python3/dist-packages/keystoneclient/v2_0/services.py 15 6 60% 25, 35, 39, 43-46, 50 /usr/lib/python3/dist-packages/keystoneclient/v2_0/tenants.py 76 54 29% 37, 40, 44-58, 61, 66, 71, 80-82, 85, 89-98, 110-130, 135-149, 153, 157, 161, 167 /usr/lib/python3/dist-packages/keystoneclient/v2_0/tokens.py 58 36 38% 24, 28, 32, 36, 44-69, 72, 75, 85, 94-96, 108-115, 124-125 /usr/lib/python3/dist-packages/keystoneclient/v2_0/users.py 51 32 37% 27, 30, 33, 42-43, 46, 55-57, 61-63, 68-70, 75-78, 87-91, 97-102, 106, 113-126, 130 /usr/lib/python3/dist-packages/keystoneclient/v3/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/access_rules.py 29 14 52% 58-61, 73-76, 87-90, 104-107, 111, 116 /usr/lib/python3/dist-packages/keystoneclient/v3/application_credentials.py 49 32 35% 72-98, 121-124, 136-139, 150-153, 166-169, 173 /usr/lib/python3/dist-packages/keystoneclient/v3/auth.py 22 10 55% 42-48, 60-66 /usr/lib/python3/dist-packages/keystoneclient/v3/client.py 104 18 83% 270, 279-284, 315, 320, 331-332, 344-352 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/endpoint_filter.py 82 62 24% 34-47, 50-64, 68-73, 77-82, 86-91, 95-99, 106-110, 117-122, 126-131, 135-140, 144-149, 156-160 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/endpoint_policy.py 59 39 34% 28-38, 42, 47, 52, 56-66, 70, 75, 80, 85-97, 102, 108, 114, 125-134, 146-153 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/base.py 19 8 58% 30, 33-40 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/core.py 16 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/domains.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/identity_providers.py 22 8 64% 36-38, 54, 69, 82, 96, 109 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/mappings.py 22 8 64% 36-38, 76, 89, 99, 136, 149 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/projects.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/protocols.py 31 16 48% 38-49, 52-54, 72, 90, 105, 123, 141 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/saml.py 16 9 44% 37-40, 56-59, 62-79 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/service_providers.py 22 8 64% 38-40, 52, 65, 75, 89, 102 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/access_tokens.py 20 9 55% 23-24, 38-51 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/consumers.py 17 4 76% 38, 43, 47, 53 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/core.py 20 5 75% 28-29, 62-65 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/request_tokens.py 33 20 39% 24-25, 30-36, 54-57, 60-73 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/utils.py 14 10 29% 28-38 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/simple_cert.py 11 4 64% 31-33, 42-44 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/trusts.py 33 17 48% 59-74, 85, 90-92, 98, 102 /usr/lib/python3/dist-packages/keystoneclient/v3/credentials.py 17 5 71% 62, 80, 93, 119, 138 /usr/lib/python3/dist-packages/keystoneclient/v3/domain_configs.py 28 13 54% 37, 63-65, 78-79, 105-107, 121-122, 125, 129 /usr/lib/python3/dist-packages/keystoneclient/v3/domains.py 19 7 63% 54, 70, 85-87, 105, 122 /usr/lib/python3/dist-packages/keystoneclient/v3/ec2.py 15 6 60% 30, 51, 68-69, 81, 97 /usr/lib/python3/dist-packages/keystoneclient/v3/endpoint_groups.py 20 6 70% 54, 72, 86, 99, 117, 135 /usr/lib/python3/dist-packages/keystoneclient/v3/endpoints.py 28 12 57% 50-53, 75-76, 94, 119-120, 149-150, 169 /usr/lib/python3/dist-packages/keystoneclient/v3/groups.py 27 15 44% 31-44, 68, 87-91, 106, 122, 138 /usr/lib/python3/dist-packages/keystoneclient/v3/limits.py 21 9 57% 61-73, 93, 115, 133, 150 /usr/lib/python3/dist-packages/keystoneclient/v3/policies.py 24 12 50% 31-42, 64, 79, 92, 108, 124 /usr/lib/python3/dist-packages/keystoneclient/v3/projects.py 106 76 28% 41-55, 58, 61, 64, 67, 70, 73, 105-108, 136-157, 161-164, 168-171, 201-222, 225-227, 246, 264, 274, 283-285, 298-302, 311, 323-326, 337-345 /usr/lib/python3/dist-packages/keystoneclient/v3/regions.py 20 5 75% 61, 75, 87, 112, 129 /usr/lib/python3/dist-packages/keystoneclient/v3/registered_limits.py 22 10 55% 61-72, 99, 120, 140, 157-158 /usr/lib/python3/dist-packages/keystoneclient/v3/role_assignments.py 69 48 30% 40-42, 45-47, 50-52, 55-57, 60-62, 95-124, 127, 131, 135, 139, 143, 147 /usr/lib/python3/dist-packages/keystoneclient/v3/roles.py 149 101 32% 61-92, 95-113, 116-121, 137-141, 156, 190-203, 217, 235, 275-284, 327-336, 377-386, 395, 401, 407, 413, 419, 430-433, 456-458, 481-482, 501-503, 521-523, 543-544, 559, 562, 566, 570 /usr/lib/python3/dist-packages/keystoneclient/v3/services.py 23 11 52% 57-58, 75, 89-90, 111-112, 130-134 /usr/lib/python3/dist-packages/keystoneclient/v3/tokens.py 37 27 27% 18-21, 37-39, 54-58, 78-94, 116-121 /usr/lib/python3/dist-packages/keystoneclient/v3/users.py 57 34 40% 43-45, 82-92, 126-132, 148, 187-197, 213-226, 240-243, 259-262, 278-281, 295 /usr/lib/python3/dist-packages/netaddr/__init__.py 19 1 95% 16 /usr/lib/python3/dist-packages/netaddr/compat.py 60 37 38% 39, 50-53, 56-59, 62-113 /usr/lib/python3/dist-packages/netaddr/contrib/__init__.py 1 0 100% /usr/lib/python3/dist-packages/netaddr/contrib/subnet_splitter.py 17 11 35% 23, 27-38, 42, 46 /usr/lib/python3/dist-packages/netaddr/core.py 73 40 45% 61-74, 89, 112-113, 122-124, 136, 145-149, 158-161, 169-170, 184-196, 199-200, 203, 206 /usr/lib/python3/dist-packages/netaddr/eui/__init__.py 361 276 24% 24, 28, 32, 37-39, 44, 52, 72-101, 104-109, 112-117, 121, 125, 129-152, 157, 169, 173-174, 181, 202-216, 230-268, 271-276, 279-284, 288, 292, 296-308, 312, 316-318, 327, 357-390, 394, 401-413, 416, 419-450, 456, 459-468, 477-480, 485-488, 492, 500-501, 506, 515-525, 529-548, 552, 559-564, 571-576, 583-588, 595-600, 607-612, 619-624, 633, 638, 643, 652, 663-671, 685-687, 699-700, 710, 718-722, 726, 730 /usr/lib/python3/dist-packages/netaddr/ip/__init__.py 822 596 27% 33-38, 47, 54, 60, 69-72, 81-84, 93-96, 105-108, 117-120, 129-132, 136, 140-143, 151-154, 162-174, 181-184, 191-199, 206, 213, 222-223, 228, 262-266, 275, 278, 285-293, 305, 316-319, 323, 330-339, 348-371, 377-378, 384-385, 396-400, 411-415, 426-429, 442-445, 456-459, 468, 472, 480, 485-487, 492, 500, 508, 513, 521, 530, 535, 544-557, 570-586, 596-600, 609, 618, 627, 636, 645, 651, 657, 661, 676-678, 685, 693-697, 705-734, 743-752, 760, 768-776, 782, 787-788, 796-798, 804-816, 820-823, 827-828, 906-908, 911-913, 915-917, 919-921, 924, 934-935, 938, 946, 953-968, 972-977, 989, 994, 999-1002, 1010, 1018-1019, 1024-1025, 1030, 1035-1036, 1041, 1049, 1064-1072, 1085-1093, 1102-1123, 1129, 1135-1138, 1146-1161, 1174-1193, 1202-1205, 1214-1217, 1229-1240, 1255-1281, 1297-1318, 1322-1323, 1327, 1361, 1365, 1371-1375, 1378-1397, 1402, 1407, 1413, 1419-1420, 1427, 1431, 1435, 1446-1448, 1477-1531, 1549-1576, 1589-1591, 1606-1650, 1663-1684, 1701-1730, 1746-1767, 1782-1796, 1811-1823, 1838-1852 /usr/lib/python3/dist-packages/netaddr/ip/glob.py 137 117 15% 26-67, 79-97, 109-127, 141-201, 213, 225-231, 283-285, 289, 293-294, 297, 300-301, 308, 312 /usr/lib/python3/dist-packages/netaddr/ip/nmap.py 64 55 14% 22-45, 51-62, 69-87, 96-101, 115-117 /usr/lib/python3/dist-packages/netaddr/ip/rfc1924.py 28 18 36% 32-42, 49-61 /usr/lib/python3/dist-packages/netaddr/ip/sets.py 350 300 14% 27-53, 65-81, 105-122, 126, 133, 145-210, 216-217, 226, 238-245, 249, 257, 263, 281-296, 317-350, 361, 371-372, 376-378, 392-413, 417, 426-429, 438-441, 450-453, 462-465, 476-479, 488-494, 505-507, 518-551, 566-619, 631-673, 683-688, 696, 700, 711-718, 729-735, 744-748 /usr/lib/python3/dist-packages/netaddr/strategy/__init__.py 113 90 20% 44-56, 70-83, 97-106, 121-138, 154-160, 177-194, 207-226, 238-257, 270-273 /usr/lib/python3/dist-packages/netaddr/strategy/eui48.py 135 70 48% 144-152, 163-197, 209-216, 226, 237-245, 249-251, 255-257, 261-263, 267-269, 273-275, 279-281, 286-288, 292, 296 /usr/lib/python3/dist-packages/netaddr/strategy/eui64.py 122 66 46% 121-124, 133-139, 149-176, 187-192, 202-203, 214-222, 226-228, 232-234, 238-240, 244-246, 250-252, 256-258, 263-265, 269, 273 /usr/lib/python3/dist-packages/netaddr/strategy/ipv4.py 103 51 50% 16, 91-107, 121, 141-148, 158-161, 171, 182, 186, 196-199, 212-214, 218, 222, 226-228, 232, 236, 240, 261-278 /usr/lib/python3/dist-packages/netaddr/strategy/ipv6.py 106 47 56% 18, 24-25, 119-126, 141-142, 154-172, 182-187, 197-198, 221, 225-229, 233, 237, 241, 245-247, 251, 255, 259 /usr/lib/python3/dist-packages/oauthlib/__init__.py 10 2 80% 27, 34 /usr/lib/python3/dist-packages/oauthlib/common.py 212 144 32% 22-24, 27-28, 59, 64-70, 74-80, 84-89, 96-101, 108-113, 129-165, 176-194, 209, 221, 232-233, 237-251, 255-257, 266, 271-275, 280-285, 297-303, 308-328, 338-340, 343, 346-348, 351-352, 355, 358-359, 362-364, 385-430, 433-436, 439-447, 452, 456-458, 463-468 /usr/lib/python3/dist-packages/oauthlib/oauth1/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/__init__.py 125 91 27% 51, 86-101, 104-110, 122-151, 156-186, 199-223, 256-327 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/__init__.py 8 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/access_token.py 59 48 19% 44-54, 104-119, 131-217 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/authorization.py 38 25 34% 50-57, 111-139, 154-163 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/base.py 87 75 14% 23-24, 32-66, 70-106, 110-112, 117-177, 182-216 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/pre_configured.py 8 4 50% 11-14 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/request_token.py 56 45 20% 42-49, 99-110, 122-211 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/resource.py 43 35 19% 70-165 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/signature_only.py 34 26 24% 35-84 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/errors.py 36 15 58% 39-46, 49, 53-58, 62 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/parameters.py 36 23 36% 48-91, 105-112, 124, 136-139 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/request_validator.py 108 48 56% 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 162-163, 170-171, 178-179, 186-187, 194-195, 200, 207-208, 232, 248, 264, 300, 333, 366, 383, 398, 416, 440, 467, 504, 539, 574, 625, 659, 678, 713, 745, 764, 788, 812, 833, 854 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/signature.py 149 117 21% 84-106, 131-205, 279-340, 423-438, 442, 468-495, 499, 525-552, 559-562, 580-586, 590-592, 616-627, 631, 651-661, 681-691, 695-697, 716-729, 739-743 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/utils.py 40 22 45% 31-32, 40-44, 55-60, 64-66, 72, 78, 83-90 /usr/lib/python3/dist-packages/os_service_types/__init__.py 9 3 67% 39-41 /usr/lib/python3/dist-packages/os_service_types/data/__init__.py 8 0 100% /usr/lib/python3/dist-packages/os_service_types/exc.py 10 2 80% 24-25 /usr/lib/python3/dist-packages/os_service_types/service_types.py 111 69 38% 28-29, 59, 64-71, 75-78, 83, 88, 93, 98, 103, 108, 114, 120, 129-133, 141-144, 152, 160-161, 169, 191-202, 210-211, 221-234, 242-245, 254-258, 269-270, 281-285 /usr/lib/python3/dist-packages/oslo_config/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_config/cfg.py 1258 889 29% 38-39, 77, 80, 87, 94-97, 104-105, 108-109, 116, 119, 126, 129, 136-137, 140-141, 149, 156, 159, 167, 170, 178, 181, 188-189, 192, 211, 237-248, 262-265, 269-278, 309, 339, 355-357, 375-388, 392-395, 400-402, 414-423, 541, 545, 548, 559, 574, 584-585, 589-592, 603-604, 612-613, 623-632, 635, 638, 648-676, 688-697, 713-729, 738-741, 753-763, 776-779, 797-806, 809, 869-870, 873, 876, 879, 930-934, 938-949, 966-968, 972-973, 977-988, 994-1006, 1026, 1047, 1086, 1104, 1134-1136, 1152, 1173, 1199-1200, 1229, 1233-1238, 1253, 1291-1295, 1299-1312, 1346-1351, 1354, 1358-1360, 1395-1406, 1409, 1414-1416, 1461-1469, 1478, 1482, 1497-1502, 1509-1510, 1513-1517, 1521, 1524, 1529-1530, 1533, 1548-1552, 1555, 1558-1559, 1562-1566, 1570-1581, 1584, 1587, 1596-1617, 1640-1647, 1672-1697, 1707-1710, 1717, 1724, 1737-1747, 1758-1788, 1802-1807, 1831-1858, 1861-1863, 1878-1879, 1882-1886, 1894-1910, 1913-1914, 1917-1918, 1921-1922, 1979-1999, 2003, 2031-2034, 2039-2049, 2059, 2066-2071, 2120-2141, 2145-2148, 2151-2187, 2196-2201, 2205, 2209, 2213-2215, 2219, 2223-2224, 2236-2244, 2247-2252, 2257-2269, 2289-2293, 2300, 2304, 2307, 2316-2317, 2332-2335, 2340-2341, 2351-2354, 2364-2380, 2385-2386, 2404-2405, 2422-2423, 2438-2441, 2459-2462, 2468-2471, 2484-2485, 2497-2498, 2502-2506, 2510-2511, 2515-2517, 2521-2523, 2542-2554, 2567-2591, 2603-2605, 2617-2619, 2622-2633, 2646-2735, 2749-2768, 2783-2786, 2806-2815, 2818-2830, 2839-2863, 2871-2879, 2893-2897, 2906-2937, 2940-2955, 2958-2965, 2975-2987, 2996, 3009-3032, 3040-3054, 3065-3081, 3089-3094, 3112-3114, 3129-3130, 3134, 3138, 3142, 3146-3147, 3151, 3167-3169, 3173-3185, 3202-3204, 3212-3229 /usr/lib/python3/dist-packages/oslo_config/iniparser.py 75 57 24% 18-20, 23, 31-32, 35-40, 43-56, 59-97, 101, 105, 109, 112, 116, 119, 123, 127 /usr/lib/python3/dist-packages/oslo_config/sources/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oslo_config/sources/_environment.py 22 11 50% 70, 73, 81-82, 85-92 /usr/lib/python3/dist-packages/oslo_config/types.py 419 300 28% 44-53, 56-61, 65, 113, 120-123, 129, 133-139, 142-167, 173-180, 183, 194, 201, 209-215, 218, 238, 241-250, 253, 256, 259, 282-304, 307-324, 327-338, 341, 352, 388, 411, 437-446, 479, 487-519, 522, 525, 531-540, 562-565, 568-586, 589, 596, 618-626, 629-680, 683, 686, 692-694, 713-722, 725-729, 732, 735, 738-739, 742-743, 746-748, 751, 766, 785-799, 802, 805, 808, 829-831, 841-848, 851, 854, 857, 880-882, 885-906, 910, 914, 918, 921, 924-932, 935 /usr/lib/python3/dist-packages/oslo_i18n/__init__.py 4 0 100% /usr/lib/python3/dist-packages/oslo_i18n/_factory.py 75 32 57% 83, 99-121, 136-152, 169, 182, 185, 190, 195, 200, 205 /usr/lib/python3/dist-packages/oslo_i18n/_gettextutils.py 41 20 51% 48-50, 61-101 /usr/lib/python3/dist-packages/oslo_i18n/_lazy.py 4 1 75% 38 /usr/lib/python3/dist-packages/oslo_i18n/_locale.py 2 0 100% /usr/lib/python3/dist-packages/oslo_i18n/_message.py 95 71 25% 59-69, 94-104, 115-132, 137-179, 194-215, 221-227, 238-251, 254-259, 262-264, 267 /usr/lib/python3/dist-packages/oslo_i18n/_translate.py 17 13 24% 39-49, 67-73 /usr/lib/python3/dist-packages/oslo_log/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_serialization/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_serialization/jsonutils.py 82 52 37% 85-181, 217, 235-236, 248, 260, 268-270 /usr/lib/python3/dist-packages/oslo_utils/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_utils/_i18n.py 4 0 100% /usr/lib/python3/dist-packages/oslo_utils/encodeutils.py 60 53 12% 38-63, 84-104, 114-119, 135-188 /usr/lib/python3/dist-packages/oslo_utils/importutils.py 40 24 40% 29-34, 44, 60-65, 92-97, 117-122 /usr/lib/python3/dist-packages/oslo_utils/reflection.py 107 83 22% 44-47, 55-58, 63, 78-96, 107-111, 121-153, 158-163, 168-186, 191, 196, 208-214, 219-220 /usr/lib/python3/dist-packages/oslo_utils/strutils.py 183 135 26% 127, 146-165, 177-178, 214-247, 265-272, 333-359, 416-441, 453-456, 470-488, 503-520, 545-569, 579-586 /usr/lib/python3/dist-packages/oslo_utils/timeutils.py 230 155 33% 35, 56-64, 71-74, 95-97, 102, 109, 120-125, 135-140, 151-165, 177-180, 182, 201, 217, 226-231, 240, 249, 258-267, 283-297, 306-307, 318-319, 333-334, 339, 344, 347-349, 379-396, 421-428, 435-441, 446, 450-459, 465-468, 473, 477-486, 490-491, 495-498, 508-516, 520-525, 529, 533, 537-541, 546-553 /usr/lib/python3/dist-packages/paramiko/__init__.py 34 0 100% /usr/lib/python3/dist-packages/paramiko/_version.py 2 0 100% /usr/lib/python3/dist-packages/paramiko/agent.py 223 152 32% 64-72, 75-78, 81-85, 88-96, 105-107, 110-126, 129-150, 153-155, 165, 173-180, 189-190, 193, 210-213, 216, 222-241, 248-252, 263-269, 272, 275-279, 286-290, 299, 302, 328-331, 334, 337, 340-341, 363-368, 370-375, 379, 385, 396-399, 402, 405, 408, 411-419 /usr/lib/python3/dist-packages/paramiko/auth_handler.py 521 373 28% 105-108, 111-118, 132-140, 146-155, 158-167, 170-177, 180-181, 192-198, 201-207, 219-220, 233-236, 239-240, 249, 254-270, 282-284, 290-291, 300-397, 402, 408-428, 432-441, 444-615, 630-632, 634-641, 654-656, 660-678, 681-694, 699-706, 737, 752-753, 756-757, 761, 765, 769, 773, 776, 779-802, 805-826, 829-830, 833-834, 847 /usr/lib/python3/dist-packages/paramiko/ber.py 84 66 21% 34-35, 38, 41, 44, 47, 50-93, 97-104, 108-114, 117-129, 135-138 /usr/lib/python3/dist-packages/paramiko/buffered_pipe.py 93 45 52% 55-59, 64, 77-90, 99-106, 118-124, 155, 162-164, 171-174, 188-196, 208, 218-222 /usr/lib/python3/dist-packages/paramiko/channel.py 597 342 43% 71, 139-142, 148-161, 190-203, 223-230, 275-283, 300-309, 330-335, 355-362, 377, 402-404, 419-425, 474-492, 509-516, 522, 532, 538, 549, 572-584, 612, 632-635, 645, 654-671, 683, 700-701, 706-710, 727, 748-749, 754-758, 775-781, 798-801, 821-825, 845-848, 866-869, 933-944, 959, 967, 979, 997, 1032-1039, 1042-1047, 1050-1060, 1067, 1082-1155, 1157-1163, 1173, 1192-1209, 1223-1226, 1238, 1254, 1267-1274, 1281-1292, 1305-1333, 1358, 1364-1365, 1379-1380 /usr/lib/python3/dist-packages/paramiko/client.py 275 127 54% 100-108, 126-127, 142-148, 161, 170, 191, 215-216, 345-348, 352-360, 368, 385, 387, 389, 394, 401-404, 419-423, 426, 430-433, 459-466, 510, 513, 545-548, 556, 566, 580-581, 596-597, 632, 637-641, 648-653, 656-670, 674-687, 694-707, 727, 730, 745, 749-765, 789, 801, 817-823, 835 /usr/lib/python3/dist-packages/paramiko/common.py 93 4 96% 186-187, 207-211 /usr/lib/python3/dist-packages/paramiko/compress.py 12 4 67% 29, 32, 37, 40 /usr/lib/python3/dist-packages/paramiko/config.py 130 107 18% 50, 58-94, 126-147, 154-157, 160-166, 180-239, 245-248, 257-259, 262-303, 342, 358-361, 372 /usr/lib/python3/dist-packages/paramiko/dsskey.py 115 86 25% 55-81, 84-90, 93, 96, 99, 102, 105, 108-130, 133-159, 162-172, 180-190, 207-219, 224-225, 228-229, 234-247 /usr/lib/python3/dist-packages/paramiko/ecdsakey.py 158 107 32% 77, 80-82, 85-87, 90-92, 119-168, 172, 175-192, 195, 198, 207, 210, 213, 216-223, 226-239, 242, 250, 266-273, 278-279, 282-283, 286-296, 299-302, 305-308 /usr/lib/python3/dist-packages/paramiko/ed25519key.py 122 36 70% 42-47, 68, 79-80, 86, 102, 113-123, 126, 132, 139-154, 160, 165, 184, 198-202, 208, 221, 225-226 /usr/lib/python3/dist-packages/paramiko/file.py 250 156 38% 76-78, 98-111, 125-128, 138, 148, 158, 168-170, 189-229, 255, 257, 268-273, 277-283, 289, 292-293, 298-334, 352-355, 376, 386, 397-422, 433-435, 442, 446, 456, 463, 474, 492, 494-496, 506-508, 512-516, 522-529, 536-545 /usr/lib/python3/dist-packages/paramiko/hostkeys.py 200 138 31% 27, 63, 75-77, 95-110, 125-129, 144-146, 149-150, 153, 156-160, 163-166, 169-180, 183, 191-192, 195, 203-211, 223-229, 235, 238-239, 242, 248, 251-258, 262-273, 277-282, 285-288, 301-310, 315-317, 343-374, 382-388, 391 /usr/lib/python3/dist-packages/paramiko/kex_curve25519.py 82 29 65% 31-32, 39, 47-48, 62, 65, 70-100 /usr/lib/python3/dist-packages/paramiko/kex_ecdh_nist.py 87 59 32% 26-30, 33-47, 50-54, 59-63, 66-106, 109-137 /usr/lib/python3/dist-packages/paramiko/kex_gex.py 181 153 15% 61-68, 71-91, 94-105, 111-125, 128-163, 168-190, 193-210, 213-251, 254-282 /usr/lib/python3/dist-packages/paramiko/kex_group1.py 85 62 27% 51-54, 57-69, 72-77, 89-96, 100-121, 125-154 /usr/lib/python3/dist-packages/paramiko/kex_group14.py 11 0 100% /usr/lib/python3/dist-packages/paramiko/kex_group16.py 9 0 100% /usr/lib/python3/dist-packages/paramiko/kex_gss.py 345 294 15% 84-89, 95-110, 124-135, 148-153, 162-166, 175-189, 199-234, 243-288, 303-307, 342-351, 357-370, 379-394, 400-414, 423-458, 466-486, 499-551, 562-566, 574-588, 596-638, 651-655, 674, 677, 680 /usr/lib/python3/dist-packages/paramiko/message.py 104 24 77% 60, 86-89, 111, 123, 138-142, 156, 164, 241-246, 254-255, 291, 293, 295 /usr/lib/python3/dist-packages/paramiko/packet.py 372 118 68% 59-62, 132, 173-174, 207, 210, 213-214, 217, 220, 223, 226, 242-244, 247, 272, 300-302, 306, 310, 315-328, 331, 333, 344-359, 361-363, 371, 374, 377, 405, 410, 413-417, 421, 437, 451-457, 471-484, 489, 494, 503, 515, 527, 532, 540, 546, 558-566, 573-580, 586, 588, 597-603, 613-616, 624, 626-637, 655, 660 /usr/lib/python3/dist-packages/paramiko/pipe.py 84 60 29% 34-38, 43-46, 49-52, 55, 58-61, 64-67, 70-71, 81-93, 96-99, 102, 105-108, 111-114, 117-118, 123-125, 128-130, 133-135, 144-148 /usr/lib/python3/dist-packages/paramiko/pkey.py 183 97 47% 82, 90, 93, 107-111, 114, 124, 133, 140, 161, 171, 183, 227-228, 242, 255, 287, 289, 297-298, 307-308, 313-338, 362-367, 370-375, 402, 415-426, 447-457, 487-489, 496-498, 505-525, 535-536, 539-542, 546, 549 /usr/lib/python3/dist-packages/paramiko/primes.py 69 58 16% 32-49, 60-61, 64-107, 113-122, 125-148 /usr/lib/python3/dist-packages/paramiko/proxy.py 51 33 35% 53-59, 68-76, 86-109, 112, 116, 121, 124 /usr/lib/python3/dist-packages/paramiko/py3compat.py 101 59 42% 32-102, 124, 132-133, 148-151, 154, 162, 165 /usr/lib/python3/dist-packages/paramiko/rsakey.py 89 34 62% 52-53, 57-67, 73, 80, 94-99, 102, 110, 113, 126-139, 142, 150, 167-170, 179-180, 187-188 /usr/lib/python3/dist-packages/paramiko/server.py 98 60 39% 88, 105, 124, 149, 181, 206, 237, 265-267, 297-299, 310-311, 332, 343, 373, 398, 414, 433, 457-463, 483, 509, 522, 562, 580, 594, 614-621, 633, 665-669, 676, 679-695, 721, 730 /usr/lib/python3/dist-packages/paramiko/sftp.py 89 57 36% 126-128, 133-140, 145-155, 158, 161-168, 171-191, 194-198, 201-213 /usr/lib/python3/dist-packages/paramiko/sftp_attr.py 150 127 15% 54-61, 73-82, 85, 90-96, 99-113, 116-144, 147-159, 163-170, 174-233, 243 /usr/lib/python3/dist-packages/paramiko/sftp_client.py 351 290 17% 78-84, 113-133, 164-170, 173-182, 194-195, 204, 218, 237-260, 276-324, 358-382, 396-398, 420-423, 440-443, 456-460, 468-470, 491-496, 509-514, 523-526, 537-541, 554-558, 574-580, 591-595, 606-616, 630-638, 656-662, 673, 676-685, 714-727, 757-759, 778-781, 801-805, 812-813, 817-838, 842-875, 878-880, 886-898, 905-913 /usr/lib/python3/dist-packages/paramiko/sftp_file.py 225 183 19% 62-73, 76, 82, 92-110, 113-128, 139-147, 157-177, 180-190, 194-212, 225, 234, 246, 256, 264-272, 283-286, 296-301, 313-319, 334-341, 351-356, 404-416, 436, 465-476, 492-515, 520-523, 526-531, 536-541, 544-563, 567-570 /usr/lib/python3/dist-packages/paramiko/sftp_handle.py 67 51 24% 49-53, 67-72, 92-106, 126-144, 157, 168, 178, 185-187, 190, 193 /usr/lib/python3/dist-packages/paramiko/sftp_server.py 322 290 10% 117-126, 129-135, 140-165, 168-177, 189-196, 213-223, 228-243, 246-256, 259-266, 269-277, 280-291, 298-358, 362-376, 379-536 /usr/lib/python3/dist-packages/paramiko/sftp_si.py 42 21 50% 51, 59, 68, 108, 140, 156, 174, 184, 208, 222, 239, 251, 266, 282-289, 302, 316 /usr/lib/python3/dist-packages/paramiko/ssh_exception.py 59 30 49% 65-67, 70, 83-84, 87, 102-104, 107, 122-125, 128-131, 147-149, 152, 185-195, 198 /usr/lib/python3/dist-packages/paramiko/ssh_gss.py 253 198 22% 51-57, 64-68, 100-107, 122-141, 151-152, 161, 175-183, 192-197, 208, 227-236, 253-263, 287-319, 334-346, 360-366, 378-392, 401-403, 416, 421, 438-448, 473-498, 514-526, 540-546, 558-572, 582-584, 596, 612-621, 643-675, 690-702, 715-724, 736-754, 763, 778 /usr/lib/python3/dist-packages/paramiko/transport.py 1294 636 51% 121-122, 401-406, 409-427, 457-458, 557-576, 588-589, 598, 626-632, 672-674, 683-686, 690, 734-756, 770, 787-791, 819-832, 838-843, 859, 921, 935, 947, 994, 1008-1011, 1013-1014, 1028-1031, 1035, 1039-1042, 1077-1096, 1107-1110, 1122, 1135-1140, 1155-1166, 1180-1183, 1202-1220, 1232-1245, 1307-1363, 1399-1403, 1414, 1428-1430, 1441-1443, 1466-1471, 1520-1556, 1595, 1599, 1604, 1650-1658, 1668-1681, 1699-1707, 1724-1730, 1743-1745, 1767, 1778, 1793, 1809-1812, 1815-1835, 1841-1842, 1848, 1854-1855, 1875-1878, 1882-1884, 1910, 1912, 1933-1934, 1940-1946, 1951, 1964-1971, 1975-1982, 1985-1990, 2020-2038, 2049-2050, 2068, 2092, 2095-2096, 2098-2099, 2101-2102, 2109-2112, 2125, 2133-2147, 2155-2170, 2172-2217, 2227, 2241, 2253, 2256-2259, 2264, 2266, 2279, 2283-2284, 2302-2317, 2354, 2371, 2442, 2451, 2459, 2466-2472, 2482, 2487, 2493-2499, 2519, 2529-2532, 2543, 2551-2557, 2580-2582, 2609-2610, 2627, 2637-2638, 2662-2663, 2680, 2691-2692, 2702-2704, 2706-2708, 2719, 2739-2741, 2753-2765, 2768-2769, 2771-2777, 2780-2783, 2786-2789, 2798-2799, 2814-2835, 2838-2961, 2970-2976, 3006, 3012, 3015-3023, 3028, 3032, 3037, 3041, 3046, 3050, 3055, 3059, 3064, 3068, 3096-3097, 3102-3106, 3109-3113 /usr/lib/python3/dist-packages/paramiko/util.py 176 104 41% 40-55, 76-83, 89, 94-101, 105-109, 119, 124-135, 139, 156-170, 189-191, 198-200, 207, 212-222, 232-242, 248-257, 263-264, 281-283, 288, 298, 301 /usr/lib/python3/dist-packages/pbr/__init__.py 0 0 100% /usr/lib/python3/dist-packages/pbr/version.py 221 127 43% 30-31, 60, 64-66, 69, 83-88, 100-102, 105, 108, 111, 114, 117, 146-147, 154, 160, 162-172, 183-188, 194-195, 200-202, 205, 210-211, 213-223, 228-232, 245, 258-273, 291-314, 326-343, 350, 360, 367, 387-405, 423, 427, 444-449, 457, 480-483 /usr/lib/python3/dist-packages/pexpect/__init__.py 11 0 100% /usr/lib/python3/dist-packages/pexpect/exceptions.py 15 7 53% 11-12, 15, 22-26 /usr/lib/python3/dist-packages/pexpect/expect.py 181 164 9% 7-11, 14-42, 45-63, 66-82, 85-89, 93-122, 148-158, 164-173, 186-217, 244-254, 262-273, 285-306 /usr/lib/python3/dist-packages/pexpect/pty_spawn.py 301 249 17% 22-25, 189-205, 211-237, 255-310, 314, 323-330, 341, 360-371, 379, 413, 436-485, 493, 501-502, 539-546, 554-555, 559-561, 572-574, 586-587, 593-594, 598, 602, 607, 615-647, 661-671, 680-690, 699-700, 705, 713, 758-768, 774-776, 782, 791-827, 832-833 /usr/lib/python3/dist-packages/pexpect/run.py 48 42 12% 95-148, 154-155 /usr/lib/python3/dist-packages/pexpect/spawnbase.py 224 182 19% 17, 21, 35-122, 125-131, 137-139, 142-144, 147, 150-151, 165-180, 183, 216-238, 334-340, 357-369, 389-418, 427-428, 437-457, 471-478, 483, 493-499, 504, 509, 513, 517, 522 /usr/lib/python3/dist-packages/pexpect/utils.py 105 90 14% 10-12, 17, 26-45, 54-66, 76-127, 139-156, 163-187 /usr/lib/python3/dist-packages/pkg_resources/__init__.py 1579 855 46% 48-50, 54-55, 65-67, 76-77, 93, 97-98, 131-132, 144-148, 152-155, 159, 163-164, 168, 172, 194-198, 256, 271, 275, 278, 285-288, 301, 312, 316, 320-322, 325, 328, 359-365, 369-381, 385, 398-408, 425-461, 466-470, 484, 490, 495, 500, 583-588, 597-610, 629, 644, 654, 663-667, 679, 699, 702, 713, 743-806, 844-890, 901-906, 915, 919-920, 924, 927, 933-937, 953-957, 982-985, 994-999, 1003, 1013-1018, 1028-1029, 1034-1038, 1054-1066, 1077-1078, 1082-1084, 1088-1096, 1100-1103, 1135, 1139, 1145, 1151, 1157, 1163, 1170-1193, 1208-1218, 1230-1244, 1261-1264, 1285-1290, 1312, 1344, 1352, 1360-1366, 1377-1381, 1392-1393, 1396, 1399, 1402, 1405, 1412, 1419, 1423, 1426-1430, 1436, 1439, 1442, 1445-1447, 1450-1470, 1473, 1478, 1483, 1491, 1554-1562, 1569-1571, 1583-1584, 1589-1598, 1608, 1611, 1614, 1639, 1642, 1665-1673, 1688-1695, 1705-1706, 1711-1716, 1723-1726, 1732, 1735-1745, 1749-1754, 1758-1809, 1815-1825, 1828-1834, 1837-1851, 1854-1855, 1858, 1861, 1864, 1867, 1889, 1896, 1907-1909, 1946-1952, 1979-1999, 2043-2048, 2098, 2101, 2110-2122, 2130, 2144-2148, 2156-2162, 2206-2220, 2228-2254, 2271, 2274-2275, 2298, 2306-2313, 2324, 2373-2377, 2389-2391, 2413-2419, 2422-2427, 2430, 2436-2445, 2451-2455, 2458-2468, 2490-2497, 2501-2506, 2511-2519, 2524-2538, 2542-2547, 2602, 2612, 2615, 2618, 2621, 2624, 2627-2630, 2633, 2649-2652, 2655-2678, 2684-2693, 2701-2705, 2714-2727, 2730-2734, 2738-2748, 2754-2765, 2791-2798, 2801-2804, 2807-2812, 2821, 2831, 2835, 2842-2847, 2851-2854, 2858-2866, 2870, 2894, 2903, 2911-2938, 2941-2957, 2963-2968, 2972-2976, 2980, 3013-3018, 3022-3026, 3030-3049, 3060-3069, 3074, 3088, 3091-3095, 3104-3105, 3122, 3128, 3133, 3143, 3146, 3160, 3174-3175, 3180-3188, 3199-3214, 3218-3225 /usr/lib/python3/dist-packages/pkg_resources/_vendor/__init__.py 0 0 100% /usr/lib/python3/dist-packages/pkg_resources/_vendor/appdirs.py 257 218 15% 29-39, 77-97, 131-163, 195-203, 236-254, 290-311, 345-353, 388-404, 411-415, 419, 424, 429, 434, 439, 444, 449, 460-476, 480-503, 507-530, 533-556, 559-571, 577-608 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/__about__.py 10 0 100% /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/__init__.py 3 0 100% /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/_compat.py 12 1 92% 17 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/_structures.py 41 17 59% 10, 13, 16, 19, 22, 25, 28, 31, 42, 45, 48, 51, 54, 57, 60, 63, 66 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/markers.py 130 73 44% 47, 50, 53, 56, 62, 68, 74, 142-145, 149-168, 184-197, 204-211, 215-238, 242-246, 250-257, 275-280, 283, 286, 297-301 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/requirements.py 72 17 76% 91-92, 98-102, 110-124, 127 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/specifiers.py 306 190 38% 83-93, 96-102, 109, 112, 115-123, 126-134, 137, 140-142, 146, 150, 154, 158, 161, 165-180, 183-211, 243-245, 248, 251, 254, 257, 260, 263, 269-271, 397-410, 416-446, 450, 454, 458, 464-483, 489-514, 517, 523-541, 545, 552-559, 563-583, 600-603, 613-619, 622, 628-648, 651-658, 661-668, 671, 680-691, 695, 698, 709, 718, 733-774 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/version.py 160 36 78% 45, 51, 60, 63, 67, 79, 82, 86, 90, 94, 98, 102, 146, 150, 234, 241, 256, 268, 272-281, 285-287, 291, 295, 303, 312, 314, 316, 318, 324-326, 363 /usr/lib/python3/dist-packages/pkg_resources/_vendor/pyparsing.py 2533 1341 47% 96-97, 103-106, 110-114, 151-181, 189-193, 212-213, 226, 234-241, 244, 247, 252-257, 259, 309, 312, 320, 322, 381, 385, 392, 394, 398, 418, 425-426, 440-442, 448-452, 455, 463-466, 469, 472, 485-504, 545-561, 580-583, 599-603, 617, 632-635, 641-642, 650-656, 659-661, 680-685, 688, 691, 697, 699, 718, 739-753, 770-825, 828-832, 856-869, 889-914, 937, 941, 948-958, 961, 964, 978-979, 991, 996-1001, 1004, 1007, 1010, 1014, 1053-1058, 1075-1090, 1097-1098, 1121, 1142, 1201, 1226-1227, 1237-1248, 1319-1320, 1335-1336, 1339-1349, 1353, 1359, 1375-1393, 1413-1425, 1436-1437, 1445, 1448-1453, 1457-1475, 1480-1533, 1543-1563, 1600-1606, 1640, 1646-1654, 1688-1727, 1746-1770, 1790-1797, 1812-1819, 1836-1838, 1848-1850, 1857-1863, 1869-1875, 1902, 1904-1909, 1914-1916, 1919, 1921, 1923, 1932-1935, 1940, 1946, 1953, 1955-1957, 1964-1970, 1977, 1979-1981, 1988-1994, 2000-2006, 2012-2018, 2076-2077, 2092-2100, 2106-2110, 2147-2151, 2157, 2165, 2171, 2179-2191, 2194-2199, 2202, 2205, 2208, 2211, 2226-2230, 2319-2361, 2388-2392, 2395, 2418-2421, 2459-2477, 2480-2491, 2494-2496, 2502, 2516-2520, 2523-2525, 2537, 2540-2543, 2571-2577, 2580-2603, 2658, 2671, 2676, 2693, 2701, 2704-2705, 2726, 2732, 2734-2735, 2740, 2785, 2794-2806, 2822-2823, 2869-2870, 2875-2878, 2891-2892, 2903, 2909, 2911-2912, 2918-2921, 2929-2961, 2997, 3002, 3019-3030, 3040, 3073, 3078-3079, 3082-3094, 3109-3110, 3113-3119, 3122-3127, 3156-3158, 3170-3178, 3191-3192, 3205, 3208-3211, 3222-3224, 3227-3231, 3242-3245, 3248-3253, 3263, 3266, 3271, 3274-3277, 3281, 3284-3286, 3298-3307, 3310-3317, 3358-3361, 3386-3388, 3404-3405, 3407-3415, 3423-3425, 3428-3432, 3436, 3463, 3477-3483, 3486-3494, 3500, 3504-3506, 3510, 3518-3520, 3545, 3558-3561, 3569, 3572-3574, 3578, 3586-3588, 3646-3649, 3652-3698, 3701-3707, 3710-3712, 3725, 3741, 3751-3760, 3769-3773, 3776-3779, 3810-3811, 3814-3815, 3837-3839, 3843, 3856, 3864, 3869, 3875, 3877, 3916, 3947, 3956, 3959, 4008-4012, 4019, 4082-4092, 4095-4139, 4165, 4177, 4180-4181, 4191-4195, 4199, 4205-4212, 4218-4220, 4224, 4262-4266, 4274, 4340-4361, 4387, 4395-4396, 4398-4402, 4404, 4427-4445, 4465, 4487-4498, 4501-4507, 4522-4535, 4551-4563, 4596-4644, 4679, 4712-4713, 4723, 4745-4746, 4784-4785, 4792-4795, 4809, 4823, 4858, 4863-4864, 4878-4879, 4885-4886, 4930, 4982-4994, 5029-5030, 5097-5146, 5215-5245, 5325-5359, 5369, 5607-5612, 5629-5634, 5659, 5675-5740 /usr/lib/python3/dist-packages/pkg_resources/_vendor/six.py 444 209 53% 49-72, 98-99, 112, 118-121, 131-133, 145, 154-157, 192-193, 203, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843 /usr/lib/python3/dist-packages/pkg_resources/extern/__init__.py 36 5 86% 21, 32, 54-57 /usr/lib/python3/dist-packages/pkg_resources/py2_warn.py 6 0 100% /usr/lib/python3/dist-packages/pkg_resources/py31compat.py 12 5 58% 9-13 /usr/lib/python3/dist-packages/rfc3986/__init__.py 16 0 100% /usr/lib/python3/dist-packages/rfc3986/_mixin.py 112 86 23% 28-51, 54, 59-63, 68-72, 77-81, 91, 113-123, 139-147, 165-168, 182-185, 199-202, 216-219, 229, 247-299, 308-319, 341-353 /usr/lib/python3/dist-packages/rfc3986/abnf_regexp.py 63 0 100% /usr/lib/python3/dist-packages/rfc3986/api.py 15 6 60% 38, 52, 77, 92-93, 106 /usr/lib/python3/dist-packages/rfc3986/compat.py 23 10 57% 20-21, 25-26, 45-47, 52-54 /usr/lib/python3/dist-packages/rfc3986/exceptions.py 45 24 47% 18, 28, 37, 52-59, 71-81, 89-95, 103-110 /usr/lib/python3/dist-packages/rfc3986/iri.py 50 34 32% 49-57, 61-73, 76, 86-89, 111-142 /usr/lib/python3/dist-packages/rfc3986/misc.py 31 5 84% 116-121 /usr/lib/python3/dist-packages/rfc3986/normalizers.py 79 63 20% 24, 29-37, 42, 47, 52-67, 72-76, 81-83, 88-90, 101-105, 114-139, 144-167 /usr/lib/python3/dist-packages/rfc3986/parseresult.py 166 124 25% 34-46, 50, 55, 60, 65, 81-92, 98-112, 134-139, 152, 159-172, 176-181, 193-198, 208-220, 227-244, 267-273, 287, 294-314, 327-337, 342-364, 368-385 /usr/lib/python3/dist-packages/rfc3986/uri.py 30 17 43% 88-96, 102-115, 128, 144-147 /usr/lib/python3/dist-packages/rfc3986/validators.py 129 99 23% 60-73, 87-89, 103-105, 119-123, 135-136, 148-149, 165-174, 190-199, 220-240, 245-251, 256-258, 265-271, 284-289, 306-309, 324-329, 344, 359, 374, 389, 396, 411-430, 435-450 /usr/lib/python3/dist-packages/secretstorage/__init__.py 21 18 14% 17-53 /usr/lib/python3/dist-packages/secretstorage/collection.py 104 99 5% 22-201 /usr/lib/python3/dist-packages/secretstorage/defines.py 11 0 100% /usr/lib/python3/dist-packages/secretstorage/dhcrypto.py 28 22 21% 18-59 /usr/lib/python3/dist-packages/secretstorage/exceptions.py 5 0 100% /usr/lib/python3/dist-packages/secretstorage/item.py 73 68 7% 17-145 /usr/lib/python3/dist-packages/secretstorage/util.py 112 107 4% 15-180 /usr/lib/python3/dist-packages/simplejson/__init__.py 80 55 31% 120-122, 126-130, 248-279, 372-385, 457, 519-535, 539-562, 577 /usr/lib/python3/dist-packages/simplejson/compat.py 29 16 45% 5-18, 24, 26 /usr/lib/python3/dist-packages/simplejson/decoder.py 225 166 26% 14-15, 26-27, 60-133, 145-234, 237-270, 369, 373, 390, 392, 397, 399 /usr/lib/python3/dist-packages/simplejson/encoder.py 394 341 13% 13-14, 42-62, 69-103, 244, 247, 249, 251, 272, 284-302, 314-380, 400-404, 407-417, 441-722 /usr/lib/python3/dist-packages/simplejson/errors.py 29 23 21% 7-12, 16-23, 41-50, 53 /usr/lib/python3/dist-packages/simplejson/raw_json.py 3 1 67% 9 /usr/lib/python3/dist-packages/simplejson/scanner.py 64 53 17% 9-10, 21-83 /usr/lib/python3/dist-packages/six.py 491 216 56% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 308, 488, 496, 501-507, 519-525, 530-532, 538-540, 545, 550, 554-568, 583, 592, 600-616, 631, 645-647, 653-673, 679, 683, 687, 691, 698-721, 737-738, 743-795, 797-804, 814-834, 853-855, 871, 873, 893-898, 913, 915, 933, 937, 948-955, 976-977 /usr/lib/python3/dist-packages/stevedore/__init__.py 9 0 100% /usr/lib/python3/dist-packages/stevedore/driver.py 29 17 41% 51-53, 66, 100-105, 108-118, 139-141, 147-148 /usr/lib/python3/dist-packages/stevedore/enabled.py 13 7 46% 64-65, 77-84 /usr/lib/python3/dist-packages/stevedore/exception.py 3 0 100% /usr/lib/python3/dist-packages/stevedore/extension.py 104 71 32% 46-49, 58, 99-107, 141-146, 150-152, 155-156, 160-165, 176-179, 183, 187-214, 220-230, 237, 259-265, 269, 290, 294-301, 309, 317, 326, 331 /usr/lib/python3/dist-packages/stevedore/hook.py 11 6 45% 59, 74-78, 87-89 /usr/lib/python3/dist-packages/stevedore/named.py 34 24 29% 74-89, 123-129, 134-140, 143-146, 154-156 /usr/lib/python3/dist-packages/swiftclient/__init__.py 7 2 71% 31-32 /usr/lib/python3/dist-packages/swiftclient/client.py 959 551 43% 53-63, 71-72, 75-76, 89, 128-135, 146-156, 164-190, 195, 197-208, 214-217, 220-221, 232, 242, 257, 275-276, 279, 282, 285-288, 291, 294, 320-328, 331-368, 411, 417, 421-424, 427-430, 441, 459, 490, 495, 524-526, 536-567, 573-574, 594-600, 604, 611-612, 615, 639-646, 658-659, 694-700, 702, 715, 720, 725-727, 738, 743, 796, 798, 801, 803-813, 817, 819, 821, 823, 825, 836, 838, 857-875, 896-921, 953, 955-970, 975, 977, 981, 983, 985, 987, 990, 992, 1004, 1007, 1027-1048, 1068-1092, 1111-1133, 1155-1180, 1213, 1216, 1221, 1228, 1231-1234, 1236, 1262-1285, 1332, 1339, 1341, 1347, 1349, 1351, 1355, 1357, 1362, 1364, 1367, 1375, 1379-1387, 1397, 1420-1439, 1465-1503, 1529-1557, 1568-1578, 1656, 1660, 1677-1679, 1694-1702, 1722-1727, 1741-1742, 1748, 1753-1791, 1795, 1812, 1818, 1836, 1842, 1848, 1855, 1873, 1886, 1892, 1903-1905, 1914, 1920, 1927, 1933-1944, 1947-1950 /usr/lib/python3/dist-packages/swiftclient/exceptions.py 51 45 12% 25-36, 40-43, 48-81 /usr/lib/python3/dist-packages/swiftclient/utils.py 229 158 31% 41, 51-68, 100-197, 202-204, 220-239, 248-253, 264, 290, 304-305, 310, 332-339, 342, 345, 348, 351-363, 367-369, 373-378, 382-387, 391-392, 396-397, 401-405, 410-416, 419, 424-427 /usr/lib/python3/dist-packages/swiftclient/version.py 6 3 50% 24-28 /usr/lib/python3/dist-packages/urllib3/__init__.py 33 8 76% 56-62, 86 /usr/lib/python3/dist-packages/urllib3/_collections.py 187 87 53% 5-6, 9-16, 70, 73, 76-80, 83-84, 87, 102-103, 145, 149, 152-153, 160, 166-170, 175, 178-179, 184, 198-206, 209-212, 228, 236, 243-244, 249-250, 256, 261-268, 275-286, 297, 300-305, 308-310, 321-323, 334-354 /usr/lib/python3/dist-packages/urllib3/connection.py 173 40 77% 17-21, 27-30, 153, 163-171, 178-184, 187-188, 194, 215, 221, 224, 226, 298-301, 319-327, 331, 335, 364, 378, 391, 411-420, 428 /usr/lib/python3/dist-packages/urllib3/connectionpool.py 318 127 60% 76, 83, 86, 89-91, 97, 215, 221-236, 254-263, 267-273, 294-303, 313, 318, 325, 330-348, 378-381, 401, 405, 418-425, 443-444, 454, 461, 479-493, 602, 605, 612, 618, 637-638, 663, 697-726, 734-735, 741, 745-748, 765-777, 782-804, 823-838, 954-955, 970, 977-978, 1004, 1035-1040, 1057 /usr/lib/python3/dist-packages/urllib3/contrib/__init__.py 0 0 100% /usr/lib/python3/dist-packages/urllib3/contrib/_appengine_environ.py 11 1 91% 36 /usr/lib/python3/dist-packages/urllib3/contrib/pyopenssl.py 248 246 1% 47-498 /usr/lib/python3/dist-packages/urllib3/contrib/socks.py 75 66 12% 55-210 /usr/lib/python3/dist-packages/urllib3/exceptions.py 96 21 78% 21-22, 26, 33-34, 38, 79-83, 90-92, 147-150, 222, 225, 241-242, 249-250 /usr/lib/python3/dist-packages/urllib3/fields.py 90 29 68% 18-20, 38-61, 83, 114, 155, 177-181, 221, 242-243 /usr/lib/python3/dist-packages/urllib3/filepost.py 43 6 86% 34, 57-60, 85, 88 /usr/lib/python3/dist-packages/urllib3/packages/__init__.py 8 2 75% 10-11 /usr/lib/python3/dist-packages/urllib3/packages/ssl_match_hostname/__init__.py 11 6 45% 7, 10-16 /usr/lib/python3/dist-packages/urllib3/poolmanager.py 172 80 53% 96, 102, 170, 173-175, 189, 199-200, 225, 299-306, 318-372, 411-431, 434-439, 448-456, 460-469, 473 /usr/lib/python3/dist-packages/urllib3/request.py 39 27 31% 54, 70-79, 88-97, 144-171 /usr/lib/python3/dist-packages/urllib3/response.py 399 219 45% 34-36, 39, 42-61, 73-74, 77, 80-98, 103-118, 131, 134, 137-139, 143-152, 190, 217, 236, 251, 258, 268-271, 283-287, 291, 294, 302, 315-322, 332, 337-338, 341, 347-348, 352, 365, 367-373, 377, 388-391, 397, 406-410, 427-443, 455-462, 495, 518-529, 539, 560-561, 585, 603, 610, 615, 618, 621, 626, 628, 631-634, 637-642, 648-653, 657, 661-666, 675, 680-689, 692-711, 727-781, 789-792, 795-809 /usr/lib/python3/dist-packages/urllib3/util/__init__.py 10 0 100% /usr/lib/python3/dist-packages/urllib3/util/connection.py 66 18 73% 19, 21, 25-26, 53, 73, 77-86, 91, 118, 130-131 /usr/lib/python3/dist-packages/urllib3/util/queue.py 14 1 93% 7 /usr/lib/python3/dist-packages/urllib3/util/request.py 50 22 56% 13, 63, 65, 71, 74, 77, 80, 85, 96, 98-103, 119-133 /usr/lib/python3/dist-packages/urllib3/util/response.py 35 17 51% 19-35, 55, 71, 83-86 /usr/lib/python3/dist-packages/urllib3/util/retry.py 150 95 37% 186-187, 202-218, 223-232, 240-249, 253-265, 270-275, 278-283, 286-289, 300-305, 311, 317, 339, 350-355, 376-442, 445 /usr/lib/python3/dist-packages/urllib3/util/ssl_.py 148 78 47% 31-34, 43-44, 50-56, 61-63, 104-149, 162-174, 193, 198, 201, 211-217, 332, 337-348, 354, 357-360, 372-383, 395, 401-407 /usr/lib/python3/dist-packages/urllib3/util/timeout.py 63 25 60% 102, 121, 126-153, 192, 204-208, 223-226, 252-254, 256 /usr/lib/python3/dist-packages/urllib3/util/url.py 205 82 60% 102, 112, 117-122, 127-129, 150-169, 172, 193-207, 239, 252, 258-259, 264, 269, 277, 282-294, 299, 304-314, 354, 358, 372, 374-376, 379-381, 391-394, 401-404, 411, 431-432 /usr/lib/python3/dist-packages/urllib3/util/wait.py 76 37 51% 8-9, 48-68, 72-87, 92, 97, 111, 121-122, 135-138, 153 /usr/lib/python3/dist-packages/yaml/__init__.py 184 118 36% 15-16, 31-37, 45, 62-67, 73-78, 85-89, 96-101, 123-132, 142, 152, 162, 172, 182, 192, 201-213, 224-243, 250, 262-283, 290, 298, 306, 316-322, 331-337, 345-350, 359-364, 373, 382, 391-397, 418, 425 /usr/lib/python3/dist-packages/yaml/composer.py 92 31 66% 18-22, 26-27, 40-41, 65-70, 74-75, 82, 96, 100-115, 126 /usr/lib/python3/dist-packages/yaml/constructor.py 479 295 38% 32, 38-39, 44-45, 52, 61, 69, 71-72, 74, 82-98, 102, 107-108, 114, 119, 125-129, 134, 141, 148-157, 175-177, 186-204, 208-209, 213, 221-222, 234-235, 242, 244, 246, 248, 250, 252, 254-261, 271-292, 295-307, 323-350, 356-373, 377-394, 397-400, 406-408, 417-424, 427, 487, 490-492, 495, 498, 501-513, 517, 520, 523, 526-538, 541-563, 566-570, 573-577, 581-593, 596-612, 617-621, 635-656, 659, 716, 719, 722, 726 /usr/lib/python3/dist-packages/yaml/cyaml.py 46 24 48% 19-21, 26-28, 33-35, 40-42, 47-49, 59-66, 76-83, 93-100 /usr/lib/python3/dist-packages/yaml/dumper.py 23 12 48% 17-25, 35-43, 53-61 /usr/lib/python3/dist-packages/yaml/emitter.py 838 769 8% 22-29, 42-104, 108-109, 112-116, 121-131, 134-144, 147-154, 161-167, 171, 176, 179-211, 215-223, 227-228, 234-258, 261-264, 267-270, 275-278, 281-290, 293-306, 311-314, 317-331, 334-352, 355-357, 360-364, 369-371, 374, 377-384, 389-390, 393, 396-407, 410-412, 415-418, 423, 427, 431-434, 438-452, 460-467, 470-492, 495-513, 516-535, 540-543, 546-555, 558-578, 581-614, 617-624, 629-778, 789-790, 794-795, 798, 802-812, 815-825, 828-836, 839-843, 846-850, 855-906, 927-978, 981-989, 992-1043, 1046-1078, 1081-1137 /usr/lib/python3/dist-packages/yaml/error.py 58 42 28% 15-34, 38-43, 52-56, 59-74 /usr/lib/python3/dist-packages/yaml/events.py 61 6 90% 9-13, 17-19 /usr/lib/python3/dist-packages/yaml/loader.py 47 24 49% 14-19, 34-39, 44-49, 58-63 /usr/lib/python3/dist-packages/yaml/nodes.py 29 7 76% 4-7, 9-23 /usr/lib/python3/dist-packages/yaml/parser.py 352 210 40% 101, 110-111, 157, 163, 167-180, 197-199, 209-215, 218-246, 268, 275-277, 283-291, 293-300, 302-310, 320-323, 333, 338-341, 343-346, 348-351, 357-369, 377-379, 382-398, 403-415, 434-435, 437-438, 453-458, 472-474, 477-500, 503-510, 513-524, 527-529, 538-540, 543-567, 570-581, 584-585, 588 /usr/lib/python3/dist-packages/yaml/reader.py 122 69 43% 27-31, 34-40, 76-85, 90-92, 96, 108-109, 119, 123-135, 141-143, 149-175, 178-185 /usr/lib/python3/dist-packages/yaml/representer.py 248 176 29% 19-24, 27-31, 34-63, 78-83, 86-101, 104-129, 132, 137-142, 145, 148, 151-155, 158-162, 165, 172-189, 199, 207, 210-213, 216-217, 220-221, 224-228, 231, 275-283, 286, 289-290, 293, 313-356, 360-364 /usr/lib/python3/dist-packages/yaml/resolver.py 135 78 42% 30, 33, 51-89, 94-112, 117-118, 122-141, 146, 153, 155-159, 163 /usr/lib/python3/dist-packages/yaml/scanner.py 753 487 35% 119, 129, 133, 138, 177, 181, 185, 195, 199, 203, 207, 211, 215, 219, 227, 231, 235, 239, 243, 251, 258, 290-293, 315-321, 341, 355, 393-400, 403, 406, 411-422, 425, 428, 433-445, 448, 451, 456-468, 473-482, 487-515, 520-543, 572-593, 604-610, 615-621, 626-632, 635, 638, 643-649, 655, 687-688, 693-696, 701-704, 709, 714-719, 725, 773, 779-780, 782-783, 789-804, 808-825, 829-842, 846-855, 859-865, 869-874, 878-883, 887-897, 908-933, 937-974, 979-1049, 1054-1090, 1094-1104, 1108-1119, 1123-1132, 1142, 1151-1152, 1197-1198, 1200-1201, 1203-1223, 1230-1250, 1254-1268, 1288, 1296, 1299, 1308, 1318, 1323-1343, 1345, 1352-1370, 1375-1395, 1399-1414, 1427-1431, 1433-1434 /usr/lib/python3/dist-packages/yaml/serializer.py 85 70 18% 17-25, 28-34, 37-41, 47-58, 61-72, 75-76, 79-110 /usr/lib/python3/dist-packages/yaml/tokens.py 76 17 78% 7-12, 20-23, 78-80, 85-87, 92-94 /usr/local/lib/python3.8/dist-packages/Cython/Shadow.py 292 147 50% 21-26, 29-35, 44-74, 92, 95, 101, 103, 133-135, 142-143, 149-152, 155-158, 164-169, 172, 175, 179, 182-188, 194-198, 201, 203, 215, 230, 232, 236, 239-241, 244-246, 249-254, 257, 262, 268-278, 281-284, 290-305, 308-313, 321-324, 327-331, 334-338, 347-348, 351, 360-377, 382, 458, 461-464, 467 /usr/local/lib/python3.8/dist-packages/Cython/__init__.py 6 2 67% 11-12 /usr/local/lib/python3.8/dist-packages/IPython/__init__.py 32 12 62% 31, 90-98, 125-126, 151-152 /usr/local/lib/python3.8/dist-packages/IPython/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/alias.py 107 66 38% 71-88, 101-108, 130-134, 138-160, 163, 166-186, 199-202, 206-209, 213, 218-221, 229-230, 235-236, 240, 243-246, 249-250, 254-258 /usr/local/lib/python3.8/dist-packages/IPython/core/application.py 251 170 32% 35-39, 58-63, 91-100, 122, 125-126, 136, 145-152, 160, 174-180, 187-191, 203, 222-229, 238-243, 247-251, 260-263, 267-287, 308-350, 355-405, 409-433, 440-445, 450-462 /usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py 68 51 25% 26-28, 31, 40-42, 46-55, 67-73, 83-92, 102-105, 108-122, 133-138, 152-173 /usr/local/lib/python3.8/dist-packages/IPython/core/autocall.py 16 5 69% 40, 48, 57, 69-70 /usr/local/lib/python3.8/dist-packages/IPython/core/builtin_trap.py 48 32 33% 26-34, 40-44, 47-51, 55-63, 67-70, 75-77, 82-86 /usr/local/lib/python3.8/dist-packages/IPython/core/compilerop.py 44 23 48% 59-63, 74-93, 101, 107, 113, 131-136, 141-150, 157-160 /usr/local/lib/python3.8/dist-packages/IPython/core/completer.py 817 691 15% 151-152, 161, 207-209, 225-230, 235-241, 268-281, 287-290, 302-322, 336-341, 344, 373-383, 386, 399, 404, 438-450, 479-504, 508, 543-544, 549, 554-557, 562-563, 617-631, 640-651, 660-679, 696-733, 738-743, 767-812, 840-843, 873-879, 885, 901-917, 926-944, 966-970, 991, 1001-1004, 1044, 1075-1117, 1122-1133, 1146-1151, 1154, 1157, 1178-1240, 1246-1282, 1286-1317, 1321-1331, 1354-1406, 1410-1428, 1437-1454, 1459-1488, 1493-1564, 1568-1672, 1685-1695, 1705-1717, 1720-1765, 1812-1827, 1856-1911, 1952-1957, 1977-2070, 2073-2093 /usr/local/lib/python3.8/dist-packages/IPython/core/crashhandler.py 72 53 26% 126-131, 144-192, 197-213, 218-227 /usr/local/lib/python3.8/dist-packages/IPython/core/debugger.py 324 262 19% 55-59, 68-76, 80-83, 132-165, 173, 189, 219-282, 286-287, 290-293, 296, 300, 305, 309-312, 319-320, 323-335, 339-352, 356-427, 430-457, 463-488, 493-521, 527-533, 540-547, 556-567, 573-575, 581-583, 590-592, 598-600, 606-608, 612-614, 624-628, 639 /usr/local/lib/python3.8/dist-packages/IPython/core/display.py 513 385 25% 36-39, 47-51, 69-74, 109-119, 128, 278-322, 342-343, 362-364, 367, 380, 393, 410, 430, 448, 465, 482, 499, 516, 535, 552, 569, 605-628, 631-636, 640, 644-647, 651-671, 676-677, 682, 688-702, 705, 713, 719, 725-729, 735, 747, 751-766, 769, 782-786, 789-792, 798, 802, 805, 809, 813-814, 817-819, 823-827, 858-866, 869-870, 874, 878-885, 888, 891, 969, 973-980, 1014-1028, 1031-1038, 1046-1048, 1054-1067, 1071, 1151-1213, 1218-1230, 1234-1237, 1240-1248, 1260-1267, 1271-1288, 1291-1292, 1295-1296, 1299-1305, 1358-1380, 1383-1418, 1422, 1432-1439, 1462-1471, 1495-1497 /usr/local/lib/python3.8/dist-packages/IPython/core/display_trap.py 26 15 42% 42-46, 49-52, 55-59, 63-65, 69 /usr/local/lib/python3.8/dist-packages/IPython/core/displayhook.py 157 125 20% 37-59, 63, 75-82, 88-103, 107, 116-119, 151, 171-195, 201-231, 234-235, 239-244, 249-250, 258-268, 272-282, 286-311, 316-319, 322-325 /usr/local/lib/python3.8/dist-packages/IPython/core/displaypub.py 36 23 36% 40-41, 54-58, 106-116, 120-123, 131, 135-138 /usr/local/lib/python3.8/dist-packages/IPython/core/error.py 7 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/events.py 47 26 45% 39-40, 60-63, 67-78, 86-91, 115, 126, 135, 146, 160 /usr/local/lib/python3.8/dist-packages/IPython/core/excolors.py 22 4 82% 164, 174-178 /usr/local/lib/python3.8/dist-packages/IPython/core/extensions.py 63 41 35% 52-56, 60, 63, 72-90, 102-110, 120-130, 133-135, 138-140, 148 /usr/local/lib/python3.8/dist-packages/IPython/core/formatters.py 361 221 39% 45, 49-53, 58, 63, 71-87, 143-193, 198, 211-214, 223-236, 261, 269-271, 276, 334-348, 352-357, 364-368, 391-395, 413-429, 456-468, 498-507, 528-548, 556-564, 640-670, 675, 679-681, 685, 692-704, 829-845, 906-919, 941-947, 958-973, 1018-1020 /usr/local/lib/python3.8/dist-packages/IPython/core/getipython.py 5 1 80% 24 /usr/local/lib/python3.8/dist-packages/IPython/core/history.py 384 277 28% 13-17, 40, 43, 46, 49, 55-58, 65-70, 86-119, 125, 129, 132, 135, 191-198, 214-229, 242, 247-268, 273, 294-303, 330-331, 340-341, 362-369, 395-411, 441-448, 467-469, 488-491, 536-551, 558-559, 564-570, 574-579, 583-584, 590-599, 628-631, 636-651, 681-685, 707-739, 751-758, 761-763, 767-769, 775-802, 816-819, 824-836, 845-847, 868-899, 904-906 /usr/local/lib/python3.8/dist-packages/IPython/core/hooks.py 59 39 34% 66-82, 86, 97-100, 109-117, 120, 124-125, 132, 142, 155, 165, 170, 176-190 /usr/local/lib/python3.8/dist-packages/IPython/core/inputtransformer2.py 359 284 21% 27-32, 40-47, 74, 77-82, 92-99, 108-117, 124-129, 154-155, 182, 185-186, 199, 207, 215-221, 226-238, 247-260, 265-278, 302-312, 320-323, 330-333, 337-338, 342-343, 347-348, 352-353, 370-380, 385-405, 422-424, 430-437, 442-463, 478-503, 507-513, 525-534, 555-571, 574-579, 584-591, 611-714, 718-721 /usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py 1477 1139 23% 95-97, 116-117, 123-124, 139-163, 210-222, 238-248, 252, 260-262, 272-274, 295-298, 301-304, 320, 324, 328-331, 334-335, 388, 392-403, 455-456, 466-467, 475, 487, 501-505, 553-554, 609-611, 630-703, 707, 714, 720-723, 726, 733-737, 740-743, 747-771, 775, 781-784, 790-791, 795, 799-801, 804, 810-815, 824-825, 833-836, 841, 853-856, 861-863, 866-867, 870-871, 874-878, 882-890, 903-945, 956-961, 965-972, 980-985, 988-990, 998-1010, 1024-1056, 1063-1065, 1072-1074, 1080-1081, 1107-1123, 1145, 1154, 1157, 1161-1168, 1184-1191, 1235-1271, 1278, 1304-1322, 1341-1342, 1373-1400, 1409, 1419-1464, 1479-1509, 1521-1531, 1549-1579, 1594-1597, 1610-1691, 1708-1737, 1741, 1748-1764, 1768-1775, 1779, 1787-1794, 1802-1803, 1813-1833, 1875-1933, 1957, 1967-1989, 1996, 2003-2005, 2020-2059, 2067, 2081-2093, 2103, 2114-2116, 2127, 2131, 2145-2166, 2211-2212, 2220-2221, 2225-2230, 2237-2268, 2273, 2291-2318, 2334-2363, 2369, 2375, 2381, 2403-2405, 2423-2429, 2445-2456, 2467-2505, 2529-2537, 2544-2545, 2552-2553, 2560-2561, 2568-2573, 2590-2594, 2606-2616, 2624-2630, 2647-2657, 2665-2666, 2673-2674, 2702-2742, 2760-2801, 2818-2829, 2855-2863, 2867-2893, 2910-2918, 2946-3079, 3095-3109, 3126-3140, 3179-3276, 3291, 3315-3351, 3373-3374, 3383, 3406-3428, 3456-3472, 3487-3505, 3519-3529, 3534-3536, 3541-3543, 3546-3548, 3552, 3577-3578, 3611-3660, 3680-3699, 3702, 3707 /usr/local/lib/python3.8/dist-packages/IPython/core/latex_symbols.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/logger.py 108 92 15% 34-51, 55-57, 60, 70-127, 132-152, 156-165, 182-185, 191-201, 210-215 /usr/local/lib/python3.8/dist-packages/IPython/core/macro.py 28 19 32% 25-36, 39, 42, 46, 49-53 /usr/local/lib/python3.8/dist-packages/IPython/core/magic.py 256 154 40% 55, 64-74, 142, 203, 220-250, 324, 334-339, 343, 351, 363-375, 399-411, 442-445, 469-475, 510-541, 545-546, 552-573, 610-654, 659-661, 675-683, 687-703 /usr/local/lib/python3.8/dist-packages/IPython/core/magic_arguments.py 102 16 84% 130, 135-136, 153, 164, 172, 190, 203-204, 206, 232, 262, 269, 272-274 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/magics/auto.py 38 27 29% 29-31, 51-60, 107-128 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/basic.py 249 188 24% 22-23, 27-38, 41, 44, 51-64, 67, 130-174, 181, 185-193, 210-275, 293-302, 307-309, 326-359, 369-379, 384-386, 415-465, 493-500, 544-546, 567-582, 622-651 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/code.py 304 260 14% 64-81, 100-134, 141-164, 170, 178-179, 209-243, 260-275, 285, 325-377, 383-507, 511-517, 664-730 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/config.py 46 36 22% 32-33, 107-158 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/display.py 29 10 66% 40, 45, 56, 61, 72-77, 82 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/execution.py 581 482 17% 26-31, 57-58, 80-87, 91, 95-96, 99-106, 119-120, 130-131, 135-139, 143-145, 165-173, 183-187, 190, 312-317, 335-389, 409-424, 466-474, 477, 480-485, 497-517, 671-864, 891-958, 973-997, 1077-1189, 1256-1338, 1403-1422, 1449-1456, 1460-1464, 1469-1503 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/extension.py 28 18 36% 31-39, 48-56, 61-63 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/history.py 128 100 22% 135-219, 256-277, 293-318 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/logging.py 65 47 28% 97-162, 171, 178, 189, 195 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/namespace.py 248 206 17% 48-58, 66, 83, 91, 96-98, 114-122, 211-249, 278-291, 339-355, 390-482, 542-608, 671-696, 710-714 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/osm.py 354 287 19% 42-55, 63-66, 75, 82-85, 153-174, 181-192, 208-272, 286-289, 333-423, 437-455, 468-485, 495-501, 507-511, 517, 536-555, 650-671, 715-725, 750-788, 805-815, 833-849 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/packaging.py 49 32 35% 22-23, 30-44, 68-69, 78-103 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/pylab.py 45 23 49% 94-100, 139-159, 165-166 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/script.py 156 119 24% 89-104, 115-119, 122, 125-127, 132-153, 178-245, 249-253, 258-259, 263-291, 294 /usr/local/lib/python3.8/dist-packages/IPython/core/oinspect.py 489 415 15% 50, 86-88, 96-112, 124-132, 154-191, 196, 209-215, 227-231, 238-266, 277-285, 303-319, 338-352, 361-366, 373-376, 380, 384-386, 390-394, 401-415, 453-475, 481-490, 495-512, 531-550, 555-567, 586-656, 680-683, 688-692, 717-905, 915-922, 950-988, 997-1031 /usr/local/lib/python3.8/dist-packages/IPython/core/page.py 174 147 16% 37-43, 51, 62-78, 86-123, 152-234, 248-260, 267-280, 288-302, 311-318, 323-336, 339-343 /usr/local/lib/python3.8/dist-packages/IPython/core/payload.py 19 11 42% 39-49, 52, 55 /usr/local/lib/python3.8/dist-packages/IPython/core/prefilter.py 286 178 38% 67, 121-125, 133-135, 145, 150, 154-156, 160-161, 169-171, 181, 186, 190-192, 196-197, 205-208, 215, 219-221, 225-232, 236, 240, 252-253, 257-262, 266-269, 283-312, 325-337, 355-358, 362, 365, 383-386, 390, 393, 404-407, 415-419, 428-433, 447-451, 464-475, 487-490, 506-524, 540-543, 558-567, 570, 577-580, 590-597, 607-667, 682 /usr/local/lib/python3.8/dist-packages/IPython/core/profiledir.py 109 65 40% 57-69, 82-103, 107, 111-120, 124, 128, 131-134, 143-150, 162, 176-179, 200-207, 220-223 /usr/local/lib/python3.8/dist-packages/IPython/core/pylabtools.py 172 144 16% 71-83, 93-94, 108-136, 140-147, 166-196, 201-206, 221-256, 280-303, 309-327, 342-360, 379-419 /usr/local/lib/python3.8/dist-packages/IPython/core/release.py 25 1 96% 34 /usr/local/lib/python3.8/dist-packages/IPython/core/shellapp.py 226 159 30% 189-191, 205-215, 218, 222-256, 265-286, 290-305, 309-324, 327-354, 358-384, 388-397, 401-427, 431-440 /usr/local/lib/python3.8/dist-packages/IPython/core/splitinput.py 34 23 32% 58-78, 111-119, 134, 137 /usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py 709 610 14% 152, 170-234, 247-300, 311-319, 330-343, 348-374, 399-423, 426-435, 450-475, 488-509, 522, 525-526, 531-542, 545-552, 558-563, 568-574, 578, 585-587, 595, 619, 623-625, 628-631, 662-704, 719-741, 756-813, 823, 835-838, 842-845, 867-881, 885-894, 898-1069, 1074-1093, 1096-1106, 1116-1141, 1144-1165, 1171-1205, 1227-1248, 1251-1257, 1263-1270, 1293-1307, 1310-1324, 1330, 1338-1350, 1354, 1357, 1360, 1363, 1392-1403, 1407-1414, 1425, 1433-1434, 1437-1439, 1446-1454, 1459-1461, 1465, 1472-1495, 1499, 1503 /usr/local/lib/python3.8/dist-packages/IPython/core/usage.py 9 0 100% /usr/local/lib/python3.8/dist-packages/IPython/display.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/extensions/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/IPython/extensions/storemagic.py 114 98 14% 24-31, 35-46, 50, 54-56, 72-75, 121-227, 232 /usr/local/lib/python3.8/dist-packages/IPython/lib/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/lib/backgroundjobs.py 209 164 22% 76-89, 93-94, 98-99, 103-104, 171-197, 200-201, 209, 223-242, 249-254, 261-266, 276-282, 287-293, 298-310, 322-330, 334-337, 340-344, 347-354, 386, 391-417, 420, 423, 426, 429-442, 455-462, 465, 477-488, 491 /usr/local/lib/python3.8/dist-packages/IPython/lib/clipboard.py 41 33 20% 17-33, 38-44, 53-67 /usr/local/lib/python3.8/dist-packages/IPython/lib/display.py 254 202 20% 99-115, 119-128, 133-151, 155-173, 178-194, 198-200, 204-210, 213-219, 223-231, 234-237, 240-243, 261-264, 268-276, 308-310, 314-319, 327-328, 343-344, 381-387, 390-391, 399-405, 410, 471-488, 512-544, 550-566, 575-579, 584-592, 597-605, 627-628, 631-639, 642, 645-649, 652-654 /usr/local/lib/python3.8/dist-packages/IPython/lib/pretty.py 500 393 21% 108-111, 116-117, 127-134, 140-144, 151-155, 162-166, 171-175, 186-197, 200-207, 210-214, 218-229, 237-248, 254-261, 271-276, 280-286, 290-295, 299-302, 309-321, 343-354, 358-399, 409-417, 423, 429-430, 433-435, 438-439, 445-450, 453-461, 467-469, 475-477, 480-483, 486-494, 497-500, 508-538, 547-559, 568-586, 596-608, 614-623, 628-648, 659-678, 684-690, 695-703, 708-718, 724-725, 760-761, 778-782, 803-811, 814-819, 822-827, 831-836, 844-856 /usr/local/lib/python3.8/dist-packages/IPython/lib/security.py 33 25 24% 54-70, 99-114 /usr/local/lib/python3.8/dist-packages/IPython/paths.py 68 52 24% 22-70, 75-84, 101-106, 113-119 /usr/local/lib/python3.8/dist-packages/IPython/terminal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/terminal/debugger.py 77 53 31% 30-32, 35-62, 71-120, 129, 133-141 /usr/local/lib/python3.8/dist-packages/IPython/terminal/embed.py 173 130 25% 66-93, 105-106, 130, 135, 139-145, 149-163, 171, 174-175, 196-237, 267-334, 362-399 /usr/local/lib/python3.8/dist-packages/IPython/terminal/interactiveshell.py 304 183 40% 69-70, 76, 88, 94-98, 123, 154-162, 167-169, 173-179, 184, 187, 211, 219, 263-267, 270-271, 274-278, 281-314, 333-391, 395, 403, 409-443, 446-474, 479-480, 485-499, 502-503, 508-515, 519-524, 527, 533-549, 554-570, 575-576, 580-605, 613-623, 628-633, 640 /usr/local/lib/python3.8/dist-packages/IPython/terminal/ipapp.py 159 71 55% 67-70, 77-91, 173-176, 198, 259-260, 275-276, 280-284, 292-303, 308-323, 331-334, 338-341, 345-348, 351-360, 367-374, 380 /usr/local/lib/python3.8/dist-packages/IPython/terminal/magics.py 89 67 25% 20-35, 41, 46-57, 60-63, 68-78, 83-84, 129-138, 171-195, 199-203 /usr/local/lib/python3.8/dist-packages/IPython/terminal/prompts.py 59 37 37% 15, 18-26, 30, 38, 41-43, 48-49, 54, 62, 67, 72, 75, 80-97, 100-107 /usr/local/lib/python3.8/dist-packages/IPython/terminal/pt_inputhooks/__init__.py 25 15 40% 23, 27, 30, 35-50 /usr/local/lib/python3.8/dist-packages/IPython/terminal/ptutils.py 86 64 26% 38-54, 58-61, 67-70, 74-77, 80-92, 99-133, 140-144, 155-168 /usr/local/lib/python3.8/dist-packages/IPython/terminal/shortcuts.py 140 111 21% 27-28, 34-95, 99-104, 109-152, 161, 170, 174-176, 180-184, 188-191, 194, 200, 203, 215-226, 238-249, 253-254, 258-274 /usr/local/lib/python3.8/dist-packages/IPython/testing/__init__.py 9 5 44% 33-37 /usr/local/lib/python3.8/dist-packages/IPython/testing/skipdoctest.py 3 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/PyColorize.py 110 82 25% 143, 186-196, 200-205, 217-281, 286-325, 330 /usr/local/lib/python3.8/dist-packages/IPython/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/_process_common.py 68 57 16% 34-40, 69-111, 130-133, 151, 171-175, 190-212 /usr/local/lib/python3.8/dist-packages/IPython/utils/_process_posix.py 82 57 30% 37-39, 62-67, 94-97, 115-118, 133-203, 214-224 /usr/local/lib/python3.8/dist-packages/IPython/utils/_sysinfo.py 1 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/capture.py 103 70 32% 18-21, 24-25, 29-35, 38, 41, 44, 47, 50, 53, 56, 59, 76-80, 83, 88-90, 95-97, 110, 114-119, 131-134, 137-163, 166-170 /usr/local/lib/python3.8/dist-packages/IPython/utils/colorable.py 7 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/coloransi.py 67 12 82% 93-94, 122-124, 149, 156, 161, 172-173, 179-180 /usr/local/lib/python3.8/dist-packages/IPython/utils/contexts.py 26 16 38% 37-38, 42-53, 56-60, 70 /usr/local/lib/python3.8/dist-packages/IPython/utils/data.py 6 3 50% 22-23, 28 /usr/local/lib/python3.8/dist-packages/IPython/utils/decorators.py 14 10 29% 38-49 /usr/local/lib/python3.8/dist-packages/IPython/utils/dir2.py 34 28 18% 16-20, 38-51, 64-84 /usr/local/lib/python3.8/dist-packages/IPython/utils/encoding.py 23 7 70% 30, 53-58, 64-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/frame.py 17 11 35% 50-53, 66-67, 81-82, 91-94 /usr/local/lib/python3.8/dist-packages/IPython/utils/generics.py 9 2 78% 12, 30 /usr/local/lib/python3.8/dist-packages/IPython/utils/importstring.py 12 10 17% 27-39 /usr/local/lib/python3.8/dist-packages/IPython/utils/io.py 130 76 42% 28-31, 41-42, 47-49, 52-64, 68-73, 84, 122-132, 136-139, 143-145, 149-150, 153-154, 170-188, 207-211, 216-218, 223-227, 232-236, 246-248 /usr/local/lib/python3.8/dist-packages/IPython/utils/ipstruct.py 76 56 26% 85-88, 111-123, 146-151, 165-166, 180-182, 196-198, 212-215, 223-229, 232, 245, 263, 271, 360-390 /usr/local/lib/python3.8/dist-packages/IPython/utils/module_paths.py 11 8 27% 61-70 /usr/local/lib/python3.8/dist-packages/IPython/utils/openpy.py 45 35 22% 24-40, 46-58, 75-79, 100-103 /usr/local/lib/python3.8/dist-packages/IPython/utils/path.py 191 148 23% 27, 30-53, 57, 67, 76-81, 87-90, 99-109, 146-162, 190-211, 220-230, 239-249, 254-256, 260-262, 266-268, 272-274, 278-280, 297-302, 307-311, 321-327, 341-351, 362-363, 375-382, 395-419, 429-436 /usr/local/lib/python3.8/dist-packages/IPython/utils/process.py 29 17 41% 15, 17, 47-50, 55-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/py3compat.py 108 72 33% 18-19, 22-23, 27-29, 32-34, 38-40, 45-59, 66-76, 93-140, 147, 156-158, 165-168, 179, 189 /usr/local/lib/python3.8/dist-packages/IPython/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/IPython/utils/strdispatch.py 33 23 30% 25-26, 31-33, 38-40, 44-52, 55, 58-61, 65-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/sysinfo.py 40 24 40% 54-65, 81-82, 97-99, 119, 123, 128-129, 134, 152-165 /usr/local/lib/python3.8/dist-packages/IPython/utils/syspathcontext.py 32 22 31% 24, 27-31, 34-40, 46, 49-53, 56-62 /usr/local/lib/python3.8/dist-packages/IPython/utils/tempdir.py 23 12 48% 24-26, 29-30, 35, 38, 51-53, 56-57 /usr/local/lib/python3.8/dist-packages/IPython/utils/terminal.py 59 37 37% 27-33, 53, 58, 62, 68-69, 73, 81-105, 110-112, 117-119, 123-125, 129 /usr/local/lib/python3.8/dist-packages/IPython/utils/text.py 255 192 25% 22, 43-47, 52-56, 61, 66-70, 101, 106-110, 115-119, 124-128, 148-165, 186-201, 215-230, 274-285, 306-309, 329-334, 342-346, 354-356, 397-409, 417-425, 460-473, 484, 509-510, 542-569, 593-610, 618-624, 629-635, 644-647, 702-707, 732-739, 763-770 /usr/local/lib/python3.8/dist-packages/IPython/utils/timing.py 35 24 31% 33, 42, 51-52, 58-67, 83-96, 106, 115 /usr/local/lib/python3.8/dist-packages/IPython/utils/wildcard.py 51 31 39% 44-52, 56, 61-73, 78-85, 92-111 /usr/local/lib/python3.8/dist-packages/MySQLdb/__init__.py 46 13 72% 21, 36-37, 44-46, 63, 66, 69, 72, 75-76, 79 /usr/local/lib/python3.8/dist-packages/MySQLdb/_exceptions.py 12 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/compat.py 12 5 58% 4-8 /usr/local/lib/python3.8/dist-packages/MySQLdb/connections.py 146 31 79% 42, 138, 140, 143, 150, 161, 197, 204, 245, 262, 265, 282, 286-293, 304-308, 314-317, 324-328 /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/CLIENT.py 18 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FIELD_TYPE.py 29 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FLAG.py 16 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/converters.py 35 8 77% 47-48, 52, 56, 65, 79, 82, 85 /usr/local/lib/python3.8/dist-packages/MySQLdb/cursors.py 261 88 66% 83-90, 93, 96-97, 107, 114-120, 127, 142-144, 160, 171, 187, 195-200, 205-206, 227, 259-260, 296-310, 328, 358-363, 368-372, 378, 391-400, 403-405, 417, 421-426, 431-434, 438-441, 444, 447-450 /usr/local/lib/python3.8/dist-packages/MySQLdb/release.py 3 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/times.py 76 47 38% 21, 25, 29, 34-37, 43-47, 53, 60-64, 75-76, 79-99, 102-113, 122-123, 127, 131 /usr/local/lib/python3.8/dist-packages/absl/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/_collections_abc.py 8 1 88% 28 /usr/local/lib/python3.8/dist-packages/absl/_enum_module.py 12 7 42% 48-56 /usr/local/lib/python3.8/dist-packages/absl/app.py 196 138 30% 45-46, 90-91, 100, 105-110, 123, 127-129, 136, 141-143, 157-162, 173-178, 206-225, 232-250, 255-266, 290-321, 344-347, 355-372, 391-426, 441-442, 452, 468-471 /usr/local/lib/python3.8/dist-packages/absl/command_name.py 29 21 28% 30, 46-67 /usr/local/lib/python3.8/dist-packages/absl/flags/__init__.py 84 0 100% /usr/local/lib/python3.8/dist-packages/absl/flags/_argument_parser.py 247 132 47% 64, 113, 119, 127-128, 154, 158-165, 180, 193-207, 211-215, 221, 239, 241, 243, 245, 247, 249, 251, 258-267, 272, 281-286, 294-296, 300, 317, 337, 342-346, 351, 369-378, 392-398, 402, 408, 412, 419-422, 428, 432-445, 453, 468-472, 476-481, 485, 492, 496-509, 513-516, 530-532, 545-552, 555-564 /usr/local/lib/python3.8/dist-packages/absl/flags/_defines.py 120 65 46% 50-56, 107, 136-142, 169-181, 199-206, 289-292, 359, 378-380, 402-405, 436, 460-462, 487-489, 514-516, 542-544, 577, 598-622 /usr/local/lib/python3.8/dist-packages/absl/flags/_exceptions.py 31 13 58% 64-75, 91-99 /usr/local/lib/python3.8/dist-packages/absl/flags/_flag.py 201 104 48% 88, 106, 121, 124, 127-129, 132, 135, 139-141, 154, 162-167, 182-183, 187-189, 193, 197-208, 228, 247-280, 284, 299, 333-337, 345-349, 352-356, 377-378, 388-393, 396-406, 410-420, 424, 427-432, 444-448, 454-458, 462-466 /usr/local/lib/python3.8/dist-packages/absl/flags/_flagvalues.py 525 404 23% 135-136, 139, 169, 201-206, 217-227, 238-247, 260-263, 276-288, 303-314, 329-340, 349, 365-375, 383-393, 403-404, 410, 414, 416, 418, 420, 423-430, 438, 440, 446, 449, 459, 467, 471-491, 495-503, 506-510, 525-531, 554-561, 578-583, 587, 590, 593, 613-637, 640, 643, 647-649, 661, 684-796, 800, 809, 813-819, 823, 827, 840-857, 870-879, 883-886, 890-892, 903-905, 916-918, 926, 929-963, 976-980, 984-995, 1013-1018, 1041-1086, 1126-1168, 1183-1190, 1203-1204, 1219-1255, 1259, 1264 /usr/local/lib/python3.8/dist-packages/absl/flags/_helpers.py 164 117 29% 29-30, 34-35, 127, 132, 157-158, 174-186, 191-204, 210-233, 238-261, 284-318, 339-356, 371-398, 407-424, 428-431 /usr/local/lib/python3.8/dist-packages/absl/flags/_validators.py 94 61 35% 64-68, 80-82, 90, 93, 104, 128-129, 132, 135, 145, 171-172, 183, 186-190, 193, 222-223, 252-257, 286-288, 320-327, 355-360, 383-384, 405-420, 435-449, 462-463 /usr/local/lib/python3.8/dist-packages/absl/logging/__init__.py 406 221 46% 96, 169, 181-194, 208, 215-216, 220, 223, 266, 280-284, 298-303, 312, 317, 322, 326, 331-333, 338, 343, 348, 382-383, 408-414, 429-430, 473, 476, 499, 515-524, 529, 534, 539, 544, 552, 568-575, 596-620, 633-647, 656-668, 704-718, 722, 727, 744-772, 776-779, 783-790, 803-808, 827-850, 854-870, 883, 886, 889, 892, 895-896, 899-902, 906, 913, 916, 931-938, 989, 993-996, 998, 1002, 1006, 1010, 1014-1017, 1021, 1025, 1029, 1046-1047, 1064-1065, 1083-1086, 1099-1100, 1110, 1119-1121, 1133-1148, 1156 /usr/local/lib/python3.8/dist-packages/absl/logging/converter.py 62 32 48% 107-114, 129-135, 151, 153, 157, 169, 184-200, 215 /usr/local/lib/python3.8/dist-packages/absl/testing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/testing/_pretty_print_reporter.py 50 28 44% 31-32, 35-40, 43-44, 47-48, 51-52, 55-56, 59-60, 63-64, 67-68, 84-87, 91-93, 96 /usr/local/lib/python3.8/dist-packages/absl/testing/absltest.py 885 696 21% 52-54, 75-76, 82-88, 94-97, 131-135, 210-223, 270, 276-278, 300-303, 315, 321, 348-350, 363-371, 383, 391-417, 423, 428-429, 434-435, 449-452, 463-464, 482-488, 504-510, 518-520, 538-542, 545-549, 575-590, 634-639, 661-665, 670, 674, 678-680, 684-692, 696-700, 704-707, 722-732, 742-743, 753-754, 764-765, 775-776, 792-817, 827-834, 843-850, 860-865, 890-911, 915-919, 924-928, 942-946, 951-978, 1004-1032, 1038-1065, 1069-1073, 1078-1085, 1119-1146, 1164-1182, 1210-1224, 1240-1243, 1246, 1249-1255, 1275-1283, 1311-1323, 1336-1350, 1364-1376, 1391-1405, 1449-1529, 1542-1607, 1611-1619, 1647-1658, 1671-1685, 1690-1700, 1704, 1724-1754, 1759, 1764, 1771, 1776, 1782-1836, 1847-1858, 1876-1892, 1905-1910, 1920-1921, 1928, 1947-1948, 1954-1959, 1966-1968, 1971-1972, 1978-1984, 2028-2065, 2071-2077, 2097-2104, 2108-2117, 2122-2127, 2145-2149, 2179-2220, 2230-2312, 2333-2335, 2340-2344, 2349-2352, 2357-2365, 2370-2371 /usr/local/lib/python3.8/dist-packages/absl/testing/parameterized.py 151 108 28% 188, 197, 201, 207-213, 238-244, 247, 255-327, 331-345, 361-387, 406, 428, 449-458, 469-473, 492-509, 516, 529, 559-563 /usr/local/lib/python3.8/dist-packages/absl/testing/xml_reporter.py 247 180 27% 64, 78-80, 92-99, 112-117, 127, 151-186, 189, 192, 206-223, 226-231, 239-243, 246-269, 272-304, 312-316, 324, 332, 346-353, 356-357, 361-373, 376-377, 380-398, 413-415, 439-447, 450-452, 455-456, 459-460, 463-465, 468-470, 473-474, 477-480, 483-488, 491-501, 504-506, 531-533, 548, 551-554 /usr/local/lib/python3.8/dist-packages/absl/third_party/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/case.py 187 177 5% 15-63, 70-235, 239-273 /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/result.py 16 9 44% 16-25 /usr/local/lib/python3.8/dist-packages/appdirs.py 257 211 18% 29-39, 77-97, 131-163, 195-203, 236-254, 291-300, 302-304, 310, 345-353, 388-404, 411-415, 419, 424, 429, 434, 439, 444, 449, 460-476, 480-503, 507-530, 533-556, 559-571, 577-608 /usr/local/lib/python3.8/dist-packages/astunparse/__init__.py 13 3 77% 18-20 /usr/local/lib/python3.8/dist-packages/astunparse/printer.py 37 28 24% 10-12, 16, 19, 23-51 /usr/local/lib/python3.8/dist-packages/astunparse/unparser.py 718 440 39% 77-78, 81-82, 85, 93-97, 100-101, 123-126, 129-139, 151, 154, 157-158, 161-165, 168-175, 178-189, 192-193, 196-197, 200-205, 208-213, 216-221, 232-243, 253-256, 258-261, 264-275, 278-290, 295-296, 298-302, 308-344, 350, 362-363, 369, 372, 375-386, 389-408, 411-420, 427-430, 439, 443, 446-459, 463-479, 483-486, 489-491, 494-495, 498-500, 503-518, 524, 527-529, 534, 541-547, 549, 552, 556-568, 576-580, 583-587, 590-594, 597-603, 606-615, 618-624, 627-630, 633-649, 663-677, 701-704, 712, 729-738, 748-749, 753, 759-766, 769, 775-776, 793, 797-810, 814-820, 824-835, 857, 866-875, 880-895, 898-903, 906 /usr/local/lib/python3.8/dist-packages/backcall/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/backcall/backcall.py 66 48 27% 12-13, 18-25, 40, 44, 51-106 /usr/local/lib/python3.8/dist-packages/boto/__init__.py 281 191 32% 75, 88-97, 102-111, 125-126, 140-141, 155-156, 170-171, 185-186, 206-207, 223-224, 239-240, 254-255, 270-271, 286-287, 302-303, 317-318, 332-333, 351-352, 366-367, 381-382, 397-398, 414-415, 437-453, 470-471, 494-508, 530-545, 562-563, 577-578, 599-607, 626-627, 643-644, 660-661, 683-684, 702-703, 720-721, 737-738, 747-748, 767-768, 788-789, 811-812, 834-835, 856-857, 878-879, 901-902, 924-925, 947-948, 970-971, 993-994, 1016-1017, 1039-1040, 1062-1063, 1078-1079, 1094-1095, 1143-1197, 1209-1214 /usr/local/lib/python3.8/dist-packages/boto/auth.py 584 467 20% 49-51, 102-105, 108-115, 118-121, 124-128, 132-134, 137-140, 143-144, 155, 158, 167-169, 172-173, 176-193, 203-205, 208-209, 212-221, 232-233, 236-247, 258-259, 266-271, 280-282, 290-298, 309-322, 334-340, 343-350, 357-367, 370-374, 377-383, 388-395, 404-414, 417-419, 422-430, 433-441, 444-451, 454-459, 462, 465-479, 482-487, 490-504, 512-516, 519-525, 536-582, 592-595, 598-601, 606-611, 617-622, 625-629, 636-645, 658-690, 696, 703-738, 741-744, 747-753, 764-806, 825, 828-834, 837-845, 856-874, 885-896, 908-910, 913-924, 935-955, 967-982, 1007-1033, 1037-1054, 1059-1098 /usr/local/lib/python3.8/dist-packages/boto/auth_handler.py 10 2 80% 52, 60 /usr/local/lib/python3.8/dist-packages/boto/cacerts/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/compat.py 47 26 45% 28-29, 35-36, 45-47, 68-102 /usr/local/lib/python3.8/dist-packages/boto/connection.py 605 493 19% 79-80, 84-85, 123, 131, 138, 146-158, 173-181, 189-190, 197-199, 234-238, 243-246, 249, 255, 264-269, 276-280, 290-300, 341-358, 361, 367-384, 390-391, 402-413, 474-572, 575, 578, 581, 587, 591, 594, 598, 602, 608, 614, 622-642, 645-662, 665-698, 701-705, 708-721, 724-778, 781, 784-851, 854-855, 858-859, 863-874, 877-881, 884, 897-1033, 1037-1059, 1066-1070, 1077-1078, 1091, 1103, 1106, 1109-1116, 1119-1122, 1158-1162, 1168-1186, 1190-1208, 1211-1227 /usr/local/lib/python3.8/dist-packages/boto/endpoints.py 79 57 28% 44-50, 55-56, 61-78, 81-82, 87-92, 100-103, 118-121, 126, 130, 149, 163-166, 177, 186, 197, 210-222, 227-232, 237-239 /usr/local/lib/python3.8/dist-packages/boto/exception.py 287 166 42% 42-43, 46, 49, 79-135, 138-142, 145-148, 151, 155, 159, 162-170, 173-176, 181-185, 188, 191-196, 204-205, 208-211, 254-256, 259, 262-267, 270-272, 280-281, 284, 287, 295-296, 299, 303-306, 310-312, 334-340, 343-347, 350-353, 356-359, 376-383, 403-405, 408, 411-416, 458-459, 466-467, 474-475, 482-483, 490-491, 532-534, 537, 549-551, 554, 565-566, 574-575, 578, 592-593 /usr/local/lib/python3.8/dist-packages/boto/gs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/gs/acl.py 187 133 29% 58-59, 63, 67-75, 80-82, 87-88, 91-93, 96-97, 100-107, 110-115, 118-126, 132-135, 138-141, 144-149, 152-155, 158-164, 172-175, 178, 181-205, 208-216, 219-223, 243-250, 254-264, 267-271, 274-284, 287-308 /usr/local/lib/python3.8/dist-packages/boto/gs/user.py 26 20 23% 25-29, 32, 35, 38-43, 46-54 /usr/local/lib/python3.8/dist-packages/boto/handler.py 29 19 34% 30-32, 35-38, 41-46, 49, 54-57, 60 /usr/local/lib/python3.8/dist-packages/boto/https_connection.py 49 34 31% 41-44, 47, 59-62, 75-83, 105-114, 118-135 /usr/local/lib/python3.8/dist-packages/boto/jsonresponse.py 108 89 18% 30-32, 35-41, 44-47, 50, 53-55, 64-74, 77-86, 89-91, 94-109, 112-119, 127-132, 135-137, 140-155, 158-168 /usr/local/lib/python3.8/dist-packages/boto/plugin.py 38 22 42% 53-56, 60-66, 70-78, 86, 91-93 /usr/local/lib/python3.8/dist-packages/boto/provider.py 247 178 28% 183-214, 217-219, 222, 227-229, 232, 237-239, 242, 247-263, 267-378, 383-402, 413-434, 437-441, 444-465, 468-473, 476, 479, 484 /usr/local/lib/python3.8/dist-packages/boto/pyami/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/pyami/config.py 158 108 32% 42, 47-49, 59, 61, 65-69, 78, 88-93, 96-103, 111-121, 124, 127, 130-134, 137-141, 144-148, 151, 166-169, 172-180, 183-186, 189-191, 194-202, 205-217, 220-235 /usr/local/lib/python3.8/dist-packages/boto/regioninfo.py 89 66 26% 44, 56-57, 77-82, 100-115, 121-134, 161-182, 208-220, 226-229, 235-239, 247-249, 259-262, 265, 268, 271-276, 289-290 /usr/local/lib/python3.8/dist-packages/boto/resultset.py 111 98 12% 47-62, 65-76, 79-82, 85-134, 140-142, 145-148, 151, 154, 157-160, 163-176 /usr/local/lib/python3.8/dist-packages/boto/s3/__init__.py 17 11 35% 43-44, 54-55, 63-74 /usr/local/lib/python3.8/dist-packages/boto/s3/acl.py 111 89 20% 34-36, 39-51, 54-64, 67-72, 75-82, 88-89, 92, 95-97, 100-101, 104-108, 111-114, 117-121, 130-135, 138-140, 143-156, 159-171 /usr/local/lib/python3.8/dist-packages/boto/s3/bucket.py 700 576 18% 71, 95-97, 100, 103, 106, 109, 112-117, 131, 143, 175-194, 197-231, 282, 328, 363, 369-390, 394-411, 424-426, 469-472, 521-522, 535, 606-609, 623-625, 630, 662-730, 757-759, 766-788, 847-889, 894-908, 912-922, 926-936, 940-944, 948-963, 989-1002, 1028-1040, 1043-1046, 1072-1080, 1110-1119, 1123-1124, 1134-1146, 1162-1171, 1193-1197, 1206-1207, 1216-1227, 1235-1240, 1243-1249, 1253-1260, 1288-1308, 1323-1339, 1350-1366, 1377-1389, 1396-1404, 1438-1441, 1448, 1454-1461, 1483, 1489-1493, 1522-1526, 1530-1538, 1544-1551, 1560-1563, 1570-1576, 1586-1594, 1598-1606, 1618-1632, 1644, 1651-1658, 1669-1673, 1679-1687, 1736-1767, 1775-1806, 1815-1822, 1826, 1829-1835, 1838-1845, 1849-1864, 1867, 1870-1878 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlistresultset.py 67 54 19% 29-41, 54-59, 62, 73-86, 99-105, 108, 121-134, 147-151, 154 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlogging.py 50 41 18% 28-33, 36-47, 50, 53-57, 60-65, 69-83 /usr/local/lib/python3.8/dist-packages/boto/s3/connection.py 282 212 25% 58-62, 67-69, 76, 79-82, 85-88, 91-95, 98-99, 106, 113, 119, 122-126, 132-135, 176-199, 205-208, 211-212, 215, 226, 232-237, 298-355, 361-380, 386-438, 443-453, 468-469, 508-511, 528-555, 577-581, 606-627, 644-647, 653-667 /usr/local/lib/python3.8/dist-packages/boto/s3/cors.py 80 69 14% 65-78, 81, 84, 87-100, 103-117, 126-130, 133, 140-144, 194-210 /usr/local/lib/python3.8/dist-packages/boto/s3/deletemarker.py 28 23 18% 26-31, 34-38, 41-55 /usr/local/lib/python3.8/dist-packages/boto/s3/key.py 690 590 14% 106-135, 138-147, 150, 154-157, 160, 163, 168-169, 172-175, 180-184, 187-192, 197-207, 210, 219-223, 226-231, 234-242, 246-259, 262-273, 280, 304-334, 348, 352-361, 381-385, 397-402, 408-415, 441-451, 501-507, 515-519, 522-542, 551, 557, 561, 566-573, 576, 580-581, 584-585, 588-589, 592-593, 596, 605-610, 624-635, 639, 695-713, 760, 767-966, 969-1021, 1036-1045, 1108-1132, 1214-1311, 1374-1375, 1437-1444, 1493, 1503-1575, 1602, 1658-1664, 1722-1739, 1795-1804, 1829-1831, 1853-1856, 1859-1867, 1875-1889, 1893-1912, 1929-1935 /usr/local/lib/python3.8/dist-packages/boto/s3/keyfile.py 74 53 28% 35-44, 47-49, 52-85, 88-89, 92-94, 97, 102, 107, 110, 113, 116, 119, 122, 125, 128, 131, 134 /usr/local/lib/python3.8/dist-packages/boto/s3/lifecycle.py 148 113 24% 48-65, 68, 71-76, 79-86, 89-99, 111-112, 115, 118-121, 124-128, 131-137, 152-154, 157-161, 164-171, 178-182, 185, 188-203, 210-213, 230-231, 234-236, 242, 246, 250, 259-263, 266, 273-278, 310-311 /usr/local/lib/python3.8/dist-packages/boto/s3/multidelete.py 64 48 25% 41-44, 47-50, 53, 56-66, 82-85, 88-92, 95, 98-107, 121-123, 126-134, 137 /usr/local/lib/python3.8/dist-packages/boto/s3/multipart.py 160 133 17% 46-52, 55, 59, 62-71, 86-90, 93-96, 99, 102-111, 118-125, 134-146, 149, 152, 155-162, 165-175, 178-200, 211-226, 253-261, 290-301, 317-318, 330 /usr/local/lib/python3.8/dist-packages/boto/s3/prefix.py 16 10 38% 24-25, 28, 31-34, 38-41 /usr/local/lib/python3.8/dist-packages/boto/s3/tagging.py 52 34 35% 7-8, 11, 14-17, 20, 24, 29-33, 36, 39-40, 43-47, 54-58, 61, 64-68, 71 /usr/local/lib/python3.8/dist-packages/boto/s3/user.py 23 18 22% 24-28, 31, 34-39, 42-49 /usr/local/lib/python3.8/dist-packages/boto/s3/website.py 122 85 30% 24-26, 57-63, 66-72, 75, 78-89, 94-98, 101, 104-106, 109-114, 131-133, 136, 152-153, 156-159, 162, 165, 168-171, 191-192, 195-198, 201, 204-209, 213, 218-223, 245-247, 250, 283-288, 291 /usr/local/lib/python3.8/dist-packages/boto/storage_uri.py 488 377 23% 57, 62, 66, 69-70, 77-78, 82-83, 87-89, 103-149, 152, 158-160, 165-172, 176-177, 180-184, 187-191, 194-196, 199-203, 209-218, 224-227, 231-234, 237-240, 288-299, 302-318, 321, 328-332, 335-344, 348-355, 365-366, 379-390, 402-413, 417-421, 425-429, 433-438, 441-443, 446-453, 457-464, 468-470, 475-491, 496-504, 508-515, 518-520, 524, 528, 536, 540, 544, 548, 552, 556, 560, 564, 568-576, 579-581, 584-585, 588-591, 596-605, 610-619, 624-625, 630-631, 636-640, 646-649, 653-655, 661-675, 680-696, 700-706, 713-724, 733-735, 738-740, 743-745, 749-754, 757-759, 762-764, 767-769, 773, 779-787, 791-795, 800-802, 805-811, 816-822, 826-833, 838-840, 845-849, 875-880, 889, 893, 897, 901, 905, 909-911, 915, 919, 923, 927, 932, 937, 944 /usr/local/lib/python3.8/dist-packages/boto/utils.py 575 467 19% 108-111, 119-169, 173-183, 187-202, 212-237, 241, 246-266, 269-270, 273-337, 340-343, 346-347, 350-351, 354-355, 358-359, 383, 399-405, 413-427, 432-441, 454-460, 464-466, 470-481, 485-498, 505-508, 518-543, 549-554, 557-576, 579, 582, 588, 616-619, 629-646, 689-691, 694, 697-700, 703, 706-709, 712, 715-717, 720-728, 731, 734-741, 744-750, 753-765, 780-782, 785-787, 790, 793-797, 800-803, 808-859, 863-872, 876-881, 897-899, 920-944, 959-972, 1000, 1004-1029, 1038, 1048-1049, 1060, 1070-1083, 1093-1098 /usr/local/lib/python3.8/dist-packages/boto/vendored/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/exceptions.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/regions.py 81 60 26% 58, 65, 85, 94-96, 99-102, 106-116, 120-124, 128-152, 156-160, 163-177, 180-182, 186 /usr/local/lib/python3.8/dist-packages/boto/vendored/six.py 444 208 53% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /usr/local/lib/python3.8/dist-packages/cachetools/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/cachetools/abc.py 25 14 44% 21-24, 29-36, 39-43 /usr/local/lib/python3.8/dist-packages/cachetools/cache.py 60 38 37% 6, 9, 12, 21-27, 30, 38-41, 44-57, 60-62, 65, 68, 71, 74, 79, 84, 89 /usr/local/lib/python3.8/dist-packages/cachetools/decorators.py 73 69 5% 11-44, 52-88 /usr/local/lib/python3.8/dist-packages/cachetools/keys.py 24 14 42% 17-20, 23, 26, 29, 40-43, 49-52 /usr/local/lib/python3.8/dist-packages/cachetools/lfu.py 22 14 36% 10-11, 14-16, 19-20, 23-24, 28-33 /usr/local/lib/python3.8/dist-packages/cachetools/lru.py 27 18 33% 10-11, 14-16, 19-20, 23-24, 28-33, 36-39 /usr/local/lib/python3.8/dist-packages/cachetools/rr.py 19 11 42% 8, 15-20, 25, 29-34 /usr/local/lib/python3.8/dist-packages/cachetools/ttl.py 156 118 24% 12-13, 16, 19-22, 28-29, 32-35, 38-43, 46, 49, 52, 59-64, 67-72, 75-84, 87-99, 102-106, 109-116, 119-126, 129-136, 139-141, 145-147, 152, 157, 161-172, 175-177, 180-181, 184-185, 188-189, 196-203, 206-208 /usr/local/lib/python3.8/dist-packages/cycler.py 177 107 40% 73, 110, 118, 122, 127, 131, 157-178, 185-189, 218-223, 229, 240-243, 255-262, 265, 268-273, 284-292, 303-311, 317-322, 325-333, 337-347, 396-397, 425, 454-465, 509, 513-516, 519-526, 548-556 /usr/local/lib/python3.8/dist-packages/cython.py 10 6 40% 9-17 /usr/local/lib/python3.8/dist-packages/decorator.py 270 120 56% 49-60, 64-67, 70-73, 100, 114-115, 117, 119-120, 133, 139, 143, 155-156, 168, 174, 186-189, 217, 233-235, 241, 246, 271, 279, 282-283, 291, 299-300, 306, 314-316, 318, 321, 329, 339-348, 357-454 /usr/local/lib/python3.8/dist-packages/defusedxml/ElementTree.py 64 25 61% 21-23, 52, 79-105, 108, 113, 120 /usr/local/lib/python3.8/dist-packages/defusedxml/__init__.py 21 15 29% 25-51 /usr/local/lib/python3.8/dist-packages/defusedxml/common.py 65 42 35% 23, 31-34, 37-38, 46-52, 55-56, 64-68, 71-72, 81-90, 98-105, 115-122, 125-132 /usr/local/lib/python3.8/dist-packages/gast/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/gast/ast3.py 192 145 24% 10-160, 174, 189-194, 200-207, 214-232, 239, 260-264, 270-306, 310-376, 391 /usr/local/lib/python3.8/dist-packages/gast/astn.py 24 1 96% 21 /usr/local/lib/python3.8/dist-packages/gast/gast.py 78 46 41% 9-11, 292, 302-304, 308-316, 327-331, 342-366, 375-380 /usr/local/lib/python3.8/dist-packages/h5py/__init__.py 57 23 60% 27-32, 37, 89-90, 97-113 /usr/local/lib/python3.8/dist-packages/h5py/_hl/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/h5py/_hl/attrs.py 133 92 31% 63, 76-78, 90, 100, 105, 122-221, 232-247, 253, 257-275, 284-286 /usr/local/lib/python3.8/dist-packages/h5py/_hl/base.py 216 108 50% 23-24, 40-45, 53-63, 88, 112, 126-128, 131, 134, 139-141, 144, 155-162, 185, 188-193, 197-199, 204-207, 219-221, 227, 237-239, 251, 265, 281, 285-287, 291, 294-295, 308, 312, 326, 340-344, 347-349, 359-363, 366-368, 381-404, 409, 413, 417, 421, 447, 450-452, 455 /usr/local/lib/python3.8/dist-packages/h5py/_hl/compat.py 70 44 37% 12-41, 51, 56-57, 67-72, 80-85, 94-95, 99-100, 113-115, 127-134 /usr/local/lib/python3.8/dist-packages/h5py/_hl/dataset.py 474 340 28% 47-54, 68-172, 183-205, 216-217, 221, 225, 229-244, 260, 263-267, 272-274, 280, 291, 297-299, 311-313, 319-322, 328-331, 337, 343, 349, 358-361, 369-376, 383-385, 391-393, 400, 423-438, 447-450, 458-462, 470-474, 488-582, 592-708, 720, 724, 730, 743-758, 770, 777-790, 800, 811, 817, 821-824, 841 /usr/local/lib/python3.8/dist-packages/h5py/_hl/datatype.py 24 11 54% 37, 43-45, 49-56 /usr/local/lib/python3.8/dist-packages/h5py/_hl/files.py 238 113 53% 46-48, 52, 78, 89, 95, 103-107, 115, 117, 119, 125-127, 130-137, 143-146, 158-167, 172, 174-206, 210-215, 239, 245-253, 259, 266-268, 274-275, 281-282, 287-298, 304, 310-314, 379, 382-383, 386-393, 400-402, 411, 418, 438-439, 451-452, 465-478 /usr/local/lib/python3.8/dist-packages/h5py/_hl/filters.py 175 141 19% 78-84, 88-101, 113-244, 263-283, 302-342 /usr/local/lib/python3.8/dist-packages/h5py/_hl/group.py 234 168 28% 41, 62-69, 132-140, 158-174, 189-206, 223-238, 247-253, 260-262, 271-274, 301-342, 368-393, 399, 404, 409-410, 415, 455-494, 504-507, 530-534, 560-565, 569-579, 603, 606, 609, 622, 627, 630-631, 634 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections.py 295 221 25% 58-95, 111, 117-118, 150-151, 171, 176, 180-182, 185, 197-207, 211-218, 222, 226, 230, 252-269, 279-283, 293, 299, 304, 315-320, 337, 340-341, 345-414, 417-419, 424-442, 453-475, 482-488, 495-508, 519-598 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections2.py 43 33 23% 26-45, 57-74, 83-91, 94-95, 102-105 /usr/local/lib/python3.8/dist-packages/h5py/_hl/vds.py 52 33 37% 56-90, 94, 97-99, 120-123, 126-127 /usr/local/lib/python3.8/dist-packages/h5py/h5py_warnings.py 19 6 68% 16-17, 34-36, 39 /usr/local/lib/python3.8/dist-packages/h5py/version.py 21 3 86% 32, 34, 36 /usr/local/lib/python3.8/dist-packages/imagecodecs/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/imagecodecs/imagecodecs.py 300 226 25% 330, 350-351, 356, 371, 375-383, 387, 390, 395, 401-402, 408-479, 484, 489-510, 515-603, 608-641, 646-666, 675, 684, 689, 698, 703-706, 711-722, 727-734, 745-788, 796-802, 814 /usr/local/lib/python3.8/dist-packages/imageio/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/fetching.py 105 88 16% 62-114, 150-184, 210-225, 230-232, 237-247 /usr/local/lib/python3.8/dist-packages/imageio/core/findlib.py 74 65 12% 23-29, 37-81, 99-160 /usr/local/lib/python3.8/dist-packages/imageio/core/format.py 276 178 36% 94, 103, 108, 111, 115, 118, 126, 142, 149, 155, 164-170, 179-185, 192, 199, 216-221, 228, 235, 238-239, 242-244, 247-248, 256-261, 267, 272-275, 289, 300, 331, 343-349, 359, 367-370, 386-392, 401-414, 419, 422-425, 437, 447, 458, 484-502, 517-521, 527, 531, 551, 557, 560-565, 569-609, 633, 635, 655, 657, 659-665, 678-697, 705-725, 735 /usr/local/lib/python3.8/dist-packages/imageio/core/functions.py 157 124 21% 100-121, 139-142, 172-186, 213-231, 259-267, 291-308, 354-374, 397-427, 453-455, 479-496, 542-561, 586-615 /usr/local/lib/python3.8/dist-packages/imageio/core/request.py 319 274 14% 19-20, 89-128, 133-262, 271, 279, 289, 295, 310-350, 359-372, 381-421, 427-428, 435-437, 440-467, 476-482, 492-496, 501-530, 533, 539-561, 564-565, 568, 571 /usr/local/lib/python3.8/dist-packages/imageio/core/util.py 263 210 20% 30-34, 38-42, 55-108, 122-134, 139-143, 149, 155-158, 164-169, 180-185, 205-211, 214-225, 228-230, 252-257, 265-273, 281, 290-309, 316, 324-328, 335-339, 346-350, 355, 358, 361, 364, 376-383, 387-395, 398-400, 404-408, 423-466, 479-491, 506-520, 531-542, 548-555 /usr/local/lib/python3.8/dist-packages/imageio/plugins/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/imageio/plugins/_freeimage.py 603 405 33% 63-67, 74-87, 422, 429-432, 439-447, 450-454, 461-480, 485-514, 518-521, 526-527, 530-531, 537, 544-549, 555-557, 564, 573-603, 609, 615, 620-625, 628, 631-640, 645-652, 660-723, 728-797, 807-839, 842-856, 876-895, 941-975, 980-1031, 1041-1083, 1089-1110, 1113-1163, 1170-1187, 1220-1245, 1271-1295, 1298-1299, 1305-1321, 1326-1328 /usr/local/lib/python3.8/dist-packages/imageio/plugins/bsdf.py 140 113 19% 14-52, 62-63, 66-73, 76, 126-130, 133-135, 143-180, 185, 188-194, 198-231, 240-249, 256-257, 261-289 /usr/local/lib/python3.8/dist-packages/imageio/plugins/dicom.py 147 122 17% 31-33, 41-56, 89-100, 105, 111-155, 159-161, 165-168, 171-199, 202-231, 239-266 /usr/local/lib/python3.8/dist-packages/imageio/plugins/example.py 43 23 47% 54-56, 67-69, 82-84, 89, 93, 97-106, 111, 124, 129, 133, 138 /usr/local/lib/python3.8/dist-packages/imageio/plugins/feisem.py 41 32 22% 27, 38-41, 55-84 /usr/local/lib/python3.8/dist-packages/imageio/plugins/ffmpeg.py 304 258 15% 27, 30-31, 57-66, 178-189, 192-194, 204-239, 257-327, 339-344, 357-358, 361, 370-386, 389, 394-468, 472-474, 478-502, 523-527, 530-532, 537-572, 575, 582-617, 629-636, 639-641, 646-649, 653-661, 667-698 /usr/local/lib/python3.8/dist-packages/imageio/plugins/fits.py 40 24 40% 15-23, 80, 84, 90-102, 105, 108, 112-116, 120 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimage.py 177 122 31% 48, 52-59, 63-70, 76, 79-80, 83, 86-88, 91-93, 99-102, 106-109, 113-132, 135, 168-174, 177-178, 215-219, 225-240, 243-259, 303-309, 312-314, 323-340, 349-362, 365-368, 393-397, 485-510 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimagemulti.py 144 104 28% 27-32, 35, 38, 41-45, 48-55, 62-67, 71, 75-86, 90-94, 97, 137-140, 197-200, 203-205, 225-254, 263-299, 308-322 /usr/local/lib/python3.8/dist-packages/imageio/plugins/gdal.py 35 19 46% 15-23, 40-43, 46, 52-54, 57, 60, 63-65, 68 /usr/local/lib/python3.8/dist-packages/imageio/plugins/grab.py 63 37 41% 25, 28-40, 44, 47, 50, 66-70, 73-79, 95-99, 102-111 /usr/local/lib/python3.8/dist-packages/imageio/plugins/lytro.py 304 225 26% 63, 69, 74, 78, 83, 101-103, 109-136, 142-143, 148, 152, 157-171, 177-195, 212-214, 220-280, 285, 289, 295-302, 308-312, 319-325, 348-371, 375-383, 388-391, 410-412, 418-435, 441-442, 447, 451, 456-470, 476-494, 511-513, 519-559, 564, 568, 574-582, 588-592, 599-607, 630-653, 657-665, 670-673 /usr/local/lib/python3.8/dist-packages/imageio/plugins/npz.py 37 17 54% 40, 44, 51-55, 58, 61, 65-69, 73, 81, 85, 88, 91 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillow.py 365 285 22% 76, 79-99, 102-108, 111-115, 119-146, 149-150, 153-155, 159, 162-165, 168-181, 184-186, 190-197, 200, 203-218, 221, 298, 301-315, 323-357, 360-364, 429, 433-440, 443-455, 461-478, 486-496, 499-503, 564, 568-575, 578-590, 596-613, 621-639, 642-648, 653-655, 667-680, 684-695, 705-792, 797-835 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillow_info.py 5 1 80% 100 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillowmulti.py 164 134 18% 60, 74-101, 106, 109-118, 138-151, 156-177, 181-186, 189, 193-223, 228-232, 245-262, 268-279, 297-305, 317-337, 347-364 /usr/local/lib/python3.8/dist-packages/imageio/plugins/simpleitk.py 62 38 39% 15-37, 95-98, 101-104, 110-112, 115, 118, 122-127, 130-131, 136-137, 140, 143-144, 147-148 /usr/local/lib/python3.8/dist-packages/imageio/plugins/spe.py 126 96 24% 255, 260, 264-295, 298-303, 307, 310-370, 373-399, 402-405, 408-411, 414-438, 456-465 /usr/local/lib/python3.8/dist-packages/imageio/plugins/swf.py 179 147 18% 25-27, 68-71, 74-76, 82-138, 141, 144, 147-150, 154-179, 188-215, 218, 224-239, 242-267, 270-288, 292-298, 302-320, 323 /usr/local/lib/python3.8/dist-packages/imageio/plugins/tifffile.py 91 65 29% 19-23, 208, 212, 218-229, 232-234, 237-240, 243-256, 259-276, 281-297, 300, 303-311, 314-322 /usr/local/lib/python3.8/dist-packages/ipykernel/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipykernel/_version.py 11 5 55% 7-11, 13 /usr/local/lib/python3.8/dist-packages/ipykernel/comm/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipykernel/comm/comm.py 86 52 40% 21-22, 28, 40, 51-59, 63-66, 76, 82-99, 103-118, 122, 135, 144, 150-152, 156-163 /usr/local/lib/python3.8/dist-packages/ipykernel/comm/manager.py 72 52 28% 37-40, 44, 48-51, 56, 66-72, 77-99, 104-113, 117-129 /usr/local/lib/python3.8/dist-packages/ipykernel/connect.py 58 41 29% 31-37, 58-81, 91-103, 128-137, 163-178 /usr/local/lib/python3.8/dist-packages/ipykernel/jsonutil.py 81 59 27% 73-106, 133-197 /usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py 441 316 28% 20-22, 61-63, 80, 163-171, 176-209, 216-223, 228-282, 287, 291, 295-331, 343-349, 358-365, 377-383, 393, 397-408, 412-441, 450, 459, 466, 482-483, 495, 505, 514, 520-569, 575, 579-585, 591, 599-611, 616, 620-627, 633, 636-643, 647, 657-661, 664-679, 683-693, 699, 703-710, 715, 723-742, 747, 755-767, 771-773, 778, 786-788, 794-798, 805-807, 811-817, 825, 835-843, 856-860, 868-904, 909-912 /usr/local/lib/python3.8/dist-packages/ipython_genutils/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/ipython_genutils/_version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipython_genutils/encoding.py 23 7 70% 30, 53-58, 64-68 /usr/local/lib/python3.8/dist-packages/ipython_genutils/importstring.py 12 10 17% 27-39 /usr/local/lib/python3.8/dist-packages/ipython_genutils/path.py 70 55 21% 55-71, 90-95, 100-101, 110-117, 130-154, 165-172 /usr/local/lib/python3.8/dist-packages/ipython_genutils/py3compat.py 196 141 28% 16-17, 20-21, 25-27, 30-32, 36-40, 45-57, 64-79, 96-143, 158-167, 175, 183-185, 195-198, 203-204, 212, 220, 224-293, 298-307 /usr/local/lib/python3.8/dist-packages/ipython_genutils/text.py 68 51 25% 19, 49-60, 74-87, 101-113, 125, 134-135, 140-146, 155-158, 215-217, 238-243 /usr/local/lib/python3.8/dist-packages/ipywidgets/__init__.py 21 7 67% 31-33, 38-40, 50 /usr/local/lib/python3.8/dist-packages/ipywidgets/_version.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/docutils.py 6 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/domwidget.py 27 14 48% 26-28, 36-38, 41-50 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/interaction.py 289 226 22% 11-12, 17-18, 33-34, 51-62, 73-85, 90-93, 99-128, 132-152, 177-232, 245-268, 272, 278-290, 295-307, 312-343, 348-359, 364-380, 388-393, 444, 507-538, 555-556, 570, 576 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/trait_types.py 78 33 58% 45-52, 84-87, 100-103, 125-128, 137-140, 162-165, 168, 195-206, 220 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/util.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/valuewidget.py 12 5 58% 20, 24-27 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget.py 403 283 30% 13-15, 32-39, 42-49, 59, 65-71, 80-116, 129-131, 141-162, 173-174, 183-195, 208-211, 216-223, 243-256, 259-265, 281-283, 302-303, 312, 317-318, 323-339, 349-354, 357-369, 372, 398, 411-415, 419, 427-438, 443-448, 455, 467-471, 481-489, 506-523, 526, 529-533, 540-545, 557, 571, 586, 590-594, 600-606, 609, 624-628, 633-642, 646-662, 668-689, 693, 697, 702, 707, 712-732, 736-737, 740-755, 758-763 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_bool.py 31 3 90% 22-24 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_box.py 37 5 86% 63-65, 68-69 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_button.py 41 12 71% 61-63, 68-73, 86, 94, 104-105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_color.py 14 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_controller.py 29 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_core.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_date.py 13 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_description.py 23 6 74% 28-34 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_float.py 168 62 63% 24-26, 36-39, 44-49, 54-59, 70-73, 78-83, 88-93, 262, 266, 270, 274, 278-281, 290-294, 298-308, 312-315 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_int.py 185 67 64% 42-44, 53-61, 73-75, 85-93, 98-101, 106-111, 116-121, 202, 206, 210, 214, 218-221, 243-247, 251-261, 265-268 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_layout.py 61 7 89% 82-85, 93-96 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_link.py 36 15 58% 24-34, 50-52, 56, 75, 105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_media.py 102 46 55% 44-51, 71-75, 86-88, 92-96, 101-111, 116-133, 159, 163, 166, 194, 197, 223, 226 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_output.py 64 33 48% 76-77, 97-105, 109-113, 117-127, 131-132, 136, 142, 146, 157-159 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selection.py 295 148 50% 11-12, 119-132, 136-139, 175-191, 196-200, 205-218, 222-225, 230-235, 239-243, 247-254, 258-260, 264-271, 274-280, 316-327, 331-335, 340-344, 349-352, 357-363, 368-371, 375-377, 381-383, 387-389, 392-395, 529-531, 535-540, 544-547, 553-555, 559-565, 615-619, 624-629 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selectioncontainer.py 36 14 61% 27-30, 44-46, 57-61, 65-68 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_string.py 68 15 78% 30-32, 79-81, 91-92, 106-108, 120-123 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_style.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_templates.py 198 138 30% 80-85, 89-91, 95-96, 100-106, 157-158, 162-167, 172, 176-242, 247, 281-289, 293-295, 300-312, 315-330, 333-345, 349-355, 397-398, 403-450, 454 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_upload.py 39 11 72% 24-26, 58-64, 68 /usr/local/lib/python3.8/dist-packages/jedi/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jedi/_compatibility.py 339 252 26% 18-19, 34-45, 52-53, 56, 59, 63-88, 92-111, 115-150, 158-162, 169-205, 223-224, 230-233, 246-248, 266, 273-275, 285, 311-312, 319, 324-325, 329-330, 334-335, 339-340, 350-352, 360-370, 376, 387-396, 404-410, 414-426, 432-440, 445-451, 456-466, 483-530, 536-582, 591-624 /usr/local/lib/python3.8/dist-packages/jedi/api/__init__.py 341 241 29% 58-62, 127-203, 209-248, 251, 254, 275, 278-283, 286-291, 310-311, 314-319, 322-342, 345-350, 370-371, 375-408, 422, 425, 429-431, 454, 471-480, 483-488, 502-514, 517-522, 541-558, 569-600, 603-634, 648-649, 657, 661-671, 683, 686-687, 715, 719-727, 765, 769-777, 800-801, 830-845, 849-855, 863-869, 884-886, 898-901 /usr/local/lib/python3.8/dist-packages/jedi/api/classes.py 297 193 35% 37, 47-49, 53, 81-86, 93, 98-104, 115, 175-187, 202, 208-211, 216-219, 224-227, 260-270, 273, 276, 311-329, 355-368, 374-377, 384-387, 402-403, 406-411, 416-427, 447-448, 451-466, 472-489, 497-522, 525, 542-552, 555-561, 570, 582, 594, 604-613, 616-624, 647-649, 664, 670-675, 678-684, 687-693, 696-697, 709-716, 719, 728, 732-738, 747-748, 758-761, 764, 770, 773, 782-783, 793, 803, 812-814, 824, 836, 839, 853, 861, 870, 879-883 /usr/local/lib/python3.8/dist-packages/jedi/api/completion.py 348 296 15% 30, 35-40, 44-68, 72-73, 80-81, 85-89, 96, 102-114, 117-151, 174-282, 285-295, 298-317, 320-323, 326-337, 340-355, 358-360, 363-365, 371-389, 401-421, 424-431, 442-448, 455-499, 503-516, 539-577, 583-619 /usr/local/lib/python3.8/dist-packages/jedi/api/completion_cache.py 19 11 42% 5-9, 14-19 /usr/local/lib/python3.8/dist-packages/jedi/api/environment.py 219 166 24% 36-37, 41-45, 49, 65-67, 70-107, 110-111, 114, 129, 134-136, 145, 148, 157-169, 173-177, 190-198, 202-238, 242-252, 257-264, 284-321, 334-338, 351-363, 374-377, 385-393, 398-420, 424-425, 432-454, 458-473, 477-480 /usr/local/lib/python3.8/dist-packages/jedi/api/errors.py 19 7 63% 8, 16, 21, 26, 31, 36, 39 /usr/local/lib/python3.8/dist-packages/jedi/api/exceptions.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/api/file_name.py 115 103 10% 17-54, 64-81, 85-99, 103-156 /usr/local/lib/python3.8/dist-packages/jedi/api/helpers.py 319 272 15% 26, 30-35, 39-42, 47, 51-61, 66-71, 77, 83-117, 124-156, 163-179, 183-201, 206-209, 213, 217, 220-268, 272-336, 343-357, 361-371, 375-421, 427-439, 449-465, 474-493, 497-500 /usr/local/lib/python3.8/dist-packages/jedi/api/interpreter.py 23 12 48% 12, 19, 24-25, 28, 34-41 /usr/local/lib/python3.8/dist-packages/jedi/api/keywords.py 34 25 26% 8-15, 22, 30-57 /usr/local/lib/python3.8/dist-packages/jedi/api/project.py 214 164 23% 40-51, 56-61, 65, 78, 82, 92-98, 106-118, 139-151, 156-161, 169-205, 208-213, 236, 248, 251, 256-343, 346, 350-353, 358-362, 375-411, 415 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/__init__.py 133 110 17% 19-23, 26-36, 39, 42-48, 51, 56-58, 64-73, 89, 92-98, 104-108, 112-118, 122-138, 142-218, 225 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/extract.py 239 210 12% 20-29, 36-41, 49-93, 100-126, 130, 134-138, 147-149, 153, 160-164, 172-200, 204, 209-292, 296-306, 310-316, 320-337, 341-353, 357-363, 371-379, 383-386 /usr/local/lib/python3.8/dist-packages/jedi/api/strings.py 64 47 27% 27-50, 54-58, 68-77, 81-86, 90-93, 97-98, 102-109 /usr/local/lib/python3.8/dist-packages/jedi/cache.py 65 40 38% 32-43, 60-73, 84-93, 105-113 /usr/local/lib/python3.8/dist-packages/jedi/common/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jedi/common/utils.py 24 18 25% 6-13, 21-26, 31-36 /usr/local/lib/python3.8/dist-packages/jedi/common/value.py 54 31 43% 3-4, 7-11, 48, 55, 59-61, 68-74, 77, 80, 83-84, 87, 90, 93, 96, 99-104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/jedi/debug.py 80 50 38% 22, 36-56, 74-75, 81-82, 89-97, 103-109, 113-120, 124-127, 136-140 /usr/local/lib/python3.8/dist-packages/jedi/file_io.py 54 30 44% 8, 11, 14, 17, 20, 23, 28, 31, 34, 37, 40-57, 62, 68-69, 72-75 /usr/local/lib/python3.8/dist-packages/jedi/inference/__init__.py 107 76 29% 86-108, 111, 117-121, 126-130, 135-136, 139-140, 144, 147-179, 183-194, 197 /usr/local/lib/python3.8/dist-packages/jedi/inference/analysis.py 126 101 20% 32-37, 41, 45, 50-51, 54, 58, 61, 65, 68, 71, 81-91, 98-109, 116-127, 138-217 /usr/local/lib/python3.8/dist-packages/jedi/inference/arguments.py 228 156 32% 19-31, 53-68, 76-108, 135, 138, 148-172, 180-183, 188, 191-231, 234-239, 242-247, 250, 253-279, 284, 287-288, 291, 296, 300, 304, 308, 311, 314, 317, 321-333, 337-349 /usr/local/lib/python3.8/dist-packages/jedi/inference/base_value.py 285 179 37% 27-34, 39, 42, 45-47, 50, 53, 56, 62-70, 73-77, 84-98, 101-104, 107-120, 123-126, 130-132, 136, 152, 155-163, 166, 169-176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 210, 213-218, 221-223, 226-227, 230-231, 234-235, 241, 244-245, 249, 253, 256, 260, 263, 266, 274, 283-289, 294, 297-298, 305-306, 309, 312, 317, 320, 325-326, 329, 334-335, 338, 341, 344, 349-371, 376, 379-382, 387, 390, 393, 396, 399, 402-410, 413, 416, 419-436, 440-448, 456 /usr/local/lib/python3.8/dist-packages/jedi/inference/cache.py 72 44 39% 25-45, 80, 90-121 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/__init__.py 38 26 32% 8-16, 25-26, 29-32, 35-37, 40, 48-53, 57, 63-68 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/access.py 327 243 26% 82-98, 109-112, 117, 121-140, 145, 151, 154, 158-159, 167-175, 180-181, 184, 187, 190, 193, 196-199, 202, 205-219, 222, 225-227, 230-234, 237-250, 253, 256, 259-265, 270-285, 288, 291, 294, 297, 300, 303-313, 316, 319-323, 327-351, 354-396, 399-401, 404, 407-409, 412-421, 424-425, 428-456, 459-461, 467-482, 485, 488-490, 493, 507-516, 519-538, 541, 548-552, 557-564 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/getattr_static.py 97 81 16% 16-21, 25-31, 35-39, 43-54, 58-65, 73-127, 131, 135, 152-184 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/mixed.py 155 108 30% 47-49, 52, 58, 63-66, 69-72, 75-78, 81-83, 86, 96, 108-109, 113-117, 121-129, 134-135, 138, 146, 156-175, 179-249, 256-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/subprocess/__init__.py 236 176 25% 20-21, 37-38, 45-50, 54, 58-70, 75-77, 80-87, 90, 93, 103, 108-110, 113-129, 132-147, 150-151, 160-162, 165-166, 176-206, 211-219, 222, 225-226, 229-273, 283, 288-292, 295-308, 311-327, 330-357, 362-364, 367, 370-374, 377, 380, 383-388, 396-398, 402 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/subprocess/functions.py 66 45 32% 15, 19, 23-24, 28, 35-43, 47, 54, 61-66, 74-78, 82, 86, 91-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/value.py 394 269 32% 36-41, 46-47, 50-69, 73, 77, 84, 90, 93, 96, 99, 102, 105, 108, 111, 114, 118-133, 136-137, 140, 144-148, 152, 155, 159, 162-170, 173-178, 188-198, 201, 205-208, 211-228, 231-236, 239-245, 248-267, 270, 273, 276, 280, 283, 289-291, 298, 301-304, 307, 312-315, 318, 323-326, 329-330, 333-336, 339-343, 346-350, 354-358, 362, 365, 370-371, 375, 378-383, 386, 389-397, 402-404, 407, 410-413, 416, 421-423, 433-434, 437, 442-444, 447-448, 460-478, 482-485, 488-507, 510, 517, 537-587, 591-600, 606, 611-618, 624-629 /usr/local/lib/python3.8/dist-packages/jedi/inference/context.py 294 199 32% 20-21, 25, 28-34, 41-86, 89-106, 109-112, 115, 118, 121, 124, 127, 130, 133, 137, 140, 144, 147, 150, 154-159, 167-168, 172, 176, 179, 182, 185, 188, 191, 194, 197, 200, 204, 207, 210, 213, 216, 221-222, 225-248, 251-287, 290-297, 302, 312, 315-327, 330, 334, 338, 346, 351, 354, 358, 361, 366, 369, 378-380, 383, 386, 389, 392, 397, 404, 408, 411, 418-432, 483-500 /usr/local/lib/python3.8/dist-packages/jedi/inference/docstrings.py 144 116 19% 52-56, 61-75, 82-100, 108-133, 154-161, 179-183, 187-235, 244-245, 256-268, 273-290, 296-307 /usr/local/lib/python3.8/dist-packages/jedi/inference/filters.py 215 115 47% 26-28, 32, 36, 43, 46, 49, 52, 56-68, 75-78, 81, 86, 89, 98, 110-115, 118-120, 123-127, 130-141, 146-151, 154, 158-166, 171-172, 175, 180, 185-189, 193-195, 198, 206, 209-214, 217-223, 226, 229-230, 235, 238, 241, 244, 252-254, 258, 270-278, 281-291, 296-299, 307, 331-334, 340 /usr/local/lib/python3.8/dist-packages/jedi/inference/flow_analysis.py 84 65 23% 15-20, 23-26, 29, 39-42, 46-83, 87-110, 114-123 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/annotation.py 228 193 15% 34-45, 49-59, 63-76, 88-107, 112-132, 139-182, 186-195, 204-230, 247-272, 276-282, 295-304, 308-313, 351-370, 374, 378-381, 385, 389-408, 414-425, 429-433, 437-443 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/base.py 217 149 31% 18-20, 23-32, 35, 38, 53-54, 57-63, 68, 73-81, 86, 89, 93, 96-117, 122-137, 147, 162-163, 166, 169-178, 181, 184-185, 188, 192-193, 196, 199-201, 205-242, 247-248, 252-261, 264-279, 284-293, 296, 310-314, 318, 322-332, 338, 342, 345, 348, 353-355, 359, 362, 365, 370-373, 376, 379 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/conversion.py 138 122 12% 11-47, 51-59, 64-92, 96-98, 109-142, 146-153, 158-167, 175-208 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/generics.py 67 38 43% 15-23, 28-32, 35, 40-41, 45, 48, 53-63, 70-71, 74-78, 81, 86, 89, 92, 95, 98, 101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/stub_value.py 69 43 38% 13-14, 17, 25-34, 37, 43-50, 53, 60, 65-70, 73, 78-81, 88-101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/type_var.py 84 65 23% 9-18, 27-48, 53-71, 74, 77, 80-85, 89, 93, 98-105, 108, 111-114, 117 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typeshed.py 158 130 18% 23-26, 33-53, 57-69, 81-89, 95-124, 130-143, 154-229, 233-249, 258-264, 271, 281-295 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typing.py 236 154 35% 40, 43-96, 105-130, 137, 140, 151, 159, 174, 179, 183, 189-228, 237-240, 244, 247, 250, 253-266, 269, 278-286, 293, 296-303, 306-310, 313-316, 321-323, 327-355, 368-369, 376, 381-386, 397-399, 402, 406-407, 413, 426-429, 433, 436-442, 445-449, 455-457 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/utils.py 17 14 18% 11-30 /usr/local/lib/python3.8/dist-packages/jedi/inference/helpers.py 122 97 20% 17-21, 29-43, 65-109, 113-121, 125-129, 133, 137-139, 143, 147, 151, 160-163, 167-192, 196, 200-207 /usr/local/lib/python3.8/dist-packages/jedi/inference/imports.py 284 237 17% 39, 42-43, 46, 53-70, 75-96, 100-118, 122-125, 134-152, 169-224, 229, 235-238, 247-260, 269-273, 284-326, 331-356, 365-427, 432-440, 450-464, 473-510, 514-519, 523-548, 558-563 /usr/local/lib/python3.8/dist-packages/jedi/inference/lazy_value.py 37 18 51% 7-9, 12, 15, 21, 27, 32, 35, 40-44, 47-48, 52-55, 61 /usr/local/lib/python3.8/dist-packages/jedi/inference/names.py 450 314 30% 16-23, 38, 44, 47-54, 58, 61, 64, 67-69, 73, 76, 80, 87, 99-101, 104, 109-110, 113-126, 129-132, 135-139, 142-211, 214-215, 219, 223, 228, 231-237, 240, 243-245, 248-251, 255, 260-261, 264, 278-279, 287-290, 307-331, 334-346, 351-354, 357-360, 363-368, 371, 378, 381, 392, 396-401, 409-416, 419-424, 427, 432-434, 437, 441, 444-450, 453-456, 460, 463-488, 491-496, 502, 506-525, 530-531, 534-538, 541-543, 548, 551, 554, 562-563, 566-574, 578-584, 588-590, 593, 597, 600, 609, 612, 615, 620-634, 640-644, 651-652, 656 /usr/local/lib/python3.8/dist-packages/jedi/inference/param.py 130 113 13% 14-18, 23-26, 29, 32-47, 50, 73-225, 246, 250-257 /usr/local/lib/python3.8/dist-packages/jedi/inference/parser_cache.py 4 1 75% 6 /usr/local/lib/python3.8/dist-packages/jedi/inference/recursion.py 67 47 30% 55, 64-75, 81-90, 100-105, 108-109, 112-153 /usr/local/lib/python3.8/dist-packages/jedi/inference/references.py 182 157 14% 29-42, 46, 53-69, 73-77, 81-96, 100-113, 117-160, 164-175, 179-193, 197-218, 222-224, 228-245, 257-270, 274-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/signature.py 108 72 33% 9-34, 39-40, 44, 48, 51-54, 57, 60, 63-65, 70-71, 74, 80-82, 86-89, 93-97, 100-117, 122-124, 128, 132-134, 137, 146, 149 /usr/local/lib/python3.8/dist-packages/jedi/inference/syntax_tree.py 545 485 11% 45-63, 69-137, 144-150, 155, 161-237, 241-261, 270-352, 357-366, 385-444, 448-469, 477-487, 493-502, 506-518, 526-538, 542, 546, 550, 554-567, 571-642, 647-730, 741-782, 789-809, 814, 821-846 /usr/local/lib/python3.8/dist-packages/jedi/inference/sys_path.py 148 125 16% 18-29, 43-72, 79-97, 105-135, 139-147, 151-170, 174-177, 189-207, 211-215, 230-271 /usr/local/lib/python3.8/dist-packages/jedi/inference/utils.py 55 23 58% 13, 20, 26, 32, 74-78, 84-86, 89, 92, 96, 99-103, 112-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/value/decorator.py 7 3 57% 11-12, 15 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/dynamic_arrays.py 114 87 24% 36-40, 52-123, 128-130, 144-145, 148-149, 152-165, 168, 173-175, 178, 181-187, 192-194, 197, 202-204 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/function.py 305 223 27% 32-33, 37, 40, 45-54, 61-64, 67-71, 74, 79-81, 84, 87, 90-114, 117-118, 121-123, 126, 132-161, 164-165, 168, 171, 176-177, 180, 185-186, 189, 194-197, 201, 206, 211-248, 251-263, 268-312, 315, 321, 327-360, 365-366, 369, 377-378, 381, 391, 394, 401, 406-407, 410-421, 424, 427, 431-470 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/instance.py 353 227 36% 28-30, 33, 36, 41-42, 45-58, 63-64, 67, 74-81, 86-87, 94-97, 100, 103, 106, 109, 113, 117, 120-121, 125, 128, 134, 137, 144-146, 149-155, 159, 162, 168-172, 176, 179-199, 203-215, 223-239, 242-250, 253-275, 278-283, 291-297, 303-307, 314-322, 328-349, 352, 355-370, 373-393, 396, 406-414, 418-422, 427-428, 431, 434, 437-438, 446-448, 451, 455, 461-462, 465-466, 469-473, 476-480, 483, 489, 492, 497, 500, 508-510, 514, 517, 522-523, 527-529, 532, 535, 545-546, 549, 552, 555, 561, 569-575, 578-580, 583-592, 595-597, 605, 608, 613-614, 617-619 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/iterable.py 404 270 33% 27, 37-40, 49-52, 55, 58, 62, 68, 71, 75, 81-82, 85, 88, 91, 95-122, 133, 136-163, 168-170, 173-174, 177, 182, 190, 193, 196-203, 206, 210, 213-215, 220-224, 231-237, 251, 255, 262-267, 270-271, 274-280, 283, 286, 290-291, 295-306, 311, 321-329, 332-334, 338-343, 350-358, 362, 365-407, 414-417, 420, 427-429, 433-439, 447-453, 457-458, 462-470, 473, 479, 490-491, 494-499, 502, 505, 508, 523-524, 527-528, 531-548, 552, 558, 561, 564, 567, 572-574, 577-579, 582, 589-620, 625-630, 633-635, 642-658 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/klass.py 204 151 26% 59-61, 66-75, 81-87, 90, 100-105, 112-126, 130-131, 136, 139-144, 147, 151, 154, 158-187, 190-221, 227-229, 232, 235-237, 243-267, 275-289, 292-296, 300-309, 314-317, 329-330, 336-356, 360-361, 365-379 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/module.py 129 85 34% 22-24, 27-35, 45-56, 63-73, 76-77, 80, 83, 88, 92-98, 101-104, 111-128, 136, 144-156, 159-164, 167-169, 175-178, 181, 184-186, 194-219, 222, 225 /usr/local/lib/python3.8/dist-packages/jedi/parser_utils.py 194 159 18% 26-56, 60-69, 79, 83-94, 99-109, 113-124, 128-142, 158-175, 182-188, 196-219, 223-228, 235-244, 252-269, 280, 287-292, 299-311, 322-326 /usr/local/lib/python3.8/dist-packages/jedi/plugins/__init__.py 31 1 97% 21 /usr/local/lib/python3.8/dist-packages/jedi/plugins/flask.py 11 8 27% 7-20 /usr/local/lib/python3.8/dist-packages/jedi/plugins/pytest.py 99 71 28% 21-25, 31-41, 44-61, 67-77, 83-90, 95-98, 108-112, 117, 123-142, 147-153, 156-164 /usr/local/lib/python3.8/dist-packages/jedi/plugins/registry.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/plugins/stdlib.py 440 292 34% 106-132, 138-143, 157-173, 181-188, 194, 200-208, 213-217, 223-224, 227, 230-235, 238-241, 246-253, 258-259, 263, 268, 278-289, 294-328, 336, 341, 346-347, 350, 358-360, 363, 366, 371-372, 375-377, 382, 391-392, 395-397, 403, 408, 423-470, 475-478, 481-485, 488-502, 505-509, 514, 519, 524-526, 529-530, 535-537, 540-549, 553, 560, 568, 573, 581-586, 591-612, 617-618, 621, 626-628, 631, 634-637, 642-643, 647-661, 666, 675, 680-681, 685, 688, 693, 702-711, 717-734, 801-808, 814-817, 821, 824-825, 828-833, 838-842 /usr/local/lib/python3.8/dist-packages/jedi/settings.py 19 2 89% 72, 75 /usr/local/lib/python3.8/dist-packages/joblib/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/joblib/_compat.py 15 3 80% 11, 24-25 /usr/local/lib/python3.8/dist-packages/joblib/_memmapping_reducer.py 180 132 27% 28, 38-39, 70, 73-78, 81-95, 98, 107-119, 152-178, 183, 189-202, 213-236, 243-252, 281-286, 293-298, 301-361, 374-434 /usr/local/lib/python3.8/dist-packages/joblib/_memory_helpers.py 65 63 3% 5-105 /usr/local/lib/python3.8/dist-packages/joblib/_multiprocessing_helpers.py 34 11 68% 20-21, 34-37, 51-53, 61-64 /usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py 271 174 36% 38-39, 78-79, 92, 99, 123, 132-136, 153, 163-184, 188, 203-205, 209-212, 216-220, 230-238, 242-245, 249, 253, 258-260, 282-284, 288-344, 348-360, 367-368, 392-399, 407-409, 432-462, 467-489, 493-497, 509-519, 523-547, 551-555, 561-564, 567-574, 579-583, 590, 593, 604, 607-624, 631 /usr/local/lib/python3.8/dist-packages/joblib/_store_backends.py 196 137 30% 26-31, 152-174, 179-193, 198-200, 205-208, 212, 217-223, 227-238, 242-243, 247-249, 253-260, 264-270, 274, 278, 282-294, 298-322, 326-328, 332, 345-348, 352, 356-388, 395-415 /usr/local/lib/python3.8/dist-packages/joblib/backports.py 48 37 23% 22-30, 37-76, 80-81 /usr/local/lib/python3.8/dist-packages/joblib/compressor.py 315 209 34% 12-13, 17-18, 22-23, 27-28, 61, 65, 73, 78, 107-110, 115, 127, 130-131, 136-140, 145-152, 164, 168-172, 178-186, 202, 206-209, 220, 225-235, 239-243, 248-249, 289-321, 330-348, 353, 357-358, 362, 366-367, 371-372, 377-383, 386-388, 391-393, 396-401, 406-424, 430-440, 446-470, 478-485, 492-493, 502-511, 515-520, 537-562, 566-568 /usr/local/lib/python3.8/dist-packages/joblib/disk.py 59 42 29% 27-38, 44-52, 59-63, 90-101, 106-124 /usr/local/lib/python3.8/dist-packages/joblib/executor.py 32 21 34% 28-50, 58-59, 63-65, 68-69, 72-73 /usr/local/lib/python3.8/dist-packages/joblib/externals/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle.py 623 480 23% 65-66, 85, 88-95, 109-115, 119-124, 128-139, 151-164, 169-202, 209-227, 257-274, 334-339, 343-384, 388, 406, 410-432, 440-443, 448-465, 473-477, 480-488, 491, 496-499, 505-509, 517-541, 551-556, 579-582, 591-608, 617-692, 706-760, 767-812, 820-847, 850-851, 854, 865-879, 886-892, 900-941, 944, 948, 953-954, 961-967, 974-989, 996-1033, 1036, 1039, 1050, 1055, 1060, 1066, 1073, 1083-1089, 1093-1094, 1109, 1122-1128, 1138-1139, 1143-1145, 1149, 1153, 1157-1161, 1186, 1194-1251, 1260, 1271-1281, 1296-1297, 1305-1315, 1335-1348, 1357-1397 /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle_fast.py 227 169 26% 47, 60-63, 70-78, 83-84, 91, 103-132, 136-158, 162-174, 190-198, 203-208, 212-213, 218-260, 264, 268, 272, 276-279, 283, 287, 291, 295, 306-312, 320-330, 346-370, 374-385, 419-431, 463-475, 481-483, 498-501, 504-534, 537-547 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/_base.py 287 271 6% 34-615, 623-627 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/_posix_reduction.py 41 21 49% 20, 29-31, 36-43, 51-52, 55-58, 67-68, 71-74 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat.py 18 8 56% 14, 19, 23, 29-38 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat_posix.py 4 1 75% 11 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/context.py 135 95 30% 37-85, 91-97, 101, 118-153, 164-165, 170-171, 175-206, 214-215, 219-220, 224-225, 229-230, 234-235, 239-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/process.py 57 42 26% 20-31, 35-39, 42-64, 67-81, 89, 100-108 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/queues.py 131 102 22% 36-62, 66-67, 72-75, 79-111, 121-175, 182-183, 187-189, 195-210, 214-215, 219, 225-229, 234-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/reduction.py 126 59 53% 27-30, 63-66, 79-82, 87, 91, 101, 109, 113, 121, 127-129, 146, 149, 154-165, 176-177, 185-197, 202-210, 218, 223, 232-234, 240, 246-250, 256 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/utils.py 94 75 20% 11-12, 20-21, 25-28, 32-46, 52-60, 67-116, 125-138, 143-145, 149-172 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/cloudpickle_wrapper.py 60 44 27% 7-8, 16-17, 20-24, 29-31, 38, 42-44, 48-49, 55-83, 95-113 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py 507 411 19% 88-89, 94, 132-138, 143, 146-147, 150-155, 158-159, 171-174, 177-179, 182-184, 189-197, 214, 217, 223-229, 232, 236-237, 245-248, 254-256, 262-268, 271-272, 275, 283-286, 289-312, 317-325, 337, 342-347, 372-465, 487-505, 542-757, 766-785, 794-797, 803-810, 882-933, 939-951, 955-994, 998-1014, 1019-1022, 1025-1048, 1073-1081, 1084-1117 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/reusable_executor.py 91 70 23% 34-37, 84-142, 150-155, 158-159, 163-194, 199-207, 212-213 /usr/local/lib/python3.8/dist-packages/joblib/format_stack.py 209 188 10% 34-35, 45-68, 72, 88-94, 104-116, 120-147, 151-176, 181-322, 337-365, 371-401 /usr/local/lib/python3.8/dist-packages/joblib/func_inspect.py 176 154 12% 47-79, 84-93, 110-162, 173-177, 190-193, 198-203, 228-318, 322-325, 330-349, 356-359 /usr/local/lib/python3.8/dist-packages/joblib/hashing.py 117 85 27% 24, 33-42, 49, 58-64, 67-75, 78-94, 101-103, 111-127, 141-150, 155, 174-182, 189-242, 258-267 /usr/local/lib/python3.8/dist-packages/joblib/logger.py 76 55 28% 28-31, 35-36, 40-44, 48-57, 77, 81, 85, 96-124, 136-156 /usr/local/lib/python3.8/dist-packages/joblib/memory.py 374 270 28% 57-63, 92-100, 106, 109, 112, 114-133, 135, 146-148, 153-160, 166-182, 225-244, 248-253, 257-277, 281, 284, 293-295, 306-307, 310-313, 316-317, 320-325, 329, 332-333, 358, 361, 365, 415-453, 483-545, 562-563, 568, 574-576, 583, 588-590, 594-595, 605-625, 636-713, 717-724, 730-745, 764-795, 804, 884, 887-901, 905, 914-921, 952, 956-962, 971-974, 978-979, 990-992, 999, 1008-1010 /usr/local/lib/python3.8/dist-packages/joblib/my_exceptions.py 53 20 62% 24, 27-33, 46-48, 51-59, 75, 80, 84, 89, 94-99, 112-113 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle.py 204 161 21% 13-14, 78-82, 91-104, 112-161, 165-178, 195-209, 234-249, 253-260, 272-295, 320-332, 342-355, 361, 415-515, 526-548, 588-607 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_compat.py 105 75 29% 21-25, 38-61, 71-75, 90-92, 96-120, 140-142, 148-154, 164-173, 176, 185-192, 198, 227-247 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_utils.py 92 65 29% 22-23, 27-28, 36-37, 45-49, 54-56, 73-90, 95-100, 105-112, 144-182, 187-197, 229-245 /usr/local/lib/python3.8/dist-packages/joblib/parallel.py 362 293 19% 40-41, 65-73, 83-124, 181-209, 212, 215, 218-222, 234, 241-249, 254-255, 259, 267-270, 282-291, 297-311, 327-329, 332-340, 360-363, 388-389, 620-696, 699-701, 704-705, 709-725, 728-730, 733-734, 744-759, 769-771, 783-836, 842-849, 855-887, 895-940, 943-1032, 1035 /usr/local/lib/python3.8/dist-packages/joblib/pool.py 116 83 28% 42-43, 75-87, 91-99, 120-127, 130-131, 135-137, 140, 143-177, 199-207, 210-216, 296-313, 316-329 /usr/local/lib/python3.8/dist-packages/jupyter_client/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/adapter.py 256 210 18% 17-25, 38-52, 64, 67, 70-74, 81, 84-96, 103-109, 125-127, 132-146, 149-151, 154-157, 160-170, 173-179, 182-190, 194-195, 200-202, 205-214, 219-220, 231-234, 239-258, 261-266, 269-282, 285-290, 297-307, 310-317, 321-339, 344-346, 349-358, 363-364, 386-398 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/channels.py 45 28 38% 29-32, 35-37, 41-48, 52-58, 62, 65-70, 74, 79, 82 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/client.py 188 138 27% 23-29, 34, 69, 77, 81, 85, 89, 94-101, 112-150, 162-177, 194-212, 216-224, 231-235, 239-253, 311-388 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/channels.py 50 32 36% 11-12, 35-38, 41-43, 47-57, 61-67, 71, 74-79, 83, 88, 91 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/client.py 161 121 25% 25-31, 36, 77-115, 127-142, 159-179, 183-191, 198-202, 262-339 /usr/local/lib/python3.8/dist-packages/jupyter_client/channels.py 119 82 31% 61-80, 87-88, 91-99, 109-132, 136-170, 174, 178, 182-185, 189-192, 195-200, 210 /usr/local/lib/python3.8/dist-packages/jupyter_client/channelsabc.py 25 7 72% 16, 20, 24, 37, 41, 45, 49 /usr/local/lib/python3.8/dist-packages/jupyter_client/client.py 172 120 30% 26-30, 53, 78, 82, 86, 90, 105-118, 125-134, 139, 148-155, 160-167, 172-179, 184-190, 195-202, 206-218, 258-277, 295-300, 322-329, 363-370, 379-381, 390-396, 404-406, 410-412, 420-422, 441-443 /usr/local/lib/python3.8/dist-packages/jupyter_client/clientabc.py 45 14 69% 32, 36, 40, 44, 48, 52, 60, 64, 68, 72, 76, 80, 84, 88 /usr/local/lib/python3.8/dist-packages/jupyter_client/connect.py 247 186 25% 79-167, 191-224, 253-274, 296, 319-325, 329-330, 350, 355-356, 377-396, 402-406, 413-419, 423-430, 438-446, 455-461, 465-481, 492-497, 511-523, 531-538, 542-551, 555-557, 561, 565, 569, 573 /usr/local/lib/python3.8/dist-packages/jupyter_client/jsonutil.py 50 34 32% 38-44, 53-59, 63-72, 76-84, 88-92 /usr/local/lib/python3.8/dist-packages/jupyter_client/kernelspec.py 196 139 29% 44-47, 50-58, 65, 73, 82, 90-103, 108, 111, 130, 134, 146-159, 163-183, 190-200, 204-222, 229-237, 252-270, 277-289, 292-297, 315-347, 351-354, 359, 366, 370, 376 /usr/local/lib/python3.8/dist-packages/jupyter_client/launcher.py 59 51 14% 56-158 /usr/local/lib/python3.8/dist-packages/jupyter_client/localinterfaces.py 170 129 24% 29-30, 34-42, 48-52, 58-59, 68-89, 96-108, 113-121, 127-135, 140-164, 173-196, 201-203, 216-248, 254, 259, 264, 269, 274 /usr/local/lib/python3.8/dist-packages/jupyter_client/manager.py 349 261 25% 42, 48, 52, 61, 64, 75-77, 83-85, 102, 109, 113, 126-127, 134, 137, 141-143, 147-149, 157-166, 174-205, 212, 217-219, 222-225, 239-267, 274-283, 286-287, 301-306, 311-315, 323-338, 342-347, 368-379, 406-418, 423, 430-456, 464-478, 488-498, 502-509, 523-524, 538-543, 551-562, 583-594, 621-634, 641-675, 683-697, 707-717, 721-728, 735-736, 744-755, 760-771, 784-789 /usr/local/lib/python3.8/dist-packages/jupyter_client/managerabc.py 27 8 70% 21, 29, 33, 37, 41, 45, 49, 53 /usr/local/lib/python3.8/dist-packages/jupyter_client/multikernelmanager.py 185 104 44% 31-39, 60-63, 67, 73, 76-90, 94-105, 109, 119, 123, 126, 130-147, 157-160, 174-188, 198, 212, 216-222, 233, 247, 258, 275-276, 286-287, 402, 422-428, 442-456, 461-463, 473-475, 488-490, 500-502, 506-512 /usr/local/lib/python3.8/dist-packages/jupyter_client/session.py 442 311 30% 27, 65-75, 148-153, 157, 172, 177, 184, 187-191, 201-205, 209, 212, 215, 218, 221, 226-228, 232-247, 305-315, 323-333, 338-340, 344, 364, 368, 376-384, 388, 393-396, 411-412, 423-425, 432-434, 488-496, 508-514, 519-521, 525-568, 571, 580-590, 600-605, 630-664, 711-766, 784-794, 810-828, 853-865, 869-876, 883-889, 916-954, 957-961, 965-978 /usr/local/lib/python3.8/dist-packages/jupyter_core/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_core/paths.py 194 155 20% 33-38, 47-51, 59-68, 78-98, 109-114, 118-122, 147-166, 170-174, 186-202, 209-213, 232-247, 266-287, 291, 316-344, 361-377, 394, 415-448, 452-456 /usr/local/lib/python3.8/dist-packages/jupyter_core/version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/__init__.py 18 5 72% 22, 37-40 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/affine_transformations.py 107 77 28% 16-17, 22-24, 28-31, 55-59, 84-90, 114-118, 145-156, 171-180, 194-195, 212-219, 236-242, 246-251, 281, 285-289, 292-298, 301-308, 311-317, 320-335 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py 120 97 19% 93-99, 124-173, 180-223, 227-233, 237-261, 273-284, 288, 292-295, 299 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/directory_iterator.py 65 4 94% 72-73, 106, 162 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/image_data_generator.py 213 111 48% 17-18, 301, 308-310, 316, 327-331, 335-342, 347-349, 354-356, 361-363, 421, 649-666, 708, 712, 714, 717-720, 725-728, 734-739, 761, 764, 771-778, 783-790, 795, 804, 814, 819, 877, 882, 885, 888, 902-903, 933-988 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/iterator.py 146 51 65% 50, 54, 59, 74, 78-95, 101, 104, 112-116, 127, 175, 180-183, 188-193, 201, 205-209, 243-250, 253, 255-257, 263-268, 272, 277, 285, 292 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/numpy_array_iterator.py 87 75 14% 46-52, 68-150, 156-183 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/utils.py 121 54 55% 16-18, 47, 71-76, 107-109, 111, 118-119, 121-122, 125-127, 132, 153-154, 179, 217, 251-286, 305, 312-319 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/sequence.py 155 135 13% 55-110, 143-149, 197-240, 254-259, 334-354, 360, 364-378, 386-402, 427-432, 446-454 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/text.py 198 166 16% 21, 42-63, 88, 128-138, 179-197, 212-251, 263-267, 281, 298-324, 338, 355-370, 382-383, 400-438, 449-455, 482-487, 500-519 /usr/local/lib/python3.8/dist-packages/parso/__init__.py 8 3 62% 56-58 /usr/local/lib/python3.8/dist-packages/parso/_compatibility.py 36 15 58% 27-29, 38-43, 49-51, 60-64, 69 /usr/local/lib/python3.8/dist-packages/parso/cache.py 103 67 35% 66, 68, 89-94, 101-111, 120-144, 148-160, 164-174, 178-179, 183-186, 190-193, 197-202 /usr/local/lib/python3.8/dist-packages/parso/file_io.py 20 11 45% 6, 12-13, 19-23, 26, 31-32, 35 /usr/local/lib/python3.8/dist-packages/parso/grammar.py 127 89 30% 33-40, 77-79, 90-155, 158-161, 169-172, 175, 178-183, 190-191, 194-196, 199-201, 210-216, 219, 223, 234-260 /usr/local/lib/python3.8/dist-packages/parso/normalizer.py 137 74 46% 19-24, 27-33, 36-39, 42-48, 52-53, 56-57, 60-65, 68, 71, 74-77, 101, 117-120, 125-138, 141, 144, 147, 150, 158, 161, 164, 167-171, 174-181, 184-186, 191, 194-197, 200-203 /usr/local/lib/python3.8/dist-packages/parso/parser.py 106 79 25% 37-38, 49-54, 59-71, 76-77, 81, 84, 89-96, 119-121, 124-143, 146-151, 154-160, 163-166, 174-198, 201-211 /usr/local/lib/python3.8/dist-packages/parso/pgen2/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/parso/pgen2/generator.py 178 152 15% 45-47, 56-57, 60, 74-87, 90-93, 96-98, 102-112, 117, 130, 133, 145-157, 170-207, 211-224, 228-232, 244-274, 282-294, 303-344, 352-377 /usr/local/lib/python3.8/dist-packages/parso/pgen2/grammar_parser.py 101 81 20% 18-23, 27-38, 42-58, 62-69, 73-94, 98-111, 115-122, 125-126, 129-135, 141-142, 145, 150-151, 154-156, 159 /usr/local/lib/python3.8/dist-packages/parso/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/parso/python/diff.py 502 437 13% 52, 56-58, 62-64, 68, 72, 81-118, 122-138, 142-146, 154-167, 171-173, 177-184, 192-195, 199-203, 207-220, 224-230, 238-247, 256-258, 261-264, 282-340, 343-344, 347-388, 391-405, 412-431, 439-453, 456-514, 523-527, 530-550, 553, 557-562, 565-586, 589, 594-599, 603, 606-609, 612-624, 627-640, 646-665, 672-685, 693-719, 723-861, 864-886 /usr/local/lib/python3.8/dist-packages/parso/python/errors.py 758 567 25% 26-33, 37-48, 56-57, 65-72, 76, 83-93, 97-121, 125-131, 135-136, 141-148, 153, 157, 160-170, 176-202, 205-247, 251-253, 256, 259, 267-269, 272-282, 285-291, 295-315, 318-349, 352, 355, 360-362, 365-368, 375-376, 384-385, 389, 400-401, 409, 413, 421, 425, 433-437, 446-456, 464, 467, 477-483, 491-522, 530-535, 546-548, 558, 561-570, 579-581, 584-590, 599-600, 608, 616-633, 643-661, 667-693, 703, 706-730, 736-750, 758, 765-768, 771-823, 833-845, 853-860, 871, 874-889, 892, 895-897, 902-983, 991-995, 1004-1009, 1015, 1021-1024, 1030-1031, 1038-1040, 1049-1108 /usr/local/lib/python3.8/dist-packages/parso/python/parser.py 102 85 17% 66-71, 74-80, 90-108, 112-118, 121-188, 191-200, 203-217 /usr/local/lib/python3.8/dist-packages/parso/python/pep8.py 492 426 13% 39-40, 43, 46-51, 56-105, 114-119, 126-144, 148, 153-172, 176-178, 182-280, 283-286, 289-290, 293-341, 344-375, 378-519, 522-537, 545-629, 632-685, 688-700, 727 /usr/local/lib/python3.8/dist-packages/parso/python/prefix.py 56 35 38% 11-16, 20-25, 28-29, 35, 69-94 /usr/local/lib/python3.8/dist-packages/parso/python/token.py 12 1 92% 10 /usr/local/lib/python3.8/dist-packages/parso/python/tokenize.py 458 395 14% 60-61, 70, 75, 80-111, 119-124, 136-265, 274-278, 283, 289-295, 298, 301-304, 307, 310, 313, 317-331, 335-371, 376-377, 384-389, 401-672, 676-705, 709-722 /usr/local/lib/python3.8/dist-packages/parso/python/tree.py 642 403 37% 48-49, 79-97, 111-119, 126, 134-143, 154, 180, 192, 204, 211, 221-249, 267, 270-275, 311-314, 318, 321, 343, 349, 355, 361, 364-372, 378, 381-386, 400-401, 412-417, 426-429, 436-453, 469, 475-485, 496, 503-509, 523-566, 586-588, 591, 597, 601, 607-624, 630-639, 645-654, 660, 667-673, 692-694, 701, 704, 711, 714, 734-736, 744-752, 758-763, 779, 782, 794-798, 810-815, 818-821, 833-842, 845, 848, 861, 865, 869-877, 882-888, 891-905, 914-918, 932, 937, 940, 944-960, 969, 976, 996, 1000, 1003-1008, 1016, 1023, 1040-1061, 1072-1075, 1084-1090, 1096-1105, 1117-1120, 1128-1131, 1139-1144, 1152-1159, 1165-1166, 1173-1176, 1179, 1186-1201, 1207, 1216-1222, 1228-1229, 1241, 1254, 1257, 1260, 1263, 1266, 1270 /usr/local/lib/python3.8/dist-packages/parso/tree.py 195 124 36% 18-20, 38-41, 48-58, 66-75, 82-101, 108-127, 186-197, 203, 207-208, 211-216, 219, 222, 225-228, 232-239, 243-246, 253-254, 266-271, 278, 281, 285, 288-292, 295, 306-328, 331, 334, 338-341, 350-351, 354, 376-377, 380 /usr/local/lib/python3.8/dist-packages/parso/utils.py 86 68 21% 38-68, 80-109, 117-119, 123-140, 146-152, 155-159, 162, 171-176 /usr/local/lib/python3.8/dist-packages/pickleshare.py 194 152 22% 43-45, 50-51, 62, 65, 73-86, 91-107, 111-123, 127-133, 139-154, 158-177, 186-198, 204-211, 215, 220-224, 227, 230, 240-243, 259-275, 279, 282, 297, 300, 302, 304-306, 311-347, 350 /usr/local/lib/python3.8/dist-packages/pooch/__init__.py 20 14 30% 36-50 /usr/local/lib/python3.8/dist-packages/pooch/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/pooch/core.py 120 84 30% 197-230, 410, 413, 424, 545-568, 575-576, 589-590, 610-637, 658-673, 702-711, 725-733 /usr/local/lib/python3.8/dist-packages/pooch/downloaders.py 82 69 16% 14-15, 47-60, 139-143, 161-203, 253-261, 277-310 /usr/local/lib/python3.8/dist-packages/pooch/processors.py 75 50 33% 38, 64-81, 88, 117-133, 162-182, 213, 240-252, 262-279 /usr/local/lib/python3.8/dist-packages/pooch/utils.py 101 54 47% 33, 96, 150, 173-193, 214-216, 246, 248, 255-256, 261-271, 308-315, 346-362, 386-393, 430-436 /usr/local/lib/python3.8/dist-packages/pooch/version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/application.py 442 346 22% 93-94, 220-336, 343-353, 366-374, 385, 395-399, 410-431, 437-477, 482, 492-523, 530-537, 546-559, 569, 579-581, 585-591, 614-778, 801-811, 826-839, 849-851, 862-869, 875-884, 916-929, 938-939, 959-981, 993-1006, 1019, 1030, 1034-1036, 1043-1053, 1064-1065, 1073, 1078, 1087-1132, 1136-1140, 1145, 1148, 1158-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/current.py 64 39 39% 7-8, 11-13, 50, 54-58, 62-66, 75, 97-103, 111-112, 127-134, 148-165 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/dummy.py 17 5 71% 21, 28, 35, 44, 47 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/run_in_terminal.py 44 33 25% 13-14, 50-57, 73-116 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/auto_suggest.py 57 28 51% 23, 44, 47, 82, 93, 98, 107-110, 121, 132-145, 155-156, 161-164, 175, 180-181, 186-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/buffer.py 775 644 17% 82-90, 93, 106-108, 114-128, 136-138, 153-155, 158, 251-316, 319-324, 332-383, 389-405, 409-412, 416, 426-442, 446, 453-464, 468, 472-478, 482-498, 504-516, 524, 536, 557-571, 578, 589-596, 619-628, 636-639, 656-658, 667, 670, 674-680, 684-690, 699-708, 717-726, 733-748, 754-762, 769-774, 784-799, 808-814, 820-822, 831-845, 856-867, 873-875, 883-890, 897-926, 932-943, 950-956, 963-967, 974, 984-1000, 1006-1021, 1035-1074, 1081, 1089, 1103-1108, 1114, 1125-1135, 1141-1144, 1150-1157, 1163-1169, 1185-1221, 1228-1237, 1240-1246, 1256-1277, 1295-1319, 1327-1330, 1342-1418, 1428-1441, 1456-1464, 1476-1483, 1486, 1493-1502, 1506-1531, 1539-1578, 1588-1614, 1628-1630, 1648-1768, 1776-1795, 1803-1807, 1813-1825, 1839-1862, 1873-1883, 1892-1909, 1920-1956 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/cache.py 56 25 55% 37-51, 55-56, 93-101, 117-121 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/base.py 38 13 66% 29-30, 52, 72, 75, 78, 81, 92, 95, 98, 101, 104, 107 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/in_memory.py 22 13 41% 23-29, 32-35, 38-41, 44-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/base.py 116 71 39% 48-62, 65-72, 80-82, 90, 95-97, 102-104, 109-111, 120-122, 147-153, 156, 185, 196-197, 212, 217, 225-228, 231, 242, 245, 256, 261-262, 267-272, 275, 284, 290-292, 299-301, 308, 318-333, 338-349 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/filesystem.py 46 36 22% 34-38, 43-98, 107 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/fuzzy_completer.py 70 52 26% 54-60, 65-68, 71-75, 81-116, 131-159, 180-188, 193 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/nested.py 40 28 30% 32-33, 36, 63-75, 81-109 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/word_completer.py 33 26 21% 41-49, 55-83 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/data_structures.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/document.py 525 407 22% 52, 68-71, 98-121, 130, 133-136, 145, 150, 155, 160, 165, 169, 173, 178-179, 184-185, 193-196, 205-224, 231, 237, 243, 248-250, 256-259, 266, 273, 280-281, 291-292, 301-304, 312-315, 324-340, 345, 350, 356, 372-397, 404-405, 420-434, 446-453, 458-461, 475-496, 510-536, 546-547, 556-572, 581-604, 613-625, 634-652, 661-671, 680-690, 696-699, 705-708, 720-725, 742-747, 761-783, 794-816, 828-834, 838, 842, 846-854, 858, 864, 872-876, 890-897, 908-947, 958-997, 1005-1033, 1048-1098, 1104-1111, 1118-1129, 1136-1145, 1154, 1165-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/enums.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/async_generator.py 26 18 31% 32-67 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/inputhook.py 65 42 35% 50-52, 61-63, 78-80, 83, 86, 89, 94-143, 149-154, 157, 166-167, 170 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/utils.py 37 25 32% 9-10, 30-33, 57-83, 90-100 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/app.py 162 97 40% 13, 54-98, 106, 114, 122-123, 131-132, 140, 148, 154, 160, 168, 182, 191-195, 200, 205, 213-225, 234-248, 253-267, 272-286, 291-295, 300-304, 309-313, 319-323, 329, 334-341, 346-347, 354-355, 364-365, 371-375, 383 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/base.py 92 37 60% 20, 26, 38, 49, 67-77, 88, 90, 101-103, 117-123, 126, 129, 142, 147, 150, 159, 162, 165, 174, 177, 186, 189, 210, 213 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/cli.py 27 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/utils.py 14 2 86% 32, 41 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/ansi.py 152 133 12% 30-47, 53-109, 115-187, 193-211, 214, 217, 225-228, 246 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/base.py 66 49 26% 6, 30-37, 68-99, 108-114, 126, 129, 144-145, 148-160, 168-174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/html.py 70 59 16% 30-96, 99, 102, 110-113, 119-123, 129-132 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/pygments.py 14 6 57% 8, 22, 25-30 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/utils.py 24 16 33% 28-29, 40-41, 56-57, 68-85 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/history.py 95 61 36% 33-34, 64-74, 80, 84-85, 101, 120-123, 126-139, 142, 145, 150, 153, 156, 165, 168, 177, 180, 184, 193-194, 197-221, 225-232 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/ansi_escape_sequences.py 12 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/base.py 53 14 74% 48, 52, 60, 65, 95, 104, 107, 110, 114, 117, 120, 123, 126, 131 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/defaults.py 24 17 29% 26-43, 51-58 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/typeahead.py 17 8 53% 54-55, 64-67, 75-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/vt100_parser.py 98 73 26% 48-61, 87-88, 91-92, 98-99, 108-118, 124-168, 176-188, 199-226, 240, 246-247 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/auto_suggest.py 30 22 27% 26-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/basic.py 157 147 6% 27, 31-248 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/completion.py 96 79 18% 21-22, 37-43, 61-79, 90-171, 178-203 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/cpr.py 12 6 50% 14-28 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/emacs.py 270 255 6% 41-335, 339-401, 409-558 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/focus.py 7 2 71% 16, 24 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/mouse.py 63 54 14% 21-146 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/named_commands.py 279 156 44% 56-59, 73-74, 82-83, 91-92, 102-103, 111-112, 118-119, 128-132, 141-145, 153, 162, 176, 184, 192, 200, 208-210, 219-223, 236, 244-246, 254-262, 270, 281-289, 297-302, 310-315, 323-328, 337, 354-364, 373-382, 391-411, 420, 428-436, 444-452, 460, 471-472, 481-482, 491-498, 511, 520, 528, 541, 550, 566-569, 578-586, 599, 609-627, 635, 643, 657, 666-678, 686-687 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/open_in_editor.py 14 7 50% 20, 29-35, 42-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/page_navigation.py 26 19 27% 38, 51-59, 67-79 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/scroll.py 80 69 14% 26-50, 57-80, 87, 94, 101-113, 120-144, 151-160, 169-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/vi.py 949 904 5% 74-76, 80-85, 91-94, 105-126, 133-141, 147-166, 181-289, 304-369, 388-2161, 2165-2210 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/defaults.py 11 2 82% 35-49 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/digraphs.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/emacs_state.py 17 7 59% 18-19, 22, 27, 31, 35-36 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_bindings.py 215 138 36% 57, 101, 104, 126, 137, 149, 158, 189-196, 199-201, 205, 209, 238-281, 297-325, 340-364, 376-389, 397-417, 461-462, 469, 475-476, 480-481, 484-485, 488-489, 514-517, 521-541, 555-556, 563-572, 583, 596-599, 602-607, 617-618, 625-635 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_processor.py 238 184 23% 23-24, 44-47, 53, 56-58, 91-98, 101-117, 124-127, 134-145, 152-204, 213-216, 222-225, 236-292, 298-303, 306-354, 362-375, 382-387, 397-420, 443-451, 454, 462, 466-469, 476, 483, 490-499, 506, 514-525, 530 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/vi_state.py 47 27 43% 7-8, 28-29, 40-76, 81, 86-91, 98-106 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/keys.py 165 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/containers.py 956 776 19% 62-64, 140, 148, 155, 160-166, 174, 215-227, 230, 233, 236, 289-308, 311-318, 321-327, 330-331, 339-364, 381-414, 428-474, 526-545, 548-555, 558-579, 582-583, 591-616, 623-667, 684-730, 769-775, 778-781, 784, 792, 803-841, 867-986, 1000-1011, 1014, 1017, 1020-1022, 1069-1090, 1093-1095, 1098-1100, 1103, 1149-1162, 1166, 1177-1185, 1195-1202, 1221, 1230-1236, 1243-1246, 1252-1255, 1263, 1277, 1284, 1294, 1301, 1309-1312, 1319-1324, 1342-1345, 1349, 1353, 1357, 1361, 1364, 1378-1379, 1490-1524, 1527, 1530-1543, 1551-1558, 1564, 1573-1593, 1604-1620, 1637-1671, 1680-1684, 1688-1695, 1710-1726, 1738-1898, 1924-2140, 2150-2162, 2169-2181, 2188-2191, 2203-2214, 2224-2262, 2277-2281, 2287-2292, 2303-2413, 2424-2507, 2525-2528, 2532-2541, 2545-2558, 2561, 2564, 2578-2579, 2582, 2585, 2588-2591, 2594-2597, 2608-2609, 2614, 2626, 2635-2636, 2639, 2642, 2645, 2648, 2651, 2656, 2663, 2670-2675, 2682-2687, 2695-2699 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/controls.py 311 237 24% 51-58, 88, 91, 100, 106, 126, 154, 179-186, 190-193, 216-266, 323-341, 344, 347, 350, 358, 367-369, 379-380, 384-430, 442-469, 472, 475, 488-491, 496, 545-574, 577, 583-589, 593-596, 606-610, 613, 626, 638-657, 666-670, 681-722, 730-819, 825-883, 886-887, 890-891, 897, 904-908, 929-939 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/dimension.py 91 53 42% 44, 56, 58, 60, 71, 75, 78, 94, 98, 101-111, 118-122, 130-167, 184-193, 201-207 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/dummy.py 17 7 59% 26-37 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/layout.py 188 143 24% 43-70, 73, 79-81, 84-85, 101-166, 175-193, 200, 207-212, 217, 222, 227, 236-241, 248-250, 258-259, 266-269, 276-280, 289-290, 297-300, 306-307, 313-323, 329-339, 345-346, 355-360, 366-375, 382-384, 391-394, 406-417 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/margins.py 119 90 24% 18, 43, 63, 80-81, 84-85, 90-132, 141-142, 145-148, 153-156, 173-175, 178, 183-243, 276-277, 282-283, 288-305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/menus.py 281 228 19% 43-45, 71, 74-81, 91-95, 101-132, 138, 144, 159-167, 173-181, 190-205, 218-232, 245-265, 281-284, 327-340, 343, 346, 353-371, 383-390, 396-507, 513, 519-557, 564-607, 626-664, 680-685, 694, 697-702, 705-720 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/mouse_handlers.py 12 4 67% 18-24, 39-40 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/processors.py 378 291 23% 38, 80, 109-115, 122, 155-157, 168, 187, 193-243, 264-267, 278-312, 323, 326-334, 354-357, 368-398, 404-439, 451-489, 502-503, 509-522, 529, 541, 544-550, 557, 570-571, 575-580, 583, 593, 597-607, 622-629, 632-645, 660-667, 670-684, 707-710, 713-764, 790-798, 803-855, 858-903, 930-931, 937-940, 943, 958, 961-962, 969-975, 985, 988-1029 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/screen.py 100 71 29% 9, 107-120, 123, 128, 131, 152-192, 198, 204, 211-214, 221-227, 234, 242-249, 256-263, 272-293, 300-307, 310 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/utils.py 32 17 47% 23, 26, 29, 35, 39, 48-53, 67-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/base.py 31 15 52% 38, 50, 53-62, 73-74, 77-78, 81-82 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/pygments.py 112 79 29% 29, 69, 86, 94-110, 117-129, 141-143, 193-202, 214-222, 229-335 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/mouse_events.py 14 3 79% 41-42, 45 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/base.py 130 33 75% 164, 174, 177, 180, 183, 186, 189, 192, 195, 198, 201, 204, 207, 210, 213, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/color_depth.py 29 12 59% 52-74 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/defaults.py 27 20 26% 31-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/vt100.py 289 192 34% 123-145, 169-175, 181-190, 237-256, 273, 276-300, 304-312, 321-369, 383-396, 420-432, 449-473, 476, 480, 484, 490, 497, 503-507, 512, 519, 522, 525, 528-534, 540-542, 548, 555, 558, 567-570, 573, 576, 579, 582, 588, 591-596, 599-606, 609-614, 617-622, 625, 628, 634-673, 679-680, 684-685 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/patch_stdout.py 61 43 30% 52-68, 81-93, 100-116, 128-139, 142-144, 147-150, 156-157, 164, 167 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/renderer.py 299 255 15% 27-28, 70-243, 262-263, 266-270, 303-326, 331-369, 377, 386, 395-402, 417-457, 464-480, 488, 494-514, 525-637, 648-657, 664-673, 686-716 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/search.py 86 66 23% 16-17, 56-58, 61, 73-78, 94-119, 126-150, 157-181, 190-217, 226 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/selection.py 19 5 74% 48-50, 53, 56 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/dialogs.py 89 60 33% 53-69, 86-99, 116-140, 152-159, 176-196, 213-231, 246-285, 290-294, 305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/base.py 177 119 33% 58-59, 74-85, 128-148, 152-233, 237-247, 265-269, 272, 281-283, 286-300, 303, 306, 326-342, 345-358, 366-367, 379, 383-387, 405, 409-415, 419-422, 429-432, 439-444 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/formatters.py 158 91 42% 22, 52, 55, 64, 72, 75, 89-90, 93-94, 103-113, 116-124, 141, 144, 164-172, 180-206, 211, 228, 233-237, 244-247, 262-263, 268-273, 291-297, 300-306, 325-326, 329-335, 352-353, 358, 391, 402-413, 416, 423 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/prompt.py 449 368 18% 133, 159-184, 193, 406-458, 470-475, 481-490, 516, 522-685, 693-761, 767-824, 912-994, 1010-1040, 1091-1173, 1178-1189, 1193, 1197, 1202-1219, 1222, 1235-1249, 1262-1270, 1274-1282, 1286-1292, 1303, 1307, 1359-1361, 1412-1435, 1442-1443 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/utils.py 59 41 31% 27, 96-147, 160-173, 180-183, 190-191, 198 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/base.py 37 10 73% 129, 148, 151, 155, 166-167, 172-174, 177, 181 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/defaults.py 15 2 87% 213, 223 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/named_colors.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/pygments.py 18 11 39% 14-15, 39-43, 51-56, 66-67 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/style.py 165 126 24% 39-74, 97-103, 112-167, 201, 227-242, 246, 256-264, 272-313, 316, 329-336, 352-353, 373-374, 380-383, 387-390, 395, 398 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/style_transformation.py 124 76 39% 54, 80-83, 94, 111-112, 115-121, 124, 159-160, 163-189, 196-205, 220, 223, 236, 240, 256, 259-262, 265-268, 280-281, 284-286, 289, 294, 297-299, 302, 311, 349-375 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/utils.py 115 73 37% 64-68, 72-73, 77, 86, 92-93, 99-100, 106-107, 116, 119, 141-160, 170, 178, 185, 193-195, 202, 209, 214, 235-271, 276-279, 284-287, 295-298, 308-311 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/validation.py 66 34 48% 32-34, 37, 65, 73-76, 100, 112-114, 117, 120-126, 137, 140, 147-150, 159, 169-170, 174-175, 186, 189-190, 193-194 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/base.py 292 180 38% 192-256, 274, 278, 285, 289, 296, 300, 303, 326-342, 351, 367-382, 393-402, 411-419, 422, 448-486, 516, 529, 552, 590-604, 624, 644-702, 710-717, 720-763, 766, 808-809, 813, 822, 827, 836, 841, 846-849, 880, 884-885, 888 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/dialogs.py 34 21 38% 49-103, 106 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/menus.py 179 152 15% 47-168, 214-223, 226-258, 261-328, 332, 335, 348-353, 357-360 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/toolbars.py 154 115 25% 62, 83-103, 108, 115-178, 187, 192-204, 207, 226-254, 260, 265-328, 333, 341, 346-369, 374 /usr/local/lib/python3.8/dist-packages/ptyprocess/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ptyprocess/ptyprocess.py 410 335 18% 16-17, 32-33, 41-47, 57-89, 95-116, 123-126, 140-148, 157-176, 202-338, 341-352, 356-358, 362, 371-379, 385, 393-402, 409, 420, 438-447, 456-465, 499-501, 515-528, 536-549, 552-555, 562, 573-590, 602, 608, 614, 622-654, 663-683, 692-760, 771-772, 777-780, 790, 802, 805-808, 818-819, 827-828, 835-836 /usr/local/lib/python3.8/dist-packages/ptyprocess/util.py 35 32 9% 3-67 /usr/local/lib/python3.8/dist-packages/pyasn1/__init__.py 4 1 75% 7 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/decoder.py 817 685 16% 33, 39, 45, 48-55, 65-79, 85-98, 112-122, 129, 141-190, 197-226, 237-263, 269-293, 304-314, 324-371, 381-478, 490, 493, 496-534, 540-737, 743-946, 983-1023, 1029-1074, 1084-1109, 1115-1171, 1312-1626 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/eoo.py 12 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/cer/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/cer/decoder.py 32 11 66% 22-35, 57 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/der/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/der/decoder.py 19 1 95% 37 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/compat/binary.py 18 15 17% 10-31 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/calling.py 7 3 57% 13-16 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/dateandtime.py 9 3 67% 16-17, 22 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/integer.py 68 58 15% 14-15, 20-94, 99, 102-107 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/octets.py 22 10 55% 10-27 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/string.py 11 8 27% 11-21, 26 /usr/local/lib/python3.8/dist-packages/pyasn1/debug.py 85 31 64% 52, 55, 63-65, 72-101, 104, 107, 110, 113, 122, 138, 151, 154 /usr/local/lib/python3.8/dist-packages/pyasn1/error.py 10 3 70% 47-49 /usr/local/lib/python3.8/dist-packages/pyasn1/type/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/type/base.py 249 74 70% 65, 70, 80, 108, 132, 138-141, 144, 149, 152, 155, 158, 162, 214, 233-236, 239, 271-273, 288, 294, 297, 300, 303, 306, 309, 312-313, 316, 367-376, 427, 447, 450, 456, 544, 550, 553, 556, 559, 562, 565, 568-569, 572, 576, 579, 604-614, 676, 681, 684, 687-691, 696, 699, 703 /usr/local/lib/python3.8/dist-packages/pyasn1/type/char.py 131 53 60% 58-97, 102, 105-109, 115-129, 135, 138, 145, 149-154, 157 /usr/local/lib/python3.8/dist-packages/pyasn1/type/constraint.py 177 81 54% 34-35, 49, 52, 55, 58, 61, 64, 67-68, 80, 88, 94, 143, 148, 151, 154, 157, 160, 200-204, 245, 249, 254, 318-320, 399-400, 403-404, 429-432, 435-436, 462-465, 468-469, 545-546, 549, 557-565, 568-575, 616-623, 626, 632, 635, 641, 644, 740-748 /usr/local/lib/python3.8/dist-packages/pyasn1/type/error.py 3 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/type/namedtype.py 232 68 71% 19-20, 58, 61, 64, 67, 70, 73, 76, 79, 82, 99, 102, 181, 184, 187, 190, 193, 196, 199, 202-206, 209, 212, 215-216, 227, 230, 233, 236, 243, 250, 252, 264, 301-305, 325-329, 349-353, 373-377, 402-406, 435-439, 470, 479, 488-489, 515, 549, 553, 561 /usr/local/lib/python3.8/dist-packages/pyasn1/type/namedval.py 89 41 54% 66-71, 74, 77, 83-90, 94-104, 116, 119, 122, 125, 128, 131, 134, 149, 152, 155, 158, 167, 172-173, 178-179, 182-183, 186-190 /usr/local/lib/python3.8/dist-packages/pyasn1/type/opentype.py 22 8 64% 78, 84, 89, 92, 95, 98, 101, 104 /usr/local/lib/python3.8/dist-packages/pyasn1/type/tag.py 123 34 72% 59, 67-69, 73, 76, 79, 82, 85, 88, 91, 94-101, 104-106, 109, 114, 210, 216, 222, 228, 234, 251, 262, 282, 325-327, 332 /usr/local/lib/python3.8/dist-packages/pyasn1/type/tagmap.py 44 20 55% 41, 45-53, 59-70, 90, 93, 96 /usr/local/lib/python3.8/dist-packages/pyasn1/type/univ.py 1274 794 38% 110, 113, 116, 119, 122, 125, 128, 131, 134, 137, 140, 143, 146, 149, 152, 155, 158, 161, 164, 167, 170-180, 183, 186, 189, 192, 197, 200-201, 204, 207, 210, 213, 216, 219, 222-226, 229, 232, 236, 242, 245, 248, 254, 264-265, 279, 346, 360-363, 468, 471, 479, 482-483, 486-487, 490-491, 494-495, 498-499, 502-503, 508, 511-517, 520-523, 526, 531-532, 535-536, 539-544, 547, 550, 553, 556, 559, 562-563, 571, 579, 584, 589-590, 601-615, 629-630, 633, 638, 651-661, 665, 667-705, 710-717, 726-732, 829, 832, 840-885, 890, 896-912, 915-920, 927, 930, 933, 954, 958-971, 982-1002, 1013-1024, 1029, 1032-1035, 1038, 1041, 1044, 1047, 1050, 1053, 1056, 1059, 1062, 1112, 1117, 1182, 1185, 1188, 1193, 1196-1199, 1202, 1205, 1208, 1224-1228, 1232, 1235, 1240-1241, 1248-1249, 1256, 1259, 1312-1315, 1334-1338, 1341-1376, 1381-1385, 1398, 1410, 1414, 1417, 1420, 1423, 1426, 1429, 1432, 1435, 1438, 1441, 1444, 1447-1451, 1454, 1457, 1460, 1463, 1466, 1469-1470, 1473-1476, 1481, 1484, 1487, 1490-1494, 1497, 1500, 1504, 1507, 1510, 1513, 1516, 1519, 1522, 1525-1526, 1529, 1534-1537, 1542, 1545, 1548, 1660-1664, 1673-1677, 1680-1684, 1687-1693, 1696, 1699-1703, 1706-1718, 1721, 1724, 1729-1732, 1735-1736, 1739-1746, 1814-1839, 1885-1950, 1954-1955, 1959, 1968-1969, 1977-1978, 1981-1995, 1998-2003, 2036-2043, 2056-2079, 2183, 2186, 2189, 2192-2196, 2199-2203, 2206-2210, 2213-2214, 2227-2241, 2244-2258, 2261-2264, 2267, 2270, 2275-2276, 2279, 2282-2286, 2289-2292, 2300-2302, 2310-2312, 2319-2329, 2359-2368, 2403-2412, 2484-2508, 2546-2615, 2652, 2664-2671, 2686-2711, 2721-2733, 2736-2747, 2752, 2755-2756, 2759-2760, 2787-2788, 2791-2794, 2820, 2849-2858, 2896-2911, 2917-2918, 2988-2990, 2993-2995, 2998-3000, 3003-3005, 3008-3010, 3013-3015, 3018-3019, 3022, 3025, 3028-3030, 3033-3035, 3040-3041, 3044-3045, 3048-3049, 3052-3053, 3056-3070, 3073-3079, 3118-3123, 3128-3132, 3152-3159, 3169-3176, 3209-3211, 3214-3215, 3220 /usr/local/lib/python3.8/dist-packages/pyasn1/type/useful.py 99 52 47% 51, 54, 57, 70-126, 143-157 /usr/local/lib/python3.8/dist-packages/pyasn1_modules/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1_modules/pem.py 39 32 18% 16-46, 53-54, 58-61, 65 /usr/local/lib/python3.8/dist-packages/pyasn1_modules/rfc2251.py 115 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1_modules/rfc2459.py 418 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1_modules/rfc5208.py 18 0 100% /usr/local/lib/python3.8/dist-packages/pygments/__init__.py 26 17 35% 41-49, 60-73, 84 /usr/local/lib/python3.8/dist-packages/pygments/filter.py 24 14 42% 19-24, 36, 50, 53, 66-69, 73-74 /usr/local/lib/python3.8/dist-packages/pygments/filters/__init__.py 159 124 22% 25-30, 39-43, 48-51, 56-64, 78-81, 86-95, 113-116, 119-123, 151-157, 160-164, 184-191, 194-197, 231-245, 248-277, 295-296, 299-302, 305-316, 326, 329-340 /usr/local/lib/python3.8/dist-packages/pygments/formatter.py 29 15 48% 21-23, 67-75, 85, 92-95 /usr/local/lib/python3.8/dist-packages/pygments/formatters/__init__.py 85 49 42% 31-34, 48-53, 61-68, 76-79, 98-115, 123-134, 147 /usr/local/lib/python3.8/dist-packages/pygments/formatters/_mapping.py 2 0 100% /usr/local/lib/python3.8/dist-packages/pygments/formatters/html.py 363 319 12% 40, 44-47, 51-59, 405-449, 454-457, 462-466, 469-490, 498-529, 532-536, 539-578, 581-640, 644-676, 679-687, 690-697, 700-712, 715-730, 733-736, 743-807, 810-814, 821-836, 844-847, 863-880 /usr/local/lib/python3.8/dist-packages/pygments/lexer.py 469 375 20% 96-105, 108-112, 118-120, 145-193, 203, 217-220, 223-236, 257, 281-282, 285, 288, 291-293, 296, 299, 307-324, 352-356, 359-372, 376-383, 412, 423-425, 429-431, 435-466, 470-510, 514-518, 532-567, 571-580, 619-673, 682-685, 688, 702-762, 779-825, 832-847, 858-877 /usr/local/lib/python3.8/dist-packages/pygments/lexers/__init__.py 191 147 23% 37-40, 55-58, 66-76, 86-98, 106-119, 137-154, 165-197, 208-211, 219-227, 232-239, 258-290, 296-321, 334-336 /usr/local/lib/python3.8/dist-packages/pygments/lexers/_mapping.py 2 0 100% /usr/local/lib/python3.8/dist-packages/pygments/lexers/python.py 123 53 57% 375, 599, 631-632, 635-690, 1143-1148, 1151 /usr/local/lib/python3.8/dist-packages/pygments/modeline.py 20 14 30% 24-26, 33-44 /usr/local/lib/python3.8/dist-packages/pygments/plugin.py 23 13 43% 45-50, 54-55, 59-60, 64-65, 69-70 /usr/local/lib/python3.8/dist-packages/pygments/regexopt.py 43 32 26% 24, 29-77, 91-92 /usr/local/lib/python3.8/dist-packages/pygments/style.py 94 59 37% 68-80, 93, 98-123, 128-143, 158, 161, 164-165, 168 /usr/local/lib/python3.8/dist-packages/pygments/styles/__init__.py 27 21 22% 58-78, 84-87 /usr/local/lib/python3.8/dist-packages/pygments/token.py 59 14 76% 30, 37, 45, 49, 53, 92, 111-118 /usr/local/lib/python3.8/dist-packages/pygments/unistring.py 65 7 89% 27-28, 129, 133-136 /usr/local/lib/python3.8/dist-packages/pygments/util.py 179 143 20% 42-48, 52-66, 72-80, 86-92, 98-106, 112-121, 154-168, 177-181, 186, 194-205, 216, 221-254, 259-273, 282-289, 299, 309-320, 330-337, 342-345, 351 /usr/local/lib/python3.8/dist-packages/pyparsing.py 3062 1771 42% 122-123, 127-128, 134-137, 141-145, 149-150, 191-194, 233-266, 274-278, 295-296, 307-308, 321, 329-336, 339-346, 349, 354-359, 361, 410-453, 487, 490, 498, 500, 563, 567, 576, 580, 588-591, 606-611, 616-634, 650, 653-656, 659, 662, 675-694, 738-754, 794-798, 813, 829-832, 838-839, 844-845, 848-850, 869-874, 877, 880, 883-891, 908, 930-944, 961-1016, 1019-1023, 1050-1063, 1086-1128, 1155, 1159, 1166-1173, 1176, 1179, 1188-1207, 1222-1223, 1235, 1240-1245, 1248, 1251, 1254, 1258, 1297-1302, 1319-1337, 1344-1345, 1370, 1392, 1396-1398, 1464, 1504-1516, 1559, 1562, 1612-1613, 1616-1626, 1630, 1636, 1641, 1653-1674, 1694-1712, 1717-1720, 1729-1730, 1735-1738, 1741-1746, 1750-1768, 1783-1786, 1792, 1800-1826, 1853-1856, 1896, 1939, 1945-1955, 1990-2031, 2053-2079, 2102-2111, 2129-2136, 2166, 2171-2173, 2181, 2186-2188, 2195-2201, 2207-2213, 2236, 2238, 2246, 2248-2253, 2258-2260, 2263, 2265, 2267, 2276-2279, 2284, 2290, 2297, 2300, 2302-2304, 2311-2317, 2323-2329, 2335-2341, 2347-2353, 2359-2365, 2376, 2398-2412, 2465-2466, 2482-2490, 2496-2500, 2539-2543, 2549, 2557, 2563, 2571-2585, 2589, 2591, 2593, 2597, 2603, 2606, 2622-2626, 2724-2788, 2795-2799, 2802-2815, 2818, 2821, 2846-2850, 2853, 2876-2879, 2891-2893, 2932-2950, 2953-2969, 2972-2974, 2980, 2994-2998, 3001-3003, 3016, 3052-3058, 3061-3084, 3146, 3159, 3164, 3181, 3187, 3191-3192, 3208, 3213-3218, 3223, 3248-3253, 3262-3267, 3304, 3313-3324, 3335, 3337, 3348-3349, 3353-3359, 3362-3368, 3392-3408, 3451-3512, 3515-3547, 3550-3558, 3587, 3593, 3610-3620, 3630, 3682, 3687-3688, 3691-3703, 3718-3719, 3722-3728, 3731-3736, 3766-3768, 3780-3788, 3799-3803, 3814, 3817-3820, 3832-3834, 3837-3841, 3852-3855, 3858-3863, 3873, 3876, 3878, 3883, 3886-3889, 3893-3895, 3907-3916, 3919-3926, 3963-3966, 3975-3977, 4007-4009, 4014-4024, 4036-4043, 4056-4057, 4059-4067, 4075-4077, 4080-4084, 4114-4118, 4121-4124, 4127-4184, 4188-4190, 4193-4199, 4202-4204, 4207-4216, 4241, 4260-4263, 4271, 4274-4276, 4280, 4288-4290, 4293-4302, 4363-4367, 4370-4372, 4375-4421, 4424-4430, 4433-4435, 4448, 4464, 4474-4483, 4492-4496, 4499-4504, 4540-4541, 4546-4549, 4581-4601, 4604-4624, 4658-4660, 4664, 4677, 4682, 4691, 4696, 4702, 4704, 4716-4726, 4757, 4787, 4797, 4800, 4852-4856, 4863, 4936, 4942-4986, 5016, 5019-5029, 5032, 5035-5036, 5039-5043, 5046-5053, 5056-5073, 5076-5081, 5084-5092, 5131-5135, 5138-5145, 5213-5234, 5263, 5270-5271, 5273-5277, 5279, 5306-5324, 5346, 5373-5384, 5387-5393, 5410-5423, 5440-5452, 5494-5547, 5586, 5617-5628, 5634, 5661-5662, 5708-5709, 5715-5718, 5733, 5748, 5787, 5792-5793, 5811-5812, 5818-5819, 5873, 5931-5943, 5981-5982, 6060-6115, 6192-6229, 6312-6355, 6365, 6622-6627, 6647-6652, 6680, 6705-6713, 6734-6740, 6745, 6750, 6755, 6760, 6886, 6889-6902, 6906-6921, 6924, 6927, 6940-6943, 6952-6955, 6964-6967, 6981-7028, 7034-7035, 7040-7105 /usr/local/lib/python3.8/dist-packages/pywt/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/pywt/_c99_config.py 1 0 100% /usr/local/lib/python3.8/dist-packages/pywt/_cwt.py 85 75 12% 18-34, 113-203 /usr/local/lib/python3.8/dist-packages/pywt/_dwt.py 161 147 9% 62-80, 114-117, 164-188, 240-292, 331-343, 390-401, 439-517 /usr/local/lib/python3.8/dist-packages/pywt/_extensions/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pywt/_functions.py 79 61 23% 30-32, 36-38, 42-44, 48-50, 54-56, 92-119, 141-160, 180, 201-203, 228-240 /usr/local/lib/python3.8/dist-packages/pywt/_multidim.py 98 85 13% 65-74, 112-118, 166-192, 196-219, 254-311 /usr/local/lib/python3.8/dist-packages/pywt/_multilevel.py 531 464 13% 32-46, 90-109, 147-176, 224-253, 300-337, 341-357, 424-442, 449-458, 518-578, 583-592, 597-609, 613-625, 633-671, 736-788, 854-877, 914-935, 965-978, 1013-1021, 1064-1106, 1152-1181, 1186-1191, 1216-1226, 1231, 1235-1238, 1243, 1248, 1253, 1258, 1263, 1268, 1273, 1278-1280, 1283-1286, 1291-1292, 1296-1300, 1303-1319, 1331-1333, 1347-1356, 1366-1368, 1450-1480, 1530-1551 /usr/local/lib/python3.8/dist-packages/pywt/_pytesttester.py 44 35 20% 36-42, 113-164 /usr/local/lib/python3.8/dist-packages/pywt/_swt.py 258 241 7% 18-24, 113-150, 187-256, 345-365, 420-502, 581-638, 680-774 /usr/local/lib/python3.8/dist-packages/pywt/_thresholding.py 63 51 19% 18-31, 36-49, 53-55, 59-62, 66-69, 164-170, 230-250 /usr/local/lib/python3.8/dist-packages/pywt/_utils.py 38 28 26% 17, 22-30, 50-66, 86-101 /usr/local/lib/python3.8/dist-packages/pywt/_wavelet_packets.py 328 261 20% 20-24, 56-78, 81-82, 85, 88-93, 96, 99, 102, 105-106, 118-136, 140-149, 153, 168-171, 174, 190-192, 195, 211-216, 236-246, 261-283, 295-303, 307, 311-314, 325-336, 357-363, 382-389, 392, 409, 419-429, 432-450, 470, 479-488, 492-521, 524-530, 553-569, 581-586, 610-631, 653-668, 680-685, 705-733 /usr/local/lib/python3.8/dist-packages/pywt/data/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/pywt/data/_readers.py 27 20 26% 39-41, 74-76, 107-109, 137-139, 173-185 /usr/local/lib/python3.8/dist-packages/pywt/data/_wavelab_signals.py 180 175 3% 69-259 /usr/local/lib/python3.8/dist-packages/pywt/version.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/rsa/__init__.py 10 2 80% 34-36 /usr/local/lib/python3.8/dist-packages/rsa/common.py 57 46 19% 22-25, 50-53, 76-78, 99-102, 112-127, 139-144, 167-179, 183-185 /usr/local/lib/python3.8/dist-packages/rsa/core.py 20 16 20% 23-26, 32-42, 48-53 /usr/local/lib/python3.8/dist-packages/rsa/key.py 242 170 30% 55-56, 110-116, 124-128, 140-146, 163, 177, 207, 210, 214, 218, 221-227, 230, 233, 256-260, 269-277, 291-292, 301-302, 320-321, 332-341, 371-379, 382, 385, 389, 393, 396-402, 412, 415, 418-422, 434-438, 450-453, 477-511, 520-548, 563-564, 573-574, 610-654, 670-684, 697, 721-728, 760-781, 790-803 /usr/local/lib/python3.8/dist-packages/rsa/pem.py 50 42 16% 29-32, 39-78, 97-105, 120-132 /usr/local/lib/python3.8/dist-packages/rsa/pkcs1.py 124 99 20% 40, 95-120, 145-154, 182-189, 243-264, 284-297, 318-319, 337-357, 371-376, 388-397, 411-425, 436-440, 447-458 /usr/local/lib/python3.8/dist-packages/rsa/prime.py 63 52 17% 34-36, 53-62, 82-115, 130-141, 160-167, 182-183, 187-198 /usr/local/lib/python3.8/dist-packages/rsa/randnum.py 30 22 27% 32-43, 50-57, 67-70, 81-96 /usr/local/lib/python3.8/dist-packages/rsa/transform.py 14 9 36% 34, 58-66, 70-72 /usr/local/lib/python3.8/dist-packages/scipy/__config__.py 28 14 50% 12-13, 24-25, 28-37 /usr/local/lib/python3.8/dist-packages/scipy/__init__.py 62 9 85% 65, 126-128, 132-136, 141-142 /usr/local/lib/python3.8/dist-packages/scipy/_distributor_init.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/_ccallback.py 98 71 28% 15-23, 87-88, 91, 95, 99, 103, 106, 125-131, 135-159, 168-180, 184-202, 207, 216-222, 227 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_numpy_compat.py 275 254 8% 18-50, 58-87, 92-102, 109-197, 203-289, 294-568, 573-781 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_testutils.py 77 62 19% 31-73, 81-82, 89-104, 108-119, 126-145 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_threadsafety.py 33 9 73% 33-37, 40-41, 45-46 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_uarray/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/_uarray/_backend.py 93 47 49% 48-57, 61-74, 96-99, 194, 213, 275, 306, 338-340, 343, 346, 363, 379-392, 404-417 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_util.py 166 107 36% 19-24, 42-60, 90-100, 111-126, 134-136, 164, 167-170, 186-196, 238-241, 244, 249, 251-252, 320-347, 367-389, 393, 396-397, 400-401, 404-405, 408-409, 412-414, 418-422 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_version.py 76 33 57% 59, 72, 82-87, 91-95, 101-112, 116, 124-132, 140, 143, 146, 155 /usr/local/lib/python3.8/dist-packages/scipy/_lib/decorator.py 252 115 54% 51-68, 77-78, 102, 114, 121-122, 124, 126-127, 140, 146, 150, 162-163, 175, 181, 192-195, 244, 253, 259-261, 275-276, 282, 290-292, 294, 297, 310-319, 328-424 /usr/local/lib/python3.8/dist-packages/scipy/_lib/deprecation.py 15 4 73% 11-14, 18-20 /usr/local/lib/python3.8/dist-packages/scipy/_lib/doccer.py 97 21 78% 53, 116-125, 140, 145, 165, 171, 200, 250, 268-274 /usr/local/lib/python3.8/dist-packages/scipy/_lib/six.py 177 112 37% 43-65, 75-76, 87-92, 106-114, 119-121, 127, 134-142, 155, 160, 165, 170, 173, 176-177, 185-192, 202-204, 210-270, 277 /usr/local/lib/python3.8/dist-packages/scipy/_lib/uarray.py 13 5 62% 17-20, 24-25 /usr/local/lib/python3.8/dist-packages/scipy/cluster/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/cluster/hierarchy.py 780 667 14% 155, 162-167, 177-178, 195-200, 278, 360, 442, 527, 629, 731, 1040, 1045-1046, 1049, 1052-1053, 1058, 1061, 1068, 1072, 1075, 1113, 1115, 1118, 1121, 1130, 1133-1136, 1139-1142, 1145-1148, 1164, 1178, 1191, 1204, 1216, 1250-1278, 1299-1310, 1357-1391, 1452-1488, 1529-1551, 1555-1559, 1566, 1675-1700, 1758-1774, 1847-1868, 1946-1955, 2036-2040, 2130-2165, 2249-2290, 2294-2298, 2302-2309, 2313-2319, 2355-2357, 2409-2413, 2568-2601, 2687-2700, 2751-2757, 2781-2787, 2791-2793, 2797-2799, 2808-2933, 2995-3007, 3272-3359, 3368-3385, 3394-3403, 3407-3408, 3412-3415, 3468-3648, 3712-3743, 3819-3826, 3904-3916, 3996-4013, 4120-4138 /usr/local/lib/python3.8/dist-packages/scipy/cluster/vq.py 152 126 17% 132-140, 201-210, 250-263, 303-316, 433-461, 482-483, 507-528, 555-571, 579, 585, 707-761 /usr/local/lib/python3.8/dist-packages/scipy/constants/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/constants/codata.py 97 15 85% 1566, 1616-1617, 1641-1642, 1692-1704, 1725 /usr/local/lib/python3.8/dist-packages/scipy/constants/constants.py 142 21 85% 220-246, 278, 307 /usr/local/lib/python3.8/dist-packages/scipy/fft/__init__.py 27 2 93% 112-113 /usr/local/lib/python3.8/dist-packages/scipy/fft/_backend.py 33 13 61% 19-23, 36-37, 40, 90-91, 122-123, 151-152 /usr/local/lib/python3.8/dist-packages/scipy/fft/_basic.py 64 23 64% 9-13, 154, 252, 339, 434, 510, 570, 668, 764, 857, 947, 1040, 1083, 1183, 1226, 1333, 1375, 1463, 1506 /usr/local/lib/python3.8/dist-packages/scipy/fft/_helper.py 10 2 80% 62, 101 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/basic.py 133 87 35% 16-30, 44-58, 72-90, 103, 110, 117, 124, 131, 138, 146-161, 172-186, 198-223, 234-251 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/helper.py 106 84 21% 28-38, 43-77, 86-95, 102-106, 111-133, 137-141, 147-153, 158-170, 190-195, 210 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/realtransforms.py 64 42 34% 19-46, 71-99 /usr/local/lib/python3.8/dist-packages/scipy/fft/_realtransforms.py 28 8 71% 63, 121, 179, 237, 387, 451, 568, 618 /usr/local/lib/python3.8/dist-packages/scipy/integrate/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_bvp.py 378 352 7% 29-57, 75-116, 124-142, 152-158, 243-276, 310-317, 322-347, 418-502, 506, 513, 556-577, 590-602, 632, 642-710, 1001-1159 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/base.py 99 80 19% 7-23, 118-151, 155-158, 170-191, 201-209, 212, 215, 231-234, 250-253, 256, 266-267, 270-275 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/bdf.py 245 222 9% 21-26, 31-34, 39-70, 188-242, 245-295, 298-438, 441, 447-451, 454-467 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/common.py 214 189 12% 13-17, 22-24, 39-40, 46-57, 62, 100-120, 153-176, 181-190, 206-238, 292-320, 325-363, 367-432 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/dop853_coefficients.py 153 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/ivp.py 162 143 12% 31-51, 77-78, 109-128, 146-154, 504-662 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/lsoda.py 57 46 19% 108-138, 141-161, 164-172, 177-180, 183-188 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/radau.py 262 230 12% 88-137, 169-177, 286-334, 337-387, 390-529, 532-533, 536, 541-545, 548-562 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/rk.py 190 127 33% 62-72, 89-104, 107, 110, 113-177, 180-181, 480-485, 488-494, 497-503, 506-524, 529-533, 536-549, 554-557, 560-576 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ode.py 507 401 21% 348-353, 357, 361-369, 382-394, 422-437, 441-445, 532-536, 540-541, 545-546, 562-567, 584-587, 620-625, 628-633, 637-656, 660, 673-686, 690-694, 722-723, 739-742, 751-754, 765-769, 786-787, 790-791, 804, 809, 814, 826-831, 862-881, 912-939, 942-987, 990-1014, 1017-1021, 1024-1028, 1044-1100, 1132-1143, 1146-1151, 1154-1168, 1171-1179, 1182-1187, 1210, 1216-1230, 1267-1283, 1287-1333, 1336-1353, 1356-1360, 1363-1367 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_quad_vec.py 262 227 13% 16, 19-24, 28, 36-41, 44-48, 51-56, 64-67, 70-71, 74-79, 83, 87-93, 98-99, 102, 202-400, 404-429, 436-449, 461-504, 512-569, 580-622 /usr/local/lib/python3.8/dist-packages/scipy/integrate/odepack.py 32 22 31% 229-260 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadpack.py 199 172 14% 41, 334-432, 436-465, 469-516, 581-585, 665-674, 799-810, 815, 823, 828, 832, 837-844, 847-883 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadrature.py 312 274 12% 45-49, 117-123, 151-168, 235-252, 256-258, 315-350, 354-381, 454-506, 568-623, 650-660, 668-669, 674-686, 773-806, 934-975 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_bsplines.py 324 291 10% 20-22, 27-30, 38-43, 182-226, 235-239, 245, 303-308, 332-355, 358, 367-370, 391-397, 425-437, 488-571, 581-588, 593, 597-604, 608-617, 735-861, 969-1022 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_cubic.py 259 230 11% 28-72, 142-158, 235-240, 245-255, 268-304, 343-350, 405-435, 438, 445, 450, 619-770, 784-837 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_fitpack_impl.py 413 388 6% 44-48, 215-311, 443-524, 580-607, 654-668, 710-733, 773-791, 890-988, 1039-1057, 1079-1080, 1128-1146, 1196-1229, 1288-1311 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_pade.py 27 22 19% 46-67 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack.py 65 49 25% 156-158, 289-290, 353-368, 417-429, 477-491, 531-534, 586-601, 654-657, 719-722 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack2.py 366 304 17% 171-196, 201-208, 211-232, 235-241, 244-256, 265-276, 303-317, 324-326, 330-332, 342, 380, 405-408, 415-421, 468-471, 524-525, 602-618, 741-770, 793, 801, 805, 841-880, 955-961, 983, 1002-1004, 1048-1069, 1110-1136, 1172-1198, 1273-1281, 1304, 1385-1396, 1486-1504, 1670-1727 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/interpolate.py 920 819 11% 32-34, 84-94, 201-252, 279-313, 318-328, 335, 431-540, 547, 552-581, 585, 591-614, 623-631, 637-646, 649, 652-654, 660-669, 687-700, 708-751, 754-758, 770-777, 784-787, 808-870, 902-923, 986, 1013-1031, 1063-1085, 1108-1167, 1217-1239, 1268, 1284-1296, 1313-1332, 1422, 1443-1477, 1502-1533, 1558-1597, 1600-1603, 1621-1639, 1705-1757, 1814-1843, 1873-1883, 1946-1965, 1978-1984, 1987-1991, 1994-1997, 2028-2062, 2069-2096, 2103-2136, 2163-2169, 2197-2203, 2236-2264, 2287-2312, 2415-2452, 2468-2501, 2505-2516, 2519-2521, 2525-2541, 2601-2676, 2689-2704, 2707, 2710-2712, 2717-2724 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/ndgriddata.py 47 38 19% 59-65, 77-81, 193-228 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/polyint.py 203 169 17% 18, 56-60, 78-80, 86, 90-92, 96-103, 106-111, 114-131, 134-139, 177-188, 216-218, 293-316, 319-326, 329-355, 400-406, 448-461, 502-513, 532-537, 559-577, 599, 602-617, 666 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/rbf.py 105 83 21% 145, 148, 151, 154, 157, 160, 163, 167-216, 223-268, 274-275, 278, 281-290 /usr/local/lib/python3.8/dist-packages/scipy/io/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/_fortran.py 79 61 23% 111-126, 129-136, 161-169, 245-294, 317, 340, 349, 352, 355 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/_fortran_format_parser.py 165 124 25% 34, 62-66, 69-71, 74-80, 84-90, 94, 123-132, 141-144, 147-153, 157-163, 167, 172-174, 177, 180, 185-186, 189-191, 194-205, 234, 237-250, 253-257, 260-261, 264-305, 308-312 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/hb.py 265 215 19% 45, 70-120, 141-205, 217-281, 285-299, 303-308, 313-332, 336-360, 391-400, 403-412, 416, 421, 437-443, 447, 451, 455, 459, 463, 466, 469, 496-504, 534-547 /usr/local/lib/python3.8/dist-packages/scipy/io/idl.py 429 382 11% 81-84, 89-90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 152-153, 163-170, 175-182, 187-224, 233-269, 278-316, 322-425, 431-445, 451-492, 498-546, 552-565, 570-631, 652, 655, 658, 705-873 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/byteordercodes.py 19 12 37% 57-69 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio.py 65 47 28% 19-22, 32-47, 71-80, 215-225, 266-279, 314-317 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio4.py 281 222 21% 91-95, 102-106, 110-127, 135-149, 168-185, 202-207, 221-223, 254-267, 273-301, 313-314, 317-326, 333-334, 351-357, 375, 386-406, 410-422, 442-446, 451-452, 455, 458, 478-490, 504-519, 522-541, 544-560, 567-584, 590-594, 616-618 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5.py 371 312 16% 151-167, 172-175, 179-186, 196-198, 217-233, 252, 261-307, 311-331, 375-400, 422-455, 471-477, 480, 483, 487-496, 500-504, 508-516, 536-559, 562-570, 587-590, 601-630, 633-655, 660-698, 703-718, 721-726, 729-733, 736-738, 742-755, 761-765, 789-798, 802-809, 829-849 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5_params.py 77 11 86% 195-197, 227-231, 235, 242-243, 249-250 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/miobase.py 108 70 35% 20, 179-184, 221-241, 305-318, 324, 328, 332, 363-377, 381-383, 387, 390-393, 398, 403-415 /usr/local/lib/python3.8/dist-packages/scipy/io/mmio.py 444 375 16% 54, 75, 101, 115, 119, 123, 127, 131, 135, 139, 150-151, 164-165, 178-179, 191, 196, 226-269, 294-327, 332-384, 389, 398, 417-425, 451-460, 469-479, 483-485, 490-657, 662-802, 812-828, 833-839 /usr/local/lib/python3.8/dist-packages/scipy/io/netcdf.py 486 393 19% 239-284, 289-293, 297-319, 323, 326, 348-352, 385-397, 408-409, 413-421, 425-428, 431-439, 442, 445-452, 455-479, 482-512, 515-553, 556-558, 561-602, 606-616, 619, 622-631, 634-635, 638-647, 650-733, 736-758, 761-776, 779-782, 785, 789, 793, 796, 799-802, 805-808, 867-876, 881-885, 896, 905, 919, 938-946, 958, 970, 973-988, 991-1021, 1027-1028, 1037-1045, 1058-1065, 1077-1093 /usr/local/lib/python3.8/dist-packages/scipy/linalg/__init__.py 36 4 89% 222-223, 227-228 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_ldl.py 85 74 13% 123-156, 207-241, 268-297, 335-354 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_polar.py 16 11 31% 98-112 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_qz.py 127 110 13% 19-34, 38-43, 47-52, 56-61, 65-71, 76-145, 262-265, 358-405 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_expm_frechet.py 153 138 10% 91-114, 122-127, 166-173, 177-187, 191-203, 207-222, 226-278, 298, 334-350, 393-411 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_matfuncs_sqrtm.py 85 74 13% 52-116, 163-196 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_procrustes.py 18 13 28% 76-91 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_sketches.py 15 8 47% 49-54, 167-168 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_solvers.py 216 194 10% 86-107, 159-199, 214-218, 228-233, 306-324, 446-528, 652-736, 778-844 /usr/local/lib/python3.8/dist-packages/scipy/linalg/basic.py 385 346 10% 27-37, 137-258, 330-359, 433-472, 568-596, 669-702, 706-711, 862-907, 952, 979, 981, 1034-1043, 1157-1246, 1304-1318, 1373-1391, 1451-1470, 1575-1619 /usr/local/lib/python3.8/dist-packages/scipy/linalg/blas.py 86 12 86% 301-310, 341, 352, 381-384 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp.py 364 338 7% 41-47, 51-73, 78-115, 214-267, 374-489, 503-530, 640-695, 767, 858, 951, 1031, 1124-1194, 1199-1203, 1252-1283, 1368-1431 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_cholesky.py 72 61 15% 19-44, 90-92, 154-156, 194-213, 274-286, 334-353 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_lu.py 48 38 21% 71-86, 135-148, 209-223 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_qr.py 130 121 7% 16-25, 121-173, 251-320, 386-424 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_schur.py 104 85 18% 119-178, 191-197, 201-210, 266-295 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_svd.py 88 60 32% 111, 116, 118, 132, 134, 139, 225-232, 273-281, 323-330, 384-391, 459-496 /usr/local/lib/python3.8/dist-packages/scipy/linalg/flinalg.py 30 23 23% 14-19, 23, 32-58 /usr/local/lib/python3.8/dist-packages/scipy/linalg/lapack.py 45 4 91% 808, 814, 826, 830 /usr/local/lib/python3.8/dist-packages/scipy/linalg/linalg_version.py 5 0 100% /usr/local/lib/python3.8/dist-packages/scipy/linalg/matfuncs.py 130 103 21% 52-55, 84-89, 136-138, 195-208, 255-256, 291-295, 330-334, 371-372, 409-410, 447-448, 485-486, 551-590, 626-670 /usr/local/lib/python3.8/dist-packages/scipy/linalg/misc.py 42 30 29% 141-181, 192-194 /usr/local/lib/python3.8/dist-packages/scipy/linalg/special_matrices.py 223 197 12% 62-73, 104-106, 138-140, 193-203, 239-244, 290-301, 344-358, 413-430, 465-471, 535-553, 600-617, 654-662, 698-700, 759-777, 840-863, 938-973, 1033-1043, 1109-1119, 1172-1196 /usr/local/lib/python3.8/dist-packages/scipy/misc/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/scipy/misc/common.py 73 65 11% 35-47, 85-120, 154-159, 195-204, 297-303 /usr/local/lib/python3.8/dist-packages/scipy/misc/doccer.py 29 8 72% 15, 21, 27, 33, 39, 44, 49, 54 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_docstrings.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_support.py 43 13 70% 42, 44, 46, 48, 52, 64-65, 78, 80-81, 83, 89, 91 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/filters.py 399 298 25% 81, 85, 90, 129-133, 141, 156-164, 332-340, 369-377, 404-419, 447-449, 487-495, 525-544, 583-591, 598-623, 648, 754, 783-795, 842-857, 897-909, 954-966, 971-1033, 1069, 1106, 1113-1162, 1201-1202, 1240, 1280, 1348-1364, 1421-1448 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/fourier.py 69 58 16% 42-55, 59-70, 120-129, 179-187, 241-249, 298-306 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/interpolation.py 210 182 13% 93-105, 127-139, 245-263, 329-351, 433-487, 520-539, 589-616, 677-746 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/measurements.py 315 288 9% 178-236, 298-305, 377-458, 463-466, 500-573, 617-618, 669-670, 721-722, 773, 782-884, 946, 1025, 1086, 1155-1164, 1210-1219, 1275-1293, 1355-1364, 1419-1424, 1459-1498 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/morphology.py 403 368 9% 50-53, 106-122, 208, 210, 218-285, 381, 501-513, 629-636, 775-782, 871-892, 1022, 1097-1107, 1213-1216, 1338-1364, 1443-1447, 1526-1530, 1636-1643, 1681-1695, 1739-1752, 1797-1810, 1872-1947, 1986-2063, 2175-2230 /usr/local/lib/python3.8/dist-packages/scipy/optimize/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py 219 182 17% 20, 23-24, 27-31, 34, 61-92, 102-146, 151-173, 177-178, 208-216, 219, 222-232, 238-242, 246-247, 264-265, 268-270, 278-280, 283-286, 304-305, 313-315, 321, 626-701, 705-707, 711-716, 720-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_constraints.py 180 157 13% 94-101, 136-139, 168-170, 173-176, 215-251, 267-274, 284-294, 304-307, 312-317, 324-411, 419-450 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentiable_functions.py 351 317 10% 31-160, 163-165, 168-170, 173-175, 178-181, 184-187, 190-193, 196-200, 223-420, 423-425, 428-429, 432-434, 437-439, 442-444, 447-449, 452-454, 458-461, 472-489, 492-494, 497-501, 504-505, 508-510, 521-528 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentialevolution.py 364 310 15% 294-308, 475-599, 607-637, 644-652, 666-687, 694, 702-704, 711, 730-849, 869-889, 894-908, 928, 948-959, 962, 965, 969-970, 974-975, 1011-1020, 1035-1144, 1150, 1154, 1158-1159, 1163-1195, 1199-1200, 1205-1206, 1211-1216, 1220-1224, 1228-1233, 1237-1242, 1249-1253, 1261-1262, 1265, 1294-1322, 1325, 1341-1346 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_dual_annealing.py 285 250 12% 55-71, 79-111, 115-127, 153-159, 166-195, 199-205, 209-210, 240-256, 259-277, 280-306, 315-355, 362-370, 373-374, 388-405, 409-425, 602-689 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_hessian_update_strategy.py 134 94 30% 52, 70, 87, 100, 136-145, 151-159, 162, 180-201, 217-220, 231-237, 279, 282-288, 311-312, 329-330, 334-375, 407-408, 412-430 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog.py 78 65 17% 78-110, 155-161, 510-581 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_ip.py 247 217 12% 84-119, 189-329, 345-349, 368-375, 399-412, 427-432, 447-453, 471-502, 534-544, 698-822, 1083-1127 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_rs.py 190 171 10% 47-99, 108-134, 161-237, 249-269, 277, 285-288, 295-310, 327-402, 522-558 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_simplex.py 107 98 8% 89-95, 154-166, 212-229, 355-435, 591-659 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_util.py 489 470 4% 57-68, 92-99, 118-121, 183-386, 497-778, 873-881, 971-1085, 1092, 1100-1130, 1138-1145, 1170-1173, 1241-1293, 1350-1396, 1473-1485 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsap.py 20 17 15% 79-105 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/bvls.py 116 109 6% 13-16, 20-177 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/common.py 295 261 12% 36-56, 108-170, 196-221, 235-247, 284-301, 316-324, 350-363, 371, 392-400, 418-439, 448-466, 499-510, 515-541, 548, 555-565, 571, 578-588, 597-600, 605-615, 620-631, 637-648, 660-671, 679-689, 697-707, 712-722, 730-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/dogbox.py 149 138 7% 65-77, 94-106, 125-149, 154-330 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/least_squares.py 255 229 10% 43-92, 98-105, 109-126, 130-149, 153-162, 169-177, 181-186, 190-195, 199-204, 212-237, 748-940 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/lsq_linear.py 82 70 15% 16-24, 218-317 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf.py 290 278 4% 121-126, 133-205, 210-402, 410-564 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf_linear.py 144 132 8% 53-69, 74-90, 95-142, 147-248 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_minimize.py 176 155 12% 479-636, 756-794, 799-806, 811-829 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_numdiff.py 254 237 7% 46-91, 100-103, 107-114, 147-175, 330-398, 404-441, 445-481, 486-561, 625-639 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_remove_redundancy.py 147 136 7% 30-31, 53-54, 83-92, 96-104, 139-230, 266-357, 393-449 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root.py 89 69 22% 153-203, 207-208, 246-257, 266-305, 369, 434, 476, 513, 550, 590, 654 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root_scalar.py 124 104 16% 30-33, 38-43, 47-49, 53-55, 58, 181-287, 306, 325, 343, 366, 392, 423, 442, 461 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo.py 657 595 9% 417-447, 456-655, 673-707, 721-737, 744-757, 762-778, 782-789, 793-795, 799-800, 803-804, 814-835, 838-845, 855-868, 871-879, 888-899, 907-910, 917-955, 974-1020, 1024-1028, 1031-1034, 1044-1052, 1070-1086, 1102-1104, 1124-1182, 1190-1199, 1203-1206, 1225-1240, 1245-1282, 1290-1292, 1302-1357, 1366-1375, 1384-1390, 1395-1403, 1407-1409, 1415-1421, 1431-1458, 1466-1472, 1476-1508, 1514-1531, 1534-1542, 1550, 1555-1568, 1574-1596, 1601-1605, 1610-1617, 1620-1628, 1631-1645, 1651-1671 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/sobol_seq.py 122 114 7% 30-40, 53-58, 97-102, 140-145, 197-372 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/triangulation.py 359 321 11% 8-46, 49, 56-85, 89-115, 119-141, 146-159, 163-174, 182, 195-225, 231-243, 258-298, 315-362, 372-451, 456-464, 467, 470-471, 477-480, 487-490, 497, 503-504, 513-517, 528-530, 536-569, 572, 575-585, 588-592, 596-600, 603-609, 616-626, 629-661 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_spectral.py 106 93 12% 66-164, 207-238, 245-251, 255, 259 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trlib/__init__.py 6 3 50% 7-12 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion.py 135 114 16% 25-35, 38, 43-45, 50-52, 57-59, 62-65, 70-72, 80-95, 98, 133-266 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/canonical_constraint.py 253 234 8% 43-48, 53-69, 78-91, 101-149, 153-181, 185-221, 225-261, 265-327, 337-390 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py 105 96 9% 14-15, 50-218 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py 175 152 13% 29-30, 33-36, 46-48, 51-57, 62-100, 107-112, 314-544 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/projections.py 164 145 12% 10, 41-55, 62-90, 96-172, 179-233, 240-287, 364-406 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/qp_subproblem.py 215 201 7% 45-63, 99-149, 189-234, 286-303, 308, 313, 364-409, 492-638 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/report.py 32 10 69% 11-16, 23-28, 32 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py 148 124 16% 38-57, 60-61, 64, 67, 80-86, 93-96, 100, 108-114, 130-136, 140, 143-163, 179-195, 199-205, 209-221, 226-240, 251-264, 287-347 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_dogleg.py 40 30 25% 31-35, 47-51, 57-62, 98-124 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_exact.py 139 124 11% 35-41, 80-122, 137-143, 173-185, 218-254, 264-285, 290-432 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_krylov.py 11 7 36% 22-59 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_ncg.py 51 42 18% 33-39, 71-128 /usr/local/lib/python3.8/dist-packages/scipy/optimize/cobyla.py 72 60 17% 139-170, 196-258 /usr/local/lib/python3.8/dist-packages/scipy/optimize/lbfgsb.py 136 123 10% 174-208, 265-380, 412-422, 440-456, 468-478 /usr/local/lib/python3.8/dist-packages/scipy/optimize/linesearch.py 320 299 7% 69-103, 150-186, 267-322, 384-468, 481-502, 512-523, 532-603, 644-659, 666-668, 685-726, 773-802, 852-883 /usr/local/lib/python3.8/dist-packages/scipy/optimize/minpack.py 284 255 10% 26-45, 139-165, 206-259, 384-457, 461-478, 482-491, 495-508, 683-813, 821-844, 848, 852, 856-870, 914-916 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nnls.py 21 16 24% 60-82 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nonlin.py 628 491 22% 139, 144-147, 152-154, 158-160, 269-367, 375-415, 434-453, 456-472, 521-530, 533, 536, 539, 542-547, 552-558, 562, 566, 573-658, 667-678, 681, 684-688, 703-708, 712-718, 723-746, 750-752, 756-758, 762-764, 768-770, 773-781, 784-790, 794-797, 803-808, 814-819, 850-884, 951-973, 977-978, 981, 984-988, 991, 994, 997, 1000-1006, 1046-1051, 1115-1121, 1124-1144, 1147-1167, 1170-1192, 1225-1226, 1229-1230, 1233, 1236, 1239, 1242, 1245, 1248, 1275-1276, 1279, 1282, 1285, 1288, 1291, 1294, 1325-1328, 1331-1332, 1335, 1338, 1341, 1344, 1347, 1350-1353, 1438-1476, 1479-1481, 1484-1491, 1494-1498, 1501-1508, 1511-1524 /usr/local/lib/python3.8/dist-packages/scipy/optimize/optimize.py 1239 1166 6% 59-61, 64-67, 70-74, 115-118, 124-129, 132, 140-145, 152, 159-164, 197-200, 229-238, 270-277, 310-317, 321-329, 435-453, 498-682, 690-707, 765, 809-813, 818-820, 841-869, 945-964, 990-1098, 1253-1271, 1297-1406, 1500-1519, 1545-1672, 1742-1750, 1771-1894, 1901-1910, 1914, 1918-1945, 1949-2053, 2056-2059, 2132-2138, 2158-2171, 2234-2239, 2253-2315, 2352-2417, 2426-2430, 2550-2570, 2599-2712, 2716-2728, 2903-2985, 2993-2994, 2998, 3069-3166, 3170-3242, 3246 /usr/local/lib/python3.8/dist-packages/scipy/optimize/slsqp.py 190 178 6% 57-66, 181-212, 237-475, 483-531 /usr/local/lib/python3.8/dist-packages/scipy/optimize/tnc.py 102 76 25% 243-277, 342-413, 420-441 /usr/local/lib/python3.8/dist-packages/scipy/optimize/zeros.py 471 417 11% 54-62, 65-68, 73-81, 86-93, 266-365, 377-469, 546-554, 643-651, 773-781, 878-886, 896-899, 904-908, 919-927, 932-937, 949-972, 980-992, 1002, 1013-1037, 1048-1064, 1067-1077, 1081-1085, 1089, 1092, 1096-1123, 1127-1132, 1139-1213, 1218-1244, 1353-1375 /usr/local/lib/python3.8/dist-packages/scipy/signal/__init__.py 44 2 95% 330, 343 /usr/local/lib/python3.8/dist-packages/scipy/signal/_arraytools.py 49 41 16% 43-46, 54, 92-106, 143-155, 196-209, 237-243 /usr/local/lib/python3.8/dist-packages/scipy/signal/_max_len_seq.py 31 25 19% 104-137 /usr/local/lib/python3.8/dist-packages/scipy/signal/_peak_finding.py 225 201 11% 66-81, 138, 194, 248-250, 264-267, 281-293, 307-319, 459-462, 584-590, 625-640, 673-678, 713-723, 935-1006, 1055-1126, 1164-1190, 1283-1299 /usr/local/lib/python3.8/dist-packages/scipy/signal/_savitzky_golay.py 81 70 14% 98-141, 153-165, 179-209, 219-223, 328-353 /usr/local/lib/python3.8/dist-packages/scipy/signal/_upfirdn.py 40 30 25% 59-63, 67-69, 75-86, 90-100, 207-210 /usr/local/lib/python3.8/dist-packages/scipy/signal/bsplines.py 202 177 12% 19, 28-43, 60-114, 125-129, 148-149, 157-167, 175-185, 189-193, 197, 202-206, 210-237, 241-252, 256-267, 292-295, 319-322, 337-358, 373-394 /usr/local/lib/python3.8/dist-packages/scipy/signal/filter_design.py 1094 1014 7% 47-56, 94-117, 180-193, 255-272, 424-477, 562-583, 662-693, 698-706, 821-828, 881-932, 987-1000, 1064-1071, 1095-1130, 1168, 1193-1200, 1230-1240, 1245-1250, 1421-1515, 1539-1561, 1592-1632, 1691-1704, 1765-1787, 1851-1876, 1938-1965, 2017-2046, 2155-2175, 2294-2386, 2393-2398, 2457-2474, 2520-2534, 2583-2600, 2652-2679, 2731-2759, 2866, 2983, 3094, 3218, 3380, 3385, 3389, 3421-3445, 3525-3615, 3693-3753, 3833-3915, 3993-4054, 4067-4074, 4091-4112, 4129-4154, 4161-4163, 4167-4179, 4201-4263, 4285-4288, 4318-4332, 4340-4357, 4371-4396, 4404-4435, 4449-4463, 4540-4568, 4648, 4728, 4764-4803 /usr/local/lib/python3.8/dist-packages/scipy/signal/fir_filter_design.py 272 250 8% 26-32, 79-85, 127-128, 250-261, 389-482, 593-684, 836-855, 968-1068, 1082-1090, 1215-1265 /usr/local/lib/python3.8/dist-packages/scipy/signal/lti_conversion.py 160 142 11% 76-114, 118-121, 125-126, 130-133, 137-139, 143-148, 176-195, 256-284, 304, 334, 405-504 /usr/local/lib/python3.8/dist-packages/scipy/signal/ltisys.py 919 757 18% 54-58, 66-70, 75, 79-82, 87, 92, 103-106, 117-120, 131-134, 206-221, 229, 236, 243, 250, 274, 284, 295, 383-398, 406-409, 414, 418, 425, 432, 439, 468, 479, 561-578, 583-592, 596, 606, 610-617, 622, 626, 638-639, 651, 663, 676, 697-702, 722-727, 800, 940-958, 963-972, 976, 987, 991-998, 1003, 1007, 1012, 1016, 1028-1030, 1042, 1055, 1067, 1131, 1207-1208, 1304-1317, 1322-1333, 1337, 1347, 1365-1404, 1411-1421, 1428, 1434-1473, 1479-1482, 1485-1488, 1491-1494, 1501-1508, 1513, 1517, 1522, 1526-1527, 1532, 1536-1537, 1542, 1546, 1558-1561, 1578, 1596, 1609, 1677, 1799-1853, 1861-1867, 1926-2032, 2058-2064, 2114-2133, 2200-2222, 2277-2292, 2358-2373, 2432-2437, 2494-2522, 2529, 2540-2580, 2590-2600, 2613-2636, 2655-2710, 2720-2763, 2777-2886, 2896-2912, 3095-3263, 3322-3379, 3431-3465, 3516-3550, 3615-3648, 3712-3722 /usr/local/lib/python3.8/dist-packages/scipy/signal/signaltools.py 1148 1072 7% 27, 47-50, 55-58, 77-93, 197-259, 265-270, 305-331, 365-391, 419-428, 523-544, 572-655, 736-860, 876-881, 889-892, 904-935, 963-977, 984-985, 996-1002, 1019-1037, 1146-1174, 1267-1296, 1353-1359, 1394-1409, 1437-1460, 1537-1549, 1627-1644, 1681-1692, 1817-1885, 1928-1953, 1997-2009, 2098-2120, 2145-2180, 2208-2210, 2265-2297, 2356-2372, 2377-2397, 2401-2424, 2503-2539, 2598-2644, 2648-2675, 2733-2749, 2832-2924, 3054-3134, 3184-3212, 3254-3297, 3391-3433, 3489-3505, 3557-3684, 3844-3885, 3890-3920, 3924-3927, 4000-4039, 4130-4151, 4202-4245 /usr/local/lib/python3.8/dist-packages/scipy/signal/spectral.py 363 339 7% 142-158, 268-289, 452-457, 584-601, 734-771, 870-895, 996-1022, 1172-1178, 1348-1456, 1566-1576, 1669-1870, 1896-1920, 1959-1981, 2001-2002 /usr/local/lib/python3.8/dist-packages/scipy/signal/waveforms.py 120 107 11% 58-88, 139-162, 224-262, 427-430, 440-483, 577-580, 591-593, 669-681 /usr/local/lib/python3.8/dist-packages/scipy/signal/wavelets.py 136 123 10% 29-76, 90-92, 127-198, 253-261, 301-308, 384-388, 462-473 /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/windows.py 289 243 16% 21-23, 28-31, 36-39, 112-121, 168-174, 226-238, 290-302, 349-357, 442, 501, 548, 609-610, 700-708, 790, 795, 856-878, 925-933, 1020, 1098, 1207-1216, 1271-1279, 1342-1349, 1438-1476, 1545-1563, 1616-1622, 1698-1710, 1876-1970, 1975-1982, 2095-2124 /usr/local/lib/python3.8/dist-packages/scipy/sparse/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/_index.py 221 190 14% 24-27, 35-75, 78-126, 129-150, 157-180, 185-191, 196-202, 205, 208, 211, 214, 217, 220, 223, 226, 229, 232, 235, 238, 242-244, 252-283, 288-322, 326-328 /usr/local/lib/python3.8/dist-packages/scipy/sparse/_matrix_io.py 42 32 24% 16, 63-80, 131-156 /usr/local/lib/python3.8/dist-packages/scipy/sparse/base.py 455 344 24% 71-75, 81-82, 86, 123-131, 157, 182-189, 194-203, 207-208, 212, 223, 239, 250, 254, 257-258, 263-281, 284-287, 295, 313-328, 340, 344, 348, 363, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 407, 410-424, 427, 430-443, 446-455, 466-530, 534, 537, 540, 543, 546-554, 561-564, 567-570, 577-617, 620, 624, 628, 632, 635, 638, 641, 644, 647, 650, 653-675, 678-691, 718, 736-741, 744, 756, 759, 762, 780-782, 791-799, 808-816, 851, 883, 894, 902, 910, 918, 926, 937, 945, 953, 993-1025, 1064-1097, 1124, 1146-1149, 1152-1176, 1179-1189 /usr/local/lib/python3.8/dist-packages/scipy/sparse/bsr.py 315 266 16% 123-214, 225-271, 279, 283-287, 292-293, 299-308, 317, 320, 330, 336, 339, 342-351, 354-364, 367-421, 436-441, 444-463, 468, 479-505, 508, 513-534, 546-561, 568-594, 599-607, 613-627, 635-676, 684-688, 722 /usr/local/lib/python3.8/dist-packages/scipy/sparse/compressed.py 737 654 11% 31-108, 111-123, 130-136, 148-195, 212-215, 219-248, 252-279, 283-313, 316, 322, 328, 334, 344-352, 355, 358, 365-458, 465-475, 478-489, 492-528, 531-539, 548-568, 571, 577, 592-611, 630-635, 642-647, 650-653, 657-668, 672-673, 678-698, 704-730, 736-758, 764-775, 782-793, 797-798, 801-802, 806-823, 826-853, 856-872, 880-911, 918-931, 942-1003, 1010-1017, 1023-1037, 1050-1053, 1068-1074, 1077-1079, 1089-1098, 1110-1113, 1116, 1123-1125, 1135-1138, 1143-1153, 1156-1187, 1201-1207, 1212-1242, 1248-1269, 1273-1290 /usr/local/lib/python3.8/dist-packages/scipy/sparse/construct.py 232 206 11% 62, 138-188, 218, 252-273, 311-355, 384-398, 406-431, 465, 499, 545-623, 668-677, 750-793, 842 /usr/local/lib/python3.8/dist-packages/scipy/sparse/coo.py 294 251 15% 129-198, 201-236, 242-263, 271-291, 294-300, 306-317, 323-330, 352-372, 394-414, 417-420, 425-443, 448-454, 459-475, 480-513, 521-525, 533-537, 541-554, 561-564, 571-579, 583-586, 589-593, 619 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csc.py 87 58 33% 111-119, 125-126, 129-132, 137-155, 164-180, 188-194, 200-206, 209, 212-214, 217-219, 222, 225, 228, 235, 261 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_laplacian.py 51 44 14% 69-81, 85, 89-111, 115-128 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_validation.py 31 25 19% 17-58 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csr.py 135 103 24% 129-137, 143-156, 161-164, 169-186, 191-222, 231, 234-242, 248-256, 263-271, 275, 278-307, 311-313, 316, 319, 322-325, 351 /usr/local/lib/python3.8/dist-packages/scipy/sparse/data.py 184 138 25% 23, 26, 29, 33-35, 38, 41, 44, 47, 50-53, 56-60, 63-68, 71-79, 84-89, 94, 99, 113-119, 126, 135-136, 148-156, 166-187, 191-215, 218-252, 255-289, 321, 353, 376, 399 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dia.py 224 188 16% 79-146, 149-150, 158-164, 167-168, 171-181, 187-225, 230-241, 244, 247-276, 279-282, 287-305, 311-318, 323-343, 349-365, 374-377, 380-392, 420 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dok.py 275 213 23% 23, 79-111, 115, 122, 125-128, 133-136, 139, 145, 151-158, 161, 164, 167, 170-191, 194, 197, 200-201, 204-205, 209-216, 220-227, 230-234, 237-246, 249-277, 280-301, 304-309, 312-316, 320-323, 327-332, 335-338, 341-346, 349-352, 358, 365-374, 380-384, 387-389, 394-404, 409-411, 416, 421-429, 457 /usr/local/lib/python3.8/dist-packages/scipy/sparse/extract.py 22 14 36% 38-42, 101-103, 162-164, 168-171 /usr/local/lib/python3.8/dist-packages/scipy/sparse/lil.py 291 232 20% 89-132, 135-136, 139-140, 143-147, 150-154, 160-172, 175, 181-185, 190-193, 198-206, 210-216, 220-226, 229-231, 234-235, 238, 241, 244-245, 248, 251-252, 255-256, 260-261, 265-271, 289-300, 303, 307-308, 314-324, 328-336, 339-350, 353-360, 363-369, 374-401, 406-426, 431-435, 440, 445-448, 454-484, 512-527, 553 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_expm_multiply.py 255 225 12% 18-23, 28-33, 38-43, 48-55, 140-144, 172-197, 204-223, 313, 342-346, 352, 358-360, 366-369, 375, 399, 414-417, 455-475, 506-511, 556-629, 639-648, 655-677, 684-713 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_norm.py 70 63 10% 15-19, 110-184 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_onenormest.py 199 177 11% 86-119, 130-139, 154-157, 162, 166-174, 178-180, 187-190, 194-197, 204-211, 215, 219, 261-322, 366-468 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/_add_newdocs.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py 197 173 12% 56-59, 63-82, 132-233, 302-324, 386-410, 442-469, 528-607 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py 726 642 12% 280-281, 297-299, 307, 313-364, 367-377, 435-533, 536-573, 576-595, 636-719, 722-759, 762-896, 900-904, 914-917, 922-927, 937-939, 942, 949-951, 961-974, 977-982, 992-1020, 1023-1028, 1032, 1037-1044, 1048-1054, 1058-1089, 1251-1349, 1554-1689, 1694-1713, 1717, 1721, 1804-1910 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py 323 310 4% 34-44, 52-57, 64-72, 77-79, 84-114, 119-125, 287-711 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/interface.py 346 245 29% 140-152, 160-168, 173-175, 184, 196, 222-243, 269-290, 294-298, 324-339, 364-377, 381-384, 387, 390, 407-419, 423-426, 429-432, 435-438, 441-444, 447-450, 453, 456, 459-465, 481, 491, 497, 501, 509-518, 521-524, 527, 530-533, 536-539, 542, 553-556, 559, 562, 565, 568, 573-576, 580, 583, 587, 590, 593-598, 603-610, 613, 616, 619, 622, 625-626, 631-639, 642, 645, 648, 651, 654-655, 660-666, 669, 672, 675, 678, 681-682, 687-695, 698-701, 704, 707, 710, 713, 716-717, 722-725, 728, 731-733, 737-740, 744, 747, 752, 755, 758, 761, 764, 767, 795-823 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/_gcrotmk.py 192 182 5% 66-182, 267-490 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/iterative.py 421 386 8% 73-77, 97-118, 137-198, 209-265, 276-337, 347-414, 514-647, 717-802 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lgmres.py 69 59 14% 128-235 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsmr.py 185 177 4% 197-482 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsqr.py 200 192 4% 81-95, 311-570 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/minres.py 203 196 3% 71-343, 347-363 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/utils.py 56 46 18% 23-27, 31, 65-123 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/matfuncs.py 354 279 21% 77-82, 103-115, 120-130, 155-173, 179-188, 191-193, 196-200, 203-205, 209, 218-235, 238-240, 243-246, 249-251, 255-256, 296, 338, 367-383, 387-390, 394-397, 401-404, 408-411, 415-418, 422-424, 428-430, 434-436, 440-442, 446-454, 458-466, 470-478, 482-490, 493-498, 501-506, 509-514, 517-525, 528-547, 595, 603-677, 700-709, 736-740, 762-764, 783-813, 833-855 /usr/local/lib/python3.8/dist-packages/scipy/sparse/sputils.py 176 146 17% 42-53, 58-63, 70, 80-90, 94, 105-118, 143-171, 176-180, 185, 194-207, 215-225, 229, 235, 241, 245-264, 269-314, 326-331, 338-339, 346-349, 353-356, 360-363 /usr/local/lib/python3.8/dist-packages/scipy/spatial/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/_plotutils.py 78 65 17% 11-27, 31-35, 81-90, 136-148, 212-264 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_procrustes.py 25 20 20% 101-132 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_spherical_voronoi.py 68 57 16% 133-166, 171-206, 217-243, 273-277 /usr/local/lib/python3.8/dist-packages/scipy/spatial/distance.py 633 503 21% 135-165, 170-172, 176-178, 182-183, 194-195, 200-220, 224-240, 244-259, 263-269, 273-286, 290-292, 307-309, 314-329, 334-339, 343-346, 350-357, 451-457, 508-524, 580-581, 620, 661-674, 708-721, 766, 813-820, 883-894, 941-950, 985-991, 1031-1037, 1079-1084, 1122-1130, 1170-1178, 1221-1236, 1286-1296, 1337-1342, 1353, 1397-1412, 1457-1462, 1507-1519, 1565-1578, 1624-1638, 1718, 1722, 1724-1731, 1999, 2006-2012, 2017, 2023, 2027, 2031, 2039-2050, 2069-2094, 2149-2212, 2254-2302, 2334-2339, 2344-2358, 2378-2380, 2403, 2407, 2708-2793 /usr/local/lib/python3.8/dist-packages/scipy/spatial/kdtree.py 419 379 10% 39-55, 78-83, 93-95, 98, 102, 120-126, 140, 154, 168, 182, 243-251, 256, 259, 262, 265, 268, 272-273, 277-281, 284-323, 329-407, 492-547, 550-572, 625-636, 663-705, 731-812, 842-889, 912-942, 978-996 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_groups.py 56 48 14% 6-58, 62-76, 80-90, 94-99, 103-105, 109-140 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_spline.py 176 159 10% 18-25, 30, 48-65, 83-104, 124-151, 168, 188, 220-248, 331-361, 364-404, 424-456 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/rotation.py 470 416 11% 15-17, 29-142, 146-150, 154-158, 162-173, 369-394, 407, 474-479, 568-612, 618, 674-707, 803-860, 907-910, 964-999, 1004, 1052-1073, 1159-1179, 1298-1329, 1397-1406, 1439-1443, 1467-1475, 1510-1527, 1560-1617, 1655, 1703, 1722-1727, 1768-1775, 1783-1838, 1915-1968, 2049-2070, 2090-2114 /usr/local/lib/python3.8/dist-packages/scipy/special/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/_basic.py 524 449 14% 100-112, 179-213, 250-254, 279-285, 305, 325, 345, 365, 397-401, 433-437, 469-473, 481-487, 515-519, 547-551, 602-606, 635-639, 667-671, 699-703, 747-755, 799-807, 888, 926-928, 941-943, 956-958, 971-973, 988, 1032-1034, 1072-1093, 1129-1150, 1201-1226, 1284-1307, 1343-1360, 1373-1380, 1424-1431, 1449-1460, 1476-1487, 1535-1538, 1586-1589, 1624-1639, 1666-1676, 1703-1713, 1740-1749, 1762-1764, 1777-1779, 1792-1794, 1800-1802, 1815-1817, 1830-1832, 1845-1847, 1860-1862, 1878-1880, 1904-1911, 1928-1935, 1984-1996, 2035-2050, 2065-2070, 2120-2158, 2195-2215, 2260-2270, 2333-2336 /usr/local/lib/python3.8/dist-packages/scipy/special/_ellip_harm.py 16 6 62% 97, 155-156, 160, 208-209 /usr/local/lib/python3.8/dist-packages/scipy/special/_logsumexp.py 34 12 65% 96-99, 105-106, 109-110, 118-119, 123, 127 /usr/local/lib/python3.8/dist-packages/scipy/special/_spherical_bessel.py 18 12 33% 53-56, 104-107, 153-156, 202-205 /usr/local/lib/python3.8/dist-packages/scipy/special/lambertw.py 4 1 75% 107 /usr/local/lib/python3.8/dist-packages/scipy/special/orthogonal.py 525 464 12% 128-151, 154-157, 160-173, 191-216, 264-290, 331-346, 394-404, 442-457, 503-524, 569-586, 630, 662-676, 744-760, 789-796, 825-831, 860-871, 898-910, 947-1020, 1050-1067, 1113-1124, 1157-1172, 1229-1248, 1282-1297, 1345-1364, 1400-1408, 1455-1463, 1499-1512, 1557-1566, 1603-1608, 1652-1659, 1696-1714, 1758-1765, 1802-1821, 1865-1866, 1894-1902, 1945-1952, 1980-1985, 2029-2039, 2080-2094, 2137-2143, 2170-2182 /usr/local/lib/python3.8/dist-packages/scipy/special/sf_error.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/spfun_stats.py 14 8 43% 86-95 /usr/local/lib/python3.8/dist-packages/scipy/stats/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_binned_statistic.py 159 145 9% 167-182, 336-349, 514-634, 640-675, 681-706 /usr/local/lib/python3.8/dist-packages/scipy/stats/_constants.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_continuous_distns.py 2994 1970 34% 35-36, 50-54, 97, 100, 103, 106, 109, 152, 155, 158, 161, 164, 180, 184, 188, 192, 196, 200, 204, 208, 235, 239, 242, 245, 248, 251, 254, 257, 260, 263, 266, 273-300, 342, 345, 348, 351, 354, 382, 385, 388, 391, 394, 422, 425, 428, 431-435, 438, 449, 461-463, 470-472, 482-486, 514, 520, 523-525, 528, 531, 534-539, 542-553, 563-658, 690-693, 697, 700, 703, 706-724, 754, 757, 760, 763-775, 778-779, 834-840, 843-852, 855, 858, 861, 864, 867, 870-887, 890-894, 948, 951, 954, 957, 960, 963, 969, 972-973, 1011, 1014, 1017, 1021, 1024, 1027, 1030, 1033, 1036, 1039, 1068, 1071, 1074, 1077, 1080, 1083, 1086, 1090-1091, 1130-1131, 1137, 1140-1141, 1144, 1147, 1150-1155, 1187, 1191, 1194, 1197, 1200, 1203, 1206, 1209-1213, 1242, 1245, 1248, 1251, 1281-1284, 1288-1289, 1292-1293, 1296-1297, 1300-1301, 1304-1305, 1308-1309, 1338-1341, 1345-1347, 1350-1351, 1354-1355, 1358-1360, 1363, 1369, 1401, 1405, 1408, 1411, 1414, 1417, 1420, 1423, 1426, 1429, 1436-1473, 1518-1520, 1525-1530, 1533-1535, 1538-1540, 1543-1545, 1548-1552, 1602, 1605-1609, 1612-1613, 1616, 1652, 1655-1657, 1660, 1663, 1666, 1669, 1705-1709, 1714, 1717, 1721, 1724-1725, 1733-1739, 1766, 1771, 1774, 1777, 1808, 1814, 1817-1821, 1824, 1827, 1830, 1833-1860, 1897, 1900, 1904, 1907, 1913-1926, 1972, 1975, 1978, 1981, 1984, 1987, 1990, 1993, 2027, 2030, 2033, 2036, 2039, 2042, 2045-2050, 2053, 2089, 2093, 2097, 2101, 2105, 2109, 2113, 2117, 2121, 2125, 2129, 2133, 2137, 2141, 2145, 2149, 2153, 2157, 2161, 2165, 2169, 2173, 2177, 2209, 2213, 2217, 2221, 2225, 2229, 2233, 2237, 2241, 2245, 2249, 2253, 2257, 2261, 2265, 2269, 2273, 2277, 2281, 2285, 2289, 2293, 2297, 2328, 2331, 2334-2335, 2338-2339, 2342-2348, 2391, 2394-2399, 2403, 2406, 2411, 2414, 2417, 2422, 2425, 2428-2434, 2439, 2479, 2483, 2486, 2526, 2529-2531, 2534, 2541, 2544-2554, 2557, 2560, 2563, 2566-2567, 2571-2572, 2576-2605, 2609-2614, 2617-2621, 2624, 2643-2663, 2703, 2707, 2710, 2713, 2716, 2719, 2722, 2725, 2733-2734, 2742-2813, 2840-2848, 2854-2855, 2860, 2863, 2903, 2907, 2910, 2913-2916, 2919-2922, 2925-2927, 2930-2932, 2936, 2939-2940, 2969, 2972, 2977-2981, 2984-2987, 2990, 2993, 3023, 3026, 3029, 3032, 3035, 3069, 3072, 3075, 3078, 3081, 3084, 3088, 3122, 3125, 3128, 3131, 3134, 3137, 3140, 3143, 3147, 3175, 3178, 3181, 3184, 3187, 3190, 3220, 3223, 3226, 3229, 3232-3240, 3243, 3272, 3276, 3279, 3282, 3285, 3288, 3294, 3322, 3325, 3328, 3331, 3334, 3367, 3372-3373, 3376-3379, 3414, 3417, 3420, 3423, 3426, 3429, 3432-3445, 3448, 3485, 3490, 3493, 3496-3500, 3503, 3547, 3553-3561, 3568, 3572, 3579-3629, 3634-3781, 3785-3788, 3791-3803, 3853, 3856-3859, 3864-3867, 3870-3875, 3915-3918, 3921-3922, 3925, 3928, 3931, 3967, 3971-3972, 3975, 3978, 4012, 4017-4019, 4022, 4025, 4052, 4056, 4059, 4062, 4065, 4068, 4105, 4109, 4113-4114, 4117, 4153-4154, 4157-4158, 4161-4162, 4165, 4256-4292, 4295, 4299-4300, 4307-4311, 4315-4318, 4322-4323, 4329-4385, 4391-4435, 4439-4481, 4485-4521, 4528-4638, 4641-4645, 4673, 4677, 4680, 4683, 4686, 4689, 4692, 4695, 4699, 4730, 4734, 4737, 4740, 4745-4749, 4787-4789, 4792, 4795, 4798, 4801, 4808, 4845, 4849, 4852, 4855, 4858, 4861, 4864, 4867, 4870-4875, 4878, 4886-4944, 4973, 4977, 4980, 4983, 4986, 4989-4994, 4997, 5031, 5035, 5038, 5041, 5044-5045, 5051, 5092, 5095, 5098, 5101, 5104-5105, 5108-5112, 5208, 5211-5249, 5254, 5257-5289, 5295, 5298-5327, 5333-5352, 5358-5368, 5408, 5412, 5415, 5418, 5421-5422, 5471-5473, 5476, 5479, 5482, 5485, 5488-5492, 5495-5513, 5544, 5547, 5550, 5553-5558, 5592, 5595, 5598-5599, 5604-5605, 5608-5609, 5612-5613, 5616-5617, 5660, 5669-5676, 5681, 5684, 5687-5691, 5697-5707, 5739, 5742, 5748-5751, 5754-5757, 5760, 5763, 5766, 5769, 5772-5782, 5814, 5817-5820, 5823-5837, 5840, 5843, 5852-5876, 5906, 5909, 5912, 5915, 5918-5942, 5945, 5977, 5980, 5983, 5986, 5989, 5992, 5995-5996, 5999, 6053-6074, 6081, 6084-6090, 6097-6103, 6111-6116, 6119-6124, 6127-6139, 6142-6146, 6178, 6181, 6184, 6187, 6190, 6193, 6199, 6234, 6238, 6241, 6272, 6275, 6278, 6281, 6323, 6326, 6329, 6332, 6335, 6338-6339, 6370, 6374, 6377, 6380, 6383, 6386, 6389, 6392, 6395-6396, 6402, 6458, 6461, 6465, 6468, 6471, 6474, 6477, 6480, 6517, 6521-6523, 6526, 6529, 6538, 6541-6544, 6579, 6582, 6585-6588, 6591, 6629, 6632, 6635, 6638, 6643-6645, 6648, 6651, 6684, 6687, 6690-6700, 6703, 6706-6710, 6713-6725, 6754, 6757-6759, 6768, 6778-6783, 6812, 6815, 6823-6832, 6835-6844, 6847, 6850, 6856, 6885, 6888, 6892, 6895, 6898, 6901, 6906-6913, 6916-6917, 6933, 6939-6946, 6950-6967, 6972-6973, 6978-6981, 6986-7004, 7010-7013, 7018-7037, 7042-7045, 7050-7063, 7069-7072, 7077-7114, 7139, 7142, 7145, 7148, 7151, 7154, 7157, 7160, 7163, 7166-7185, 7189-7213, 7244, 7247-7250, 7253, 7256, 7259, 7262-7264, 7272, 7293, 7296, 7299, 7302, 7305, 7308, 7383-7455, 7493, 7497, 7500, 7503, 7506, 7538, 7542, 7545, 7548, 7551, 7580, 7584, 7587-7604, 7607-7610, 7613, 7655, 7658, 7661-7663, 7666-7668, 7671, 7674, 7677-7678, 7681, 7728, 7731, 7734, 7737, 7740, 7743, 7746, 7799-7809, 7815-7824, 7830-7841, 7844-7861, 7867-7885, 7893, 7904, 7952-7953, 7959, 7965, 8053-8070, 8076, 8082, 8088, 8092-8093, 8097-8101, 8107-8109 /usr/local/lib/python3.8/dist-packages/scipy/stats/_discrete_distns.py 446 296 34% 43, 46, 49, 52-54, 58, 61-63, 66-67, 70-73, 76-84, 87-89, 119, 122, 126, 129, 134, 137, 140, 143, 146, 149, 193-194, 197, 200, 203-205, 208, 211-230, 263, 266, 270, 273-274, 277-278, 282-283, 286-289, 292-298, 331, 334, 337, 340, 343-344, 347, 350-351, 354-356, 359-364, 433, 436, 439-441, 444-449, 454, 459-471, 474-476, 483-489, 492-501, 504-513, 545, 548, 552, 555-567, 598, 601, 604-605, 609, 612-613, 616-617, 620-623, 626-631, 666, 669, 672-673, 676, 679-680, 683-686, 690-691, 694-698, 701-702, 731, 734, 739-740, 743-744, 747-751, 754-764, 794, 797, 801-802, 805-806, 809-812, 815-821, 825-836, 839, 871, 874, 878-879, 882, 915, 918-921, 924-929, 932-935, 938, 976-977, 981-985, 988-992, 995-999, 1040-1043, 1046, 1049, 1052, 1055, 1058, 1061, 1064-1077 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distn_infrastructure.py 1393 1036 26% 42, 361-363, 367-395, 402-406, 413-417, 424-432, 436, 440, 443, 446, 449, 452, 455, 458, 461-463, 466, 469, 472-474, 477, 480, 483, 486, 489, 492, 495, 498, 501, 508-512, 516, 541-545, 567-571, 575, 579, 607, 611, 614, 617-620, 640-649, 665, 668, 671, 679, 733-734, 742-751, 768, 771, 778, 785-788, 801-859, 871-874, 895, 898-899, 902-903, 912-914, 917-918, 921, 924-925, 928, 931, 961-995, 1024-1110, 1135-1146, 1165-1192, 1219, 1241-1245, 1267-1271, 1293-1295, 1321-1328, 1349-1351, 1362-1368, 1612, 1621, 1629, 1643-1651, 1654, 1657-1678, 1683, 1686-1687, 1692, 1695, 1698, 1702, 1705-1706, 1709, 1736-1752, 1778-1795, 1819-1837, 1861-1880, 1904-1922, 1949-1968, 1992-2014, 2038-2060, 2063, 2066-2072, 2082-2089, 2092-2102, 2109-2114, 2118-2121, 2132-2176, 2276-2314, 2336-2380, 2402-2412, 2415-2438, 2516-2540, 2546-2550, 2554-2609, 2663-2676, 2822, 2851, 2878, 2885, 2893, 2912-2921, 2924, 2927, 2930, 2933-2935, 2938-2939, 2968-2969, 2991-3006, 3028-3044, 3066-3084, 3106-3125, 3147-3164, 3189-3207, 3229-3248, 3270-3294, 3297-3301, 3356-3386, 3394-3428, 3449-3463, 3475-3517, 3532, 3535, 3539-3541, 3544-3546, 3551-3557, 3560, 3563-3564, 3593-3602 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distr_params.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_hypotests.py 43 36 16% 80-132 /usr/local/lib/python3.8/dist-packages/scipy/stats/_multivariate.py 972 690 29% 50-53, 81-88, 108, 156-173, 177-179, 203, 207, 210-213, 223, 227, 361, 373-426, 434-444, 469-471, 493-497, 519-523, 550-555, 587-594, 626-633, 657-661, 681-683, 733-741, 744-747, 750, 753, 756-759, 762, 774-776, 938, 948-1005, 1013-1019, 1050-1055, 1078-1085, 1107, 1131-1142, 1175-1179, 1182-1186, 1189, 1192, 1227-1233, 1237-1277, 1299, 1412, 1429-1430, 1448-1452, 1470-1474, 1490-1493, 1510-1514, 1531-1539, 1559-1561, 1569-1570, 1573, 1576, 1579, 1582, 1585, 1588, 1739, 1742-1768, 1775-1805, 1808-1819, 1851-1864, 1887-1894, 1917, 1933, 1948-1950, 1966-1970, 1988-1990, 2006-2010, 2025-2027, 2051-2072, 2098-2118, 2142-2150, 2169, 2197-2199, 2223-2225, 2250-2253, 2256-2260, 2263, 2266-2267, 2270-2271, 2274-2275, 2278-2281, 2284, 2325-2357, 2471, 2495-2515, 2538-2542, 2566, 2582-2586, 2605-2607, 2623, 2639-2641, 2657-2664, 2682-2684, 2708-2733, 2757-2769, 2773, 2798-2812, 2815-2818, 2821, 2824-2825, 2828-2829, 2832-2833, 2836-2841, 2845, 3002, 3011-3023, 3032-3047, 3050-3058, 3061, 3082-3094, 3115, 3130-3132, 3147-3156, 3182-3196, 3218-3220, 3244-3251, 3254, 3257, 3260, 3263, 3266, 3269, 3348, 3355-3359, 3378-3401, 3430-3431, 3434, 3495-3499, 3518-3538, 3601-3615, 3631-3648, 3657-3689, 3718-3730, 3787-3791, 3810-3824 /usr/local/lib/python3.8/dist-packages/scipy/stats/_rvs_sampling.py 33 28 15% 123-169 /usr/local/lib/python3.8/dist-packages/scipy/stats/_stats_mstats_common.py 107 98 8% 93-144, 232-266, 271-285, 374-404 /usr/local/lib/python3.8/dist-packages/scipy/stats/_tukeylambda_stats.py 54 40 26% 68-102, 164-201 /usr/local/lib/python3.8/dist-packages/scipy/stats/contingency.py 36 28 22% 57-62, 99-109, 243-275 /usr/local/lib/python3.8/dist-packages/scipy/stats/distributions.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/kde.py 183 151 17% 194-209, 231-265, 293-320, 344-355, 375-388, 411-438, 465-475, 485, 495, 548-565, 571-581, 593, 600-632, 636-640, 644-648 /usr/local/lib/python3.8/dist-packages/scipy/stats/morestats.py 829 760 8% 127-136, 194-211, 278-306, 349-358, 416-421, 440-452, 457-471, 585-627, 705-718, 797-812, 894-910, 916-945, 1034-1059, 1129-1165, 1173-1202, 1270, 1345-1363, 1370-1385, 1470-1481, 1533-1536, 1606, 1661-1678, 1771-1825, 1853-1869, 1897-1906, 2014-2070, 2113-2166, 2225-2247, 2307-2371, 2421-2465, 2472-2475, 2548-2600, 2670-2725, 2847-2938, 3080-3162, 3167-3186, 3225-3255, 3295-3307, 3349-3361 /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats_basic.py 947 845 11% 61-67, 71-79, 83-89, 116-128, 151-160, 196-211, 235-259, 299-324, 328-330, 335, 373-399, 459-528, 569-663, 676-727, 759-782, 798-828, 870-884, 924-938, 942-949, 982-996, 1037-1066, 1096-1114, 1144-1165, 1214-1229, 1258-1282, 1322-1343, 1372-1410, 1465-1468, 1500, 1535-1543, 1556-1561, 1579-1586, 1604-1610, 1646-1690, 1718-1736, 1786, 1825-1831, 1876-1878, 1927-1929, 1967-1974, 2030-2071, 2101-2135, 2176-2177, 2205-2222, 2262-2283, 2343-2351, 2367-2381, 2411-2430, 2460-2490, 2521-2526, 2628-2654, 2664-2668, 2714-2720, 2736-2749, 2798-2801, 2824-2837, 2867-2888, 2931-2977 /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats_extras.py 159 141 11% 62-103, 128-129, 156-190, 235-241, 260-286, 316-320, 346-371, 400-404, 429-444, 463-477 /usr/local/lib/python3.8/dist-packages/scipy/stats/stats.py 1635 1486 9% 172, 217-227, 231-245, 249-274, 328-339, 391-406, 459-503, 531-549, 594-599, 648-654, 706-717, 768-779, 828, 877-883, 950-970, 974-1021, 1064-1072, 1149-1175, 1259-1288, 1357-1374, 1437-1463, 1520-1560, 1625-1637, 1681-1693, 1738-1740, 1805-1820, 1825-1864, 1934-1954, 2008-2036, 2116-2118, 2195-2199, 2258-2281, 2336-2346, 2408-2419, 2465-2468, 2567-2594, 2727-2764, 2858-2882, 2893-2942, 2961-2986, 3051-3062, 3105-3124, 3156-3177, 3228-3247, 3323-3353, 3360-3363, 3370-3373, 3496-3557, 3624-3719, 3821-3877, 3967-3968, 4059-4169, 4307-4347, 4357-4360, 4363-4369, 4412-4430, 4434, 4640-4713, 4743-4773, 4792-4817, 4842-4869, 4891-4895, 4966-4985, 4990-4994, 4999-5004, 5008-5017, 5021-5024, 5131-5138, 5237-5266, 5333-5367, 5487-5528, 5549-5560, 5721-5763, 5877, 5916-5962, 5989-5998, 6047-6084, 6183-6282, 6323-6328, 6387-6429, 6467-6478, 6550-6601, 6644-6670, 6754-6815, 6890-6927, 7008, 7090, 7138-7177, 7202-7221, 7264, 7290-7291, 7316-7321, 7377-7407 /usr/local/lib/python3.8/dist-packages/scipy/version.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/skimage/__init__.py 28 9 68% 93-100, 114, 121-122 /usr/local/lib/python3.8/dist-packages/skimage/_shared/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/skimage/_shared/_geometry.py 14 10 29% 27-37, 53-55 /usr/local/lib/python3.8/dist-packages/skimage/_shared/_warnings.py 54 42 22% 46-70, 109-145 /usr/local/lib/python3.8/dist-packages/skimage/_shared/fft.py 9 4 56% 12-15 /usr/local/lib/python3.8/dist-packages/skimage/_shared/utils.py 123 45 63% 63-66, 99, 104-114, 154-163, 179, 235-236, 242-243, 251-253, 270-278, 298-304, 333-334, 362, 365, 369 /usr/local/lib/python3.8/dist-packages/skimage/_shared/version_requirements.py 60 51 15% 6, 10-12, 48-62, 67-69, 95-117, 141-155, 177-179 /usr/local/lib/python3.8/dist-packages/skimage/color/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/skimage/color/adapt_rgb.py 25 11 56% 19, 37-40, 58-62, 77-79 /usr/local/lib/python3.8/dist-packages/skimage/color/colorconv.py 355 254 28% 79-82, 124-140, 147-153, 188-212, 249-294, 331-348, 486-490, 596-598, 638-643, 681-685, 717, 750, 793-811, 816-818, 840-855, 880-920, 925-927, 974-993, 1034-1056, 1092, 1128, 1176-1213, 1252-1280, 1311, 1336, 1371, 1407, 1459-1462, 1516-1521, 1559-1563, 1571-1573, 1607-1611, 1620-1624, 1654, 1675, 1700, 1730-1734, 1764-1765, 1790, 1811, 1836, 1866-1870, 1900 /usr/local/lib/python3.8/dist-packages/skimage/color/colorlabel.py 82 67 18% 33-36, 46-71, 112-118, 152-204, 226-237 /usr/local/lib/python3.8/dist-packages/skimage/color/delta_e.py 100 93 7% 47-51, 105-119, 161-244, 286-308, 329-340 /usr/local/lib/python3.8/dist-packages/skimage/color/rgb_colors.py 146 0 100% /usr/local/lib/python3.8/dist-packages/skimage/data/__init__.py 114 53 54% 98, 143-164, 196-204, 219, 241-244, 264-265, 278, 300, 366, 414, 426-429, 484, 503, 518, 549, 574, 585, 613, 627, 641, 657, 674, 694, 712, 731, 755, 782, 799, 810, 833, 890-892, 926 /usr/local/lib/python3.8/dist-packages/skimage/data/_binary_blobs.py 12 10 17% 47-57 /usr/local/lib/python3.8/dist-packages/skimage/data/_registry.py 4 0 100% /usr/local/lib/python3.8/dist-packages/skimage/draw/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/skimage/draw/_polygon2mask.py 9 6 33% 35-41 /usr/local/lib/python3.8/dist-packages/skimage/draw/_random_shapes.py 119 107 10% 40-62, 96-119, 153-172, 208-241, 283-289, 378-434 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw.py 112 92 18% 35-43, 114-143, 177-181, 225-226, 277-305, 345-369, 411, 453, 500, 563, 624, 695, 752, 840-848, 912-921, 936-949 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw3d.py 35 31 11% 33-63, 88-114 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw_nd.py 19 16 16% 45-51, 97-108 /usr/local/lib/python3.8/dist-packages/skimage/exposure/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/skimage/exposure/_adapthist.py 106 95 10% 78-98, 126-233, 256-287, 312-317 /usr/local/lib/python3.8/dist-packages/skimage/exposure/exposure.py 138 117 15% 23-35, 61-74, 122-144, 181-184, 217-223, 254-266, 292-307, 405-428, 433-434, 485-494, 528-537, 577-586, 629-640 /usr/local/lib/python3.8/dist-packages/skimage/exposure/histogram_matching.py 20 17 15% 9-19, 53-70 /usr/local/lib/python3.8/dist-packages/skimage/feature/__init__.py 25 4 84% 35-36, 46-47 /usr/local/lib/python3.8/dist-packages/skimage/feature/_canny.py 108 100 7% 45-50, 157-297 /usr/local/lib/python3.8/dist-packages/skimage/feature/_daisy.py 97 90 7% 97-222 /usr/local/lib/python3.8/dist-packages/skimage/feature/_hog.py 82 77 6% 6-19, 34-43, 142-295 /usr/local/lib/python3.8/dist-packages/skimage/feature/_orb_descriptor_positions.py 6 0 100% /usr/local/lib/python3.8/dist-packages/skimage/feature/blob.py 148 130 12% 38-52, 79-81, 111-141, 168-185, 192-210, 312-373, 468-533, 618-644 /usr/local/lib/python3.8/dist-packages/skimage/feature/brief.py 41 33 20% 119-130, 143-185 /usr/local/lib/python3.8/dist-packages/skimage/feature/censure.py 100 81 19% 31-76, 80-90, 94-102, 106-107, 201-216, 242-296 /usr/local/lib/python3.8/dist-packages/skimage/feature/corner.py 200 168 16% 41-44, 95-104, 159-173, 191-197, 236-242, 246-248, 285, 317-325, 394-397, 433-445, 513-525, 583-588, 657-672, 733-737, 801-916, 981-1024, 1071, 1138 /usr/local/lib/python3.8/dist-packages/skimage/feature/haar.py 58 44 24% 21-33, 77-84, 195-219, 290-321 /usr/local/lib/python3.8/dist-packages/skimage/feature/match.py 36 33 8% 53-97 /usr/local/lib/python3.8/dist-packages/skimage/feature/orb.py 132 113 14% 120-131, 134-135, 140-161, 172-210, 213-222, 244-276, 290-348 /usr/local/lib/python3.8/dist-packages/skimage/feature/peak.py 133 124 7% 12-20, 28-40, 48-53, 162-254, 285-357 /usr/local/lib/python3.8/dist-packages/skimage/feature/template.py 59 53 10% 9-17, 22-28, 113-179 /usr/local/lib/python3.8/dist-packages/skimage/feature/texture.py 115 104 10% 106-155, 217-278, 326-337, 381-383, 444-493 /usr/local/lib/python3.8/dist-packages/skimage/feature/util.py 64 52 19% 10, 21, 27, 40, 77-135, 141-143, 166-174 /usr/local/lib/python3.8/dist-packages/skimage/filters/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/skimage/filters/_gabor.py 28 21 25% 10-12, 76-95, 170-177 /usr/local/lib/python3.8/dist-packages/skimage/filters/_gaussian.py 60 51 15% 100-126, 148-157, 258-290 /usr/local/lib/python3.8/dist-packages/skimage/filters/_median.py 14 9 36% 84-97 /usr/local/lib/python3.8/dist-packages/skimage/filters/_rank_order.py 14 12 14% 48-59 /usr/local/lib/python3.8/dist-packages/skimage/filters/_unsharp_mask.py 24 19 21% 9-16, 117-135 /usr/local/lib/python3.8/dist-packages/skimage/filters/_window.py 29 23 21% 99-129 /usr/local/lib/python3.8/dist-packages/skimage/filters/edges.py 126 82 35% 60-64, 91-93, 123-124, 162-185, 237-241, 270-271, 300-301, 358-362, 396-397, 430-431, 485-489, 518-519, 548-549, 581-585, 616-619, 650-653, 682-687, 733-737, 770-773, 803-806 /usr/local/lib/python3.8/dist-packages/skimage/filters/lpi_filter.py 75 60 20% 14-15, 22-24, 37-39, 78-83, 89-113, 123-127, 161-164, 191-204, 230-246, 251 /usr/local/lib/python3.8/dist-packages/skimage/filters/rank/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/skimage/filters/rank/_percentile.py 27 13 52% 38-45, 83, 120, 157, 194, 235, 274, 314, 354, 393 /usr/local/lib/python3.8/dist-packages/skimage/filters/rank/bilateral.py 16 7 56% 37-44, 101, 157, 218 /usr/local/lib/python3.8/dist-packages/skimage/filters/rank/generic.py 95 64 33% 102-146, 181-189, 231-240, 281, 331-337, 376, 416, 465, 504, 548, 596, 642-644, 693, 735, 779, 829, 879, 930, 980-985, 1031-1037, 1085, 1130, 1178-1181, 1225 /usr/local/lib/python3.8/dist-packages/skimage/filters/ridges.py 98 85 13% 44-50, 75-81, 102-106, 141-164, 218-265, 317-354, 430-503, 570-584 /usr/local/lib/python3.8/dist-packages/skimage/filters/thresholding.py 270 240 11% 52-81, 118-139, 204-234, 274-302, 340-356, 412-466, 511-521, 581-645, 694-730, 761, 799-838, 853-858, 891-915, 977-978, 1034-1038, 1076-1085, 1148-1179 /usr/local/lib/python3.8/dist-packages/skimage/io/__init__.py 33 1 97% 45 /usr/local/lib/python3.8/dist-packages/skimage/io/_image_stack.py 9 4 56% 20-23, 35 /usr/local/lib/python3.8/dist-packages/skimage/io/_io.py 44 22 50% 45, 51, 55-56, 59-61, 92, 126-136, 157-159, 179, 201 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/imageio_plugin.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/matplotlib_plugin.py 86 67 22% 44-58, 70-78, 97-112, 148-165, 176-200, 207-208 /usr/local/lib/python3.8/dist-packages/skimage/io/collection.py 152 119 22% 47-52, 77-78, 85-95, 175-205, 209, 213, 216-239, 258-313, 317-323, 327-328, 332, 335, 347, 366, 390, 435-443, 448 /usr/local/lib/python3.8/dist-packages/skimage/io/manage_plugins.py 136 22 84% 78, 115-116, 123, 188, 192-196, 202-206, 257, 293, 304-306, 330-331, 344-347 /usr/local/lib/python3.8/dist-packages/skimage/io/sift.py 27 20 26% 41-69, 73, 77 /usr/local/lib/python3.8/dist-packages/skimage/io/util.py 27 13 52% 24-41 /usr/local/lib/python3.8/dist-packages/skimage/measure/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/skimage/measure/_find_contours.py 60 11 82% 118, 121, 124, 126, 128-133, 139, 155 /usr/local/lib/python3.8/dist-packages/skimage/measure/_label.py 3 1 67% 93 /usr/local/lib/python3.8/dist-packages/skimage/measure/_marching_cubes_classic.py 52 44 15% 103-109, 118-152, 188-194, 257-301 /usr/local/lib/python3.8/dist-packages/skimage/measure/_marching_cubes_lewiner.py 70 56 20% 126-143, 260-265, 277-338, 342-346, 363-388 /usr/local/lib/python3.8/dist-packages/skimage/measure/_marching_cubes_lewiner_luts.py 48 0 100% /usr/local/lib/python3.8/dist-packages/skimage/measure/_moments.py 73 60 18% 45, 111-146, 191, 241-250, 296-305, 348, 372-376, 406-428, 461-469 /usr/local/lib/python3.8/dist-packages/skimage/measure/_polygon.py 61 21 66% 32, 134-168 /usr/local/lib/python3.8/dist-packages/skimage/measure/_regionprops.py 304 167 45% 118-124, 132-135, 147-161, 166, 176, 181, 185, 190, 195-196, 200-201, 207-210, 214-217, 221-224, 228, 232, 237-238, 243, 248-249, 254, 260-262, 265, 269-270, 275, 279, 283, 287-288, 292-293, 298-299, 304-306, 311, 316, 321-328, 333, 337, 341-342, 347-348, 354, 359-360, 366, 371, 374-390, 393-397, 400-411, 499-525, 624-640, 852-896, 935-962 /usr/local/lib/python3.8/dist-packages/skimage/measure/_structural_similarity.py 8 2 75% 10-13 /usr/local/lib/python3.8/dist-packages/skimage/measure/block.py 18 15 17% 62-86 /usr/local/lib/python3.8/dist-packages/skimage/measure/entropy.py 5 2 60% 39-40 /usr/local/lib/python3.8/dist-packages/skimage/measure/fit.py 230 204 11% 9-10, 14-15, 20, 26, 81-100, 120-131, 156-171, 193-194, 216-217, 270-294, 313-320, 339-346, 414-482, 501-544, 564-577, 598-617, 785-876 /usr/local/lib/python3.8/dist-packages/skimage/measure/pnpoly.py 5 2 60% 29, 53 /usr/local/lib/python3.8/dist-packages/skimage/measure/profile.py 35 29 17% 95-127, 154-174 /usr/local/lib/python3.8/dist-packages/skimage/measure/simple_metrics.py 18 6 67% 13-16, 37-40, 61-64 /usr/local/lib/python3.8/dist-packages/skimage/metrics/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/skimage/metrics/_adapted_rand_error.py 19 15 21% 49-76 /usr/local/lib/python3.8/dist-packages/skimage/metrics/_contingency_table.py 15 11 27% 30-40 /usr/local/lib/python3.8/dist-packages/skimage/metrics/_structural_similarity.py 100 92 8% 89-232 /usr/local/lib/python3.8/dist-packages/skimage/metrics/_variation_of_information.py 33 24 27% 43-46, 64-71, 92-117, 133-136 /usr/local/lib/python3.8/dist-packages/skimage/metrics/simple_metrics.py 40 32 20% 15-18, 42-44, 92-105, 139-160 /usr/local/lib/python3.8/dist-packages/skimage/morphology/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/skimage/morphology/_deprecated.py 5 2 60% 109-110 /usr/local/lib/python3.8/dist-packages/skimage/morphology/_flood_fill.py 51 45 12% 107-121, 218-289 /usr/local/lib/python3.8/dist-packages/skimage/morphology/_skeletonize.py 142 125 12% 80-90, 166-176, 184-225, 327-356, 436-510, 518, 559-576, 628-659 /usr/local/lib/python3.8/dist-packages/skimage/morphology/_util.py 57 50 12% 37-54, 92-126, 167-184, 207-210, 252-261 /usr/local/lib/python3.8/dist-packages/skimage/morphology/binary.py 25 14 44% 40-43, 75-78, 110-112, 144-146 /usr/local/lib/python3.8/dist-packages/skimage/morphology/convex_hull.py 58 46 21% 15-18, 50-93, 134-164 /usr/local/lib/python3.8/dist-packages/skimage/morphology/extrema.py 81 70 14% 23-31, 37-45, 114-170, 236-263, 373-426, 530 /usr/local/lib/python3.8/dist-packages/skimage/morphology/grey.py 106 80 25% 32-49, 80-81, 106-127, 181-186, 241-252, 299-302, 349-352, 405-426, 480-489 /usr/local/lib/python3.8/dist-packages/skimage/morphology/greyreconstruct.py 59 56 5% 126-211 /usr/local/lib/python3.8/dist-packages/skimage/morphology/max_tree.py 56 43 23% 118-143, 244-254, 335-346, 457-472, 565-579, 662-670 /usr/local/lib/python3.8/dist-packages/skimage/morphology/misc.py 56 40 29% 37-39, 46-47, 107-139, 201-227 /usr/local/lib/python3.8/dist-packages/skimage/morphology/selem.py 65 51 22% 29, 57, 84-86, 112-114, 154-157, 184, 213-218, 244-249, 279-290, 318-339, 359 /usr/local/lib/python3.8/dist-packages/skimage/restoration/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/skimage/restoration/_cycle_spin.py 48 39 19% 7, 22-45, 115-145 /usr/local/lib/python3.8/dist-packages/skimage/restoration/_denoise.py 245 220 10% 31, 60-61, 85-88, 160-230, 284-312, 344-393, 468-479, 485-488, 493, 519-528, 580-640, 649-669, 683-691, 806-875, 921-935 /usr/local/lib/python3.8/dist-packages/skimage/restoration/deconvolution.py 78 70 10% 115-137, 233-326, 364-377 /usr/local/lib/python3.8/dist-packages/skimage/restoration/inpaint.py 67 57 15% 12-14, 19-73, 116-152 /usr/local/lib/python3.8/dist-packages/skimage/restoration/j_invariant.py 69 57 17% 27-39, 84-87, 114-136, 156-158, 234-254, 285-317 /usr/local/lib/python3.8/dist-packages/skimage/restoration/non_local_means.py 22 17 23% 136-164 /usr/local/lib/python3.8/dist-packages/skimage/restoration/uft.py 53 39 26% 54-57, 85-88, 125-128, 169-172, 204, 236, 270, 307, 336-340, 390-405, 439-449 /usr/local/lib/python3.8/dist-packages/skimage/restoration/unwrap.py 36 30 17% 67-113 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/skimage/segmentation/_chan_vese.py 117 101 14% 8-17, 23-49, 56, 63, 69-79, 86-88, 96-97, 103-106, 113, 121-123, 132-137, 145-150, 156-167, 297-338 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/_clear_border.py 33 30 9% 61-106 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/_felzenszwalb.py 7 4 43% 58-63 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/_join.py 34 29 15% 37-44, 118-155 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/_quickshift.py 17 11 35% 59-74 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/_watershed.py 39 31 21% 63-89, 203-227 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/active_contour_model.py 106 100 6% 100-245 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/boundaries.py 55 47 15% 24-45, 158-180, 214-230 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/morphsnakes.py 128 96 25% 25-26, 50-62, 68-80, 89-92, 101-114, 148-153, 180-190, 213-221, 252-253, 328-357, 442-484 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/random_walker_segmentation.py 174 149 14% 24-32, 38, 76-83, 89-105, 109-135, 144-166, 171-208, 213-263, 420-505 /usr/local/lib/python3.8/dist-packages/skimage/segmentation/slic_superpixels.py 96 82 15% 35-49, 71-85, 211-313 /usr/local/lib/python3.8/dist-packages/skimage/transform/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/skimage/transform/_geometric.py 400 243 39% 11-15, 97-144, 165, 181, 202, 208, 242-247, 263-264, 280-281, 308-333, 356-367, 387-395, 436-458, 481-494, 539, 541, 546, 594, 656-658, 690, 713, 717-722, 727-729, 733-736, 740-743, 793, 796-798, 810, 826-828, 832, 836-837, 841, 861-864, 887-905, 924-940, 959-975, 1016-1040, 1064-1066, 1070, 1074, 1122, 1125-1127, 1134, 1145, 1169-1171, 1176, 1202-1207, 1258-1287, 1303-1317, 1320, 1402-1410 /usr/local/lib/python3.8/dist-packages/skimage/transform/_warps.py 231 145 37% 96-97, 101-102, 109, 122-128, 141-142, 158, 181-196, 277-290, 365-403, 451, 455-470, 524-532, 554-557, 609-635, 676, 681, 827, 837, 846, 861, 867-871, 877, 892-927, 959-963, 990-994, 1054-1095 /usr/local/lib/python3.8/dist-packages/skimage/transform/finite_radon_transform.py 30 25 17% 55-68, 120-134 /usr/local/lib/python3.8/dist-packages/skimage/transform/histogram_matching.py 7 2 71% 6-9 /usr/local/lib/python3.8/dist-packages/skimage/transform/hough_transform.py 69 59 14% 59-68, 110-111, 163, 215-222, 260-266, 328-373, 403-427 /usr/local/lib/python3.8/dist-packages/skimage/transform/integral.py 31 26 16% 33-36, 73-131 /usr/local/lib/python3.8/dist-packages/skimage/transform/pyramids.py 70 59 16% 10-17, 21-22, 66-82, 126-142, 197-224, 284-317 /usr/local/lib/python3.8/dist-packages/skimage/transform/radon_transform.py 183 163 11% 15, 67-123, 127-133, 162-189, 252-313, 348-372, 452-514 /usr/local/lib/python3.8/dist-packages/skimage/util/__init__.py 19 2 89% 22-25 /usr/local/lib/python3.8/dist-packages/skimage/util/_invert.py 14 11 21% 62-74 /usr/local/lib/python3.8/dist-packages/skimage/util/_map_array.py 70 56 20% 26-58, 107-110, 114, 122-126, 130, 133, 136-153, 156, 159-180, 183-187 /usr/local/lib/python3.8/dist-packages/skimage/util/_montage.py 32 29 9% 90-142 /usr/local/lib/python3.8/dist-packages/skimage/util/_regular_grid.py 27 24 11% 61-83, 112-116 /usr/local/lib/python3.8/dist-packages/skimage/util/apply_parallel.py 50 46 8% 23-44, 48-52, 109-147 /usr/local/lib/python3.8/dist-packages/skimage/util/arraycrop.py 17 14 18% 45-63 /usr/local/lib/python3.8/dist-packages/skimage/util/compare.py 25 21 16% 36-60 /usr/local/lib/python3.8/dist-packages/skimage/util/dtype.py 142 95 33% 51-54, 98-101, 126-173, 245, 249, 256-257, 261, 265-268, 272-302, 319-320, 325-349, 375, 401, 454, 479, 503, 527 /usr/local/lib/python3.8/dist-packages/skimage/util/lookfor.py 4 1 75% 24 /usr/local/lib/python3.8/dist-packages/skimage/util/noise.py 54 50 7% 87-192 /usr/local/lib/python3.8/dist-packages/skimage/util/shape.py 48 41 15% 74-95, 209-248 /usr/local/lib/python3.8/dist-packages/skimage/util/unique.py 9 7 22% 39-50 /usr/local/lib/python3.8/dist-packages/tensorflow/__init__.py 365 9 98% 341, 355-356, 372-373, 380, 405, 422, 427 /usr/local/lib/python3.8/dist-packages/tensorflow/_api/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/audio/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/autodiff/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/autograph/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/autograph/experimental/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/bitwise/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/__init__.py 663 3 99% 683-684, 691 /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/app/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/audio/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/autograph/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/autograph/experimental/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/bitwise/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/compat/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/compat/v1/__init__.py 663 3 99% 683-684, 691 /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/compat/v1/compat/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/compat/v2/__init__.py 330 5 98% 327-328, 345-346, 353 /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/compat/v2/compat/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/config/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/config/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/config/optimizer/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/config/threading/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/data/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/data/experimental/__init__.py 62 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/debugging/__init__.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/debugging/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/distribute/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/distribute/cluster_resolver/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/distribute/experimental/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/distributions/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/dtypes/__init__.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/errors/__init__.py 41 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/feature_column/__init__.py 23 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/gfile/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/graph_util/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/image/__init__.py 74 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/initializers/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/io/__init__.py 49 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/io/gfile/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/layers/__init__.py 41 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/layers/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/linalg/__init__.py 70 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/linalg/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/lite/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/lite/constants/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/lite/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/lite/experimental/nn/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/logging/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/lookup/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/lookup/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/losses/__init__.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/manip/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/math/__init__.py 133 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/math/special/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/metrics/__init__.py 37 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/mixed_precision/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/mixed_precision/experimental/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/mlir/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/mlir/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/nest/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/nn/__init__.py 113 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/nn/rnn_cell/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/profiler/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/python_io/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/quantization/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/queue/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/ragged/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/random/__init__.py 33 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/random/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/raw_ops/__init__.py 1265 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/resource_loader/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/__init__.py 56 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/builder/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/constants/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/loader/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/main_op/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/signature_constants/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/signature_def_utils/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/tag_constants/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/saved_model/utils/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/sets/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/signal/__init__.py 34 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/sparse/__init__.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/spectral/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/strings/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/summary/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/sysconfig/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/test/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/tpu/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/tpu/experimental/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/train/__init__.py 122 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/train/experimental/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/train/queue_runner/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/user_ops/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/version/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/xla/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v1/xla/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/__init__.py 330 5 98% 327-328, 345-346, 353 /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/audio/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/autodiff/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/autograph/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/autograph/experimental/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/bitwise/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/compat/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/compat/v1/__init__.py 663 3 99% 683-684, 691 /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/compat/v1/compat/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/compat/v2/__init__.py 330 5 98% 327-328, 345-346, 353 /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/compat/v2/compat/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/config/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/config/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/config/optimizer/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/config/threading/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/data/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/data/experimental/__init__.py 54 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/debugging/__init__.py 33 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/debugging/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/distribute/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/distribute/cluster_resolver/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/dtypes/__init__.py 35 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/errors/__init__.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/experimental/dlpack/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/experimental/tensorrt/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/feature_column/__init__.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/graph_util/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/image/__init__.py 66 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/io/__init__.py 43 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/io/gfile/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/linalg/__init__.py 71 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/linalg/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/lite/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/lite/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/lookup/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/lookup/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/math/__init__.py 133 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/math/special/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/mixed_precision/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/mixed_precision/experimental/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/mlir/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/mlir/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/nest/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/nn/__init__.py 88 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/profiler/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/client/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/server/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/quantization/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/queue/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/ragged/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/random/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/random/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/raw_ops/__init__.py 1265 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/saved_model/__init__.py 33 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/sets/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/signal/__init__.py 34 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/sparse/__init__.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/strings/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/summary/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/summary/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/sysconfig/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/test/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/tpu/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/tpu/experimental/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/train/__init__.py 28 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/train/experimental/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/version/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/xla/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/compat/v2/xla/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/config/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/config/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/config/optimizer/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/config/threading/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/data/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/data/experimental/__init__.py 54 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/debugging/__init__.py 33 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/debugging/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/distribute/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/distribute/cluster_resolver/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/distribute/experimental/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/dtypes/__init__.py 35 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/errors/__init__.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/experimental/dlpack/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/experimental/tensorrt/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/feature_column/__init__.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/graph_util/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/image/__init__.py 66 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/io/__init__.py 43 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/io/gfile/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/linalg/__init__.py 71 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/linalg/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/lite/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/lite/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/lookup/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/lookup/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/math/__init__.py 133 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/math/special/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/mixed_precision/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/mixed_precision/experimental/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/mlir/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/mlir/experimental/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/nest/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/nn/__init__.py 88 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/profiler/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/profiler/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/profiler/experimental/client/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/profiler/experimental/server/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/quantization/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/queue/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/ragged/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/random/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/random/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/raw_ops/__init__.py 1265 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/saved_model/__init__.py 33 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/sets/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/signal/__init__.py 34 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/sparse/__init__.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/strings/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/summary/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/summary/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/sysconfig/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/test/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/tpu/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/tpu/experimental/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/train/__init__.py 28 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/train/experimental/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/version/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/xla/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/_api/v2/xla/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/jit/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/jit/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/jit/ops/xla_ops.py 116 94 19% 35-74, 80-89, 108-166, 172-193 /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/jit/ops/xla_ops_grad.py 8 2 75% 26-27 /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/tf2tensorrt/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/tf2tensorrt/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/tf2tensorrt/ops/gen_trt_ops.py 316 279 12% 35-76, 82-92, 107-146, 152-161, 178-225, 230-240, 257-301, 306-316, 345-501, 507-572 /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/tf2xla/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/tf2xla/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/tf2xla/ops/gen_xla_ops.py 1130 999 12% 50-94, 100-111, 144-207, 213-228, 258-311, 317-331, 354-402, 408-421, 451-494, 500-511, 541-584, 590-601, 621-662, 668-679, 705-762, 768-782, 808-861, 867-882, 911-951, 957-967, 993-1041, 1047-1059, 1079-1127, 1133-1144, 1167-1221, 1227-1243, 1272-1334, 1340-1353, 1367-1406, 1412-1420, 1450-1510, 1516-1531, 1562-1619, 1625-1638, 1678-1728, 1734-1747, 1766-1799, 1804-1811, 1826-1865, 1871-1880, 1900-1939, 1945-1954, 1991-2041, 2047-2060, 2088-2129, 2135-2143 /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/tf2xla/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/tf2xla/python/xla.py 185 66 64% 72, 122-128, 152-162, 167-177, 213, 225-229, 233, 270-273, 290, 294-297, 306, 310-313, 326-327, 332-333, 370-374, 390-393, 397, 405-409, 417-418, 429, 440 /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/xla/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/xla/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/xla/experimental/xla_sharding/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py 77 50 35% 37, 46, 58, 82-86, 109-122, 135-149, 154, 157-163, 166-170, 180-185, 193-199, 215-221, 240-245 /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/xla/service/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/xla/service/hlo_pb2.py 150 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/compiler/xla/xla_data_pb2.py 223 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/example/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/example/example_pb2.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/example/feature_pb2.py 62 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/allocation_description_pb2.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/api_def_pb2.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/attr_value_pb2.py 63 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/cost_graph_pb2.py 37 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/device_attributes_pb2.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/function_pb2.py 65 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/graph_pb2.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/kernel_def_pb2.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/node_def_pb2.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/op_def_pb2.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/resource_handle_pb2.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/step_stats_pb2.py 56 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/summary_pb2.py 70 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/tensor_description_pb2.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/tensor_pb2.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/tensor_shape_pb2.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/types_pb2.py 62 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/variable_pb2.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/versions_pb2.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/grappler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/grappler/costs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/grappler/costs/op_performance_data_pb2.py 71 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/kernels/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/kernels/boosted_trees/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/kernels/boosted_trees/boosted_trees_pb2.py 105 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/lib/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/lib/core/error_codes_pb2.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/profiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/profiler/tfprof_log_pb2.py 137 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/profiler/tfprof_options_pb2.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/profiler/tfprof_output_pb2.py 48 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/cluster_pb2.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/config_pb2.py 124 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/control_flow_pb2.py 41 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/debug_event_pb2.py 99 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/debug_pb2.py 28 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/device_filters_pb2.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/device_properties_pb2.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/error_codes_pb2.py 33 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/graph_debug_info_pb2.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/meta_graph_pb2.py 129 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/queue_runner_pb2.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/rewriter_config_pb2.py 62 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/saved_model_pb2.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/saved_object_graph_pb2.py 100 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/saver_pb2.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/struct_pb2.py 100 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/tensorflow_server_pb2.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/tpu/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/tpu/compilation_result_pb2.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/tpu/dynamic_padding_pb2.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/tpu/optimization_parameters_pb2.py 160 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/tpu/topology_pb2.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/tpu/tpu_embedding_configuration_pb2.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/tpu/tpu_embedding_output_layout_pb2.py 35 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/trackable_object_graph_pb2.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/protobuf/verifier_config_pb2.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/util/event_pb2.py 91 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/core/util/test_log_pb2.py 110 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/__init__.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/activations/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/__init__.py 35 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/densenet/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/imagenet_utils/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/inception_resnet_v2/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/inception_v3/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/mobilenet/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/mobilenet_v2/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/nasnet/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/resnet/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/resnet50/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/resnet_v2/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/vgg16/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/vgg19/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/applications/xception/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/backend/__init__.py 148 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/callbacks/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/constraints/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/datasets/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/datasets/boston_housing/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/datasets/cifar10/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/datasets/cifar100/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/datasets/fashion_mnist/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/datasets/imdb/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/datasets/mnist/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/datasets/reuters/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/estimator/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/experimental/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/initializers/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/layers/__init__.py 118 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/layers/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/layers/experimental/preprocessing/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/losses/__init__.py 48 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/metrics/__init__.py 71 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/mixed_precision/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/mixed_precision/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/models/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/optimizers/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/optimizers/schedules/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/preprocessing/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/preprocessing/image/__init__.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/preprocessing/sequence/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/preprocessing/text/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/regularizers/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/utils/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/wrappers/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/wrappers/scikit_learn/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/examples/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/examples/lstm/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/examples/lstm/rnn.py 91 68 25% 161-276, 364-429 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/examples/lstm/rnn_cell.py 165 127 23% 74-85, 89, 93, 104-125, 129-148, 151-157, 243-274, 280, 284, 296-385, 411-512, 515-530 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/microfrontend/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/microfrontend/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/microfrontend/ops/gen_audio_microfrontend_op.py 189 172 9% 97-301, 307-391 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/microfrontend/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/microfrontend/python/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/microfrontend/python/ops/audio_microfrontend_op.py 21 6 71% 102-108 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/tensorboard/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/tensorboard/ops_util.py 12 2 83% 49-50 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/convert.py 171 125 27% 50, 58, 68-76, 98, 103, 142-236, 338-404, 437-460, 488-497, 525-526 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/convert_saved_model.py 67 50 25% 32-42, 59-60, 76-85, 97-109, 135-152, 187-207 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/interpreter.py 138 98 29% 46-50, 86-120, 125-128, 138, 160-165, 199-228, 237-238, 241-242, 254, 266-267, 283-295, 320-349, 359, 372-378, 386, 404, 417-421, 429, 446, 496, 510-511, 514, 547-548 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/lite.py 431 302 30% 118, 140, 160-165, 172-181, 192-203, 206-214, 218-221, 225, 230-233, 236, 243, 246, 250, 254, 260-265, 271-279, 287-298, 349-351, 368-375, 398-410, 422-436, 451-524, 661-688, 703-704, 739-796, 831-838, 870-911, 918-932, 935-944, 959-1096, 1104-1107, 1115, 1127-1135, 1139-1153, 1168, 1179, 1193, 1206 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/lite_constants.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/op_hint.py 549 464 15% 193-202, 217-227, 254-310, 332-347, 358-377, 382-384, 408, 430, 442-448, 460-466, 494-495, 512-513, 520-522, 525, 528, 532-536, 539, 551-555, 558-559, 563-579, 583, 600-615, 634-659, 662-665, 689-695, 703-709, 712-721, 737-787, 791, 805-825, 843-879, 893-895, 899, 917-927, 955-1060, 1073-1150, 1154-1159, 1165-1171, 1185-1247, 1272-1282, 1286-1294, 1318-1326 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/optimize/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/optimize/calibrator.py 32 20 38% 47-55, 75-78, 102-105, 118-121 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/util.py 175 140 20% 68-71, 83-92, 110-129, 145-160, 172-176, 196-218, 223-229, 252-269, 284-288, 302-327, 341-356, 370-387, 413-507 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/wrap_toco.py 10 2 80% 32, 43 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/model_flags_pb2.py 45 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/toco_flags_pb2.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/types_pb2.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/__init__.py 116 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/arg_defaults.py 30 7 77% 66-70, 81, 87-88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/asserts.py 18 8 56% 31-47, 51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/break_statements.py 68 47 31% 30-31, 34, 41-48, 52-63, 66-71, 74-105, 108-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/call_trees.py 97 10 90% 102-106, 136, 198-207, 211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/conditional_expressions.py 12 1 92% 29 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/continue_statements.py 91 46 49% 34, 62-74, 79-93, 97-115, 124-128, 131-136, 149-154, 157-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/control_flow.py 208 86 59% 126-128, 144-155, 160-167, 184-195, 200-203, 236, 343-353, 357-378, 381-396, 399-432, 447-510 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/directives.py 92 39 58% 67-87, 94-101, 104-115, 139-140, 153-154, 156-157, 164-168, 171, 174 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/function_scopes.py 57 8 86% 70-88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/lists.py 100 72 28% 46, 53-57, 60-65, 90-107, 110-123, 130-148, 151-180, 190-199, 202, 208-211, 214-217, 220-223, 226-229, 232-234, 238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/logical_expressions.py 66 23 65% 56-59, 72-77, 90-112, 119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/return_statements.py 187 49 74% 87, 99-102, 105-109, 122-126, 130-131, 144, 147, 166, 177, 258-266, 282-293, 296-315, 323-327, 330-331, 376 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/slices.py 35 22 37% 37-45, 49-56, 59-80, 85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/ag_ctx.py 37 3 92% 58, 70, 73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config_lib.py 28 3 89% 48, 60, 64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/converter.py 125 24 81% 131, 136-138, 171, 187, 310-335, 340 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/function_wrappers.py 59 20 66% 55, 60-61, 66-68, 74, 76, 83, 85, 90-101, 107-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/naming.py 64 19 70% 62, 72-73, 79-88, 98-99, 110-113, 117-118 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/unsupported_features_checker.py 25 9 64% 35, 40-43, 46-49, 54, 57 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py 290 117 60% 55, 78-120, 146-147, 189, 206, 212-214, 218, 263-267, 280-287, 305, 308-309, 319-333, 343, 355, 362, 374, 413, 421-422, 425-426, 448, 450, 452, 459-464, 467-468, 481-482, 488-489, 499, 511, 514-522, 526-528, 532-534, 541-580, 588-590, 655-665, 732-734, 785-788, 831-836 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/conversion.py 322 94 71% 87, 117, 134, 151, 210, 308-310, 345, 350, 355, 388, 397-398, 406-410, 418-419, 440, 443-444, 451-453, 459-465, 474-476, 482-484, 509, 514-521, 526, 528-529, 538-632, 638-639, 683-690, 700, 704, 715, 724, 751, 758-759 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/directives.py 16 7 56% 44-46, 95-98 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/special_functions.py 33 20 39% 33-45, 53, 83-88, 113-119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/__init__.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/control_flow.py 449 379 16% 109-119, 128-135, 142-191, 204-234, 241-263, 272-297, 341-372, 377-401, 407-439, 452-485, 497-526, 538-586, 606, 621-677, 684-704, 735-751, 765-769, 772, 775-776, 779-781, 785-804, 808-811, 815-825, 830-846, 851-856, 861-886, 923, 932-966, 991-999, 1005-1030 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/data_structures.py 150 118 21% 45-54, 59-104, 109-163, 168, 189-199, 204-215, 220, 226-227, 257-269, 274-286, 291-295, 321-332, 337, 342-345, 351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/exceptions.py 24 14 42% 50-59, 77-80, 85-86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/logical.py 43 16 63% 29, 35, 47, 54, 64-67, 73, 78, 83-85, 90, 95, 100 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/py_builtins.py 253 176 30% 62, 68-84, 92-99, 120-161, 165-169, 173, 177-180, 184, 188-190, 195-197, 201, 205-207, 211-217, 221-223, 227-233, 237, 241, 247-273, 278, 284-294, 298, 303-319, 324-326, 336-343, 347-351, 355-361, 365, 369, 373-375, 379, 383, 387-389, 393, 397, 401-403, 407, 411, 415-417, 428-436, 440, 444-446, 454-462, 466, 470-472, 477-497, 501-507, 514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/slices.py 55 31 44% 55-67, 72, 77-81, 86, 91-92, 97, 117-125, 130, 135, 140, 145-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/special_values.py 27 10 63% 52, 55, 58-63, 66, 81, 93, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/anno.py 59 7 88% 40, 107, 134-138 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/ast_util.py 214 117 45% 101-105, 108-109, 112-113, 130-132, 137-142, 149-151, 154-157, 160-161, 164-170, 173-206, 222-227, 262-275, 294, 299, 309, 315-316, 318-319, 325, 330, 341, 350-351, 354-359, 362-378, 381-388, 392-394 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/cfg.py 416 121 71% 86-93, 132, 136-143, 188, 200, 334-335, 340, 356, 418, 420-422, 455-456, 468-471, 507-511, 515-526, 566, 571-576, 580-586, 662, 665, 671, 695-700, 714-728, 758, 761, 770, 773, 779, 782, 785, 794, 797, 822-841, 844-871, 874, 877, 884, 886, 906-912, 928-931 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/error_utils.py 80 60 25% 84-123, 144-145, 148, 165-175, 179-209, 212-219, 222-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/errors.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/inspect_utils.py 144 65 55% 57, 64, 72-79, 89, 91, 171-173, 195-237, 246, 261-262, 292-293, 299, 304-336, 349 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/loader.py 34 2 94% 80, 88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/origin_info.py 114 17 85% 77, 81-85, 117, 139-155, 180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/parser.py 116 28 76% 63-67, 75-78, 81, 93, 149-178, 197-198, 213, 239, 258, 279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/pretty_printer.py 87 69 21% 30-33, 36-38, 41, 44, 47, 50, 53, 56-57, 62-125, 129-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/qual_names.py 141 45 68% 48, 51, 58, 61, 70, 77, 80, 88, 96, 104, 107, 120-122, 134-138, 153-161, 173, 180, 187-191, 198, 203, 210-215, 244, 252, 265-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/activity.py 341 103 70% 121-124, 134-135, 189, 204-205, 244-249, 261-264, 269-274, 279, 281, 290-300, 324, 327, 330-332, 347, 352-361, 364, 372-382, 387, 400-404, 407, 445-456, 461-463, 466, 469, 472, 475, 481-497, 554-568, 571-579, 586 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/annos.py 17 1 94% 30 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py 121 9 93% 78, 191-194, 204-206, 209-211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py 166 26 84% 56, 78, 109, 159-174, 249, 279-292, 295-296 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/templates.py 147 28 81% 69-70, 86-87, 90-93, 98-99, 158-167, 175, 179, 190-195, 260, 283, 289-292 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/transformer.py 134 27 80% 131, 258-259, 264-265, 322, 329, 382-395, 398-406, 415-418, 421, 443 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/ag_logging.py 47 19 60% 37, 87-88, 111, 117, 126-128, 132-135, 140-142, 146-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/compat_util.py 14 3 79% 31, 37-38 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/context_managers.py 18 10 44% 38-49 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/misc.py 26 15 42% 42-52, 57-59, 63-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/py_func.py 48 37 23% 64-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/tensor_list.py 32 16 50% 28-40, 47-49, 52, 55-56, 59, 62, 65, 68 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/tensors.py 16 3 81% 39, 47, 53 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/testing.py 17 8 53% 30-37 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/type_check.py 7 1 86% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/client_lib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/device_lib.py 15 8 47% 34-42 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/pywrap_tf_session.py 31 11 65% 53-59, 66-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py 620 490 21% 57, 62, 66, 70, 74, 78, 84, 141, 192, 201, 206-207, 231, 245, 261-278, 301-316, 319, 322-326, 349-362, 374-379, 382, 386-396, 408-413, 416, 419-422, 434-437, 440, 443-446, 476-498, 501-502, 515, 523, 544-570, 582, 597-600, 604, 608, 612, 616, 619, 650-704, 731-742, 753-755, 759-772, 777, 787, 791, 846, 952-967, 1014, 1038-1089, 1095-1184, 1222-1309, 1340-1361, 1364-1384, 1387-1388, 1396-1411, 1417-1437, 1441, 1446, 1454-1462, 1467-1479, 1485-1487, 1504-1505, 1586-1589, 1592-1601, 1604-1641, 1669-1675, 1738-1769, 1773-1784 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/timeline.py 288 227 21% 60-62, 81-88, 97-102, 112-118, 132-135, 148-150, 163-165, 180-183, 198-200, 215-217, 230-232, 244-246, 257-262, 285-292, 297, 302, 307, 312, 317, 322, 327, 335, 343, 362-372, 376-378, 382-384, 389-397, 401-414, 424-441, 453-455, 459-463, 467, 471-481, 485-512, 517-561, 569-608, 611-617, 633-636 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/compat.py 32 7 78% 49, 54, 119, 162-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/v2_compat.py 60 19 68% 92-113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/mlir.py 9 1 89% 41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/trt_convert.py 455 365 20% 58, 80-81, 86-88, 93-95, 105-108, 199-245, 258-284, 309-371, 384, 389-395, 487-542, 547-559, 562-569, 573-580, 587-592, 596-641, 649-654, 683-747, 763-830, 834-835, 842-844, 847-849, 861-867, 870, 873, 995-1022, 1033-1036, 1041-1047, 1051-1056, 1075-1124, 1146-1187, 1195-1266, 1340-1357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/jit.py 36 21 42% 36-37, 90-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/xla.py 240 189 21% 115-122, 153-157, 160-168, 173-193, 198-266, 270-283, 286-288, 296, 301-303, 329-406, 430-441, 460-493, 507-528, 542-548, 555, 558-563, 566, 571-581, 598-629 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/__init__.py 64 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/batching.py 107 64 40% 81-86, 133-136, 179-192, 243-255, 282-285, 293-310, 315, 323-361, 364, 368, 394-422, 426 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/cardinality.py 27 8 70% 66, 95-98, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/counter.py 23 6 74% 51-54, 60, 66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute.py 70 45 36% 48-67, 71, 75, 88-119, 123, 130-133, 137, 150-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute_options.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/enumerate_ops.py 12 3 75% 55-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/error_ops.py 17 6 65% 50-53, 61-66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/get_single_element.py 13 3 77% 62-66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/grouping.py 177 125 29% 60-64, 106-124, 177-244, 252-268, 272-276, 283, 293-349, 353, 359, 362, 367, 375-388, 393-401, 407-413, 418-428, 433, 436, 439, 453-455, 459, 463, 467 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/interleave_ops.py 86 46 47% 95-101, 108-135, 138, 142, 169-226, 231, 272-276, 281, 290-291 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/iterator_ops.py 64 37 42% 32-38, 94-95, 184-221, 227-238, 243, 266-277, 281-284, 287, 290, 301-302, 313 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/optimization_options.py 76 10 87% 57, 63-66, 191, 209, 211, 232, 256, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/parsing_ops.py 61 41 33% 37-117, 121, 161-180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/prefetching_ops.py 103 71 31% 52-56, 72-80, 97-212, 220-225, 233-247, 250, 254, 257, 278-281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/random_ops.py 30 7 77% 37-40, 44, 53-54, 60 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/readers.py 300 228 24% 50-55, 59-63, 67-70, 87-107, 113-127, 134-151, 159-179, 184-203, 209-213, 268-311, 424-558, 584, 677-720, 724, 742-745, 854-929, 948, 970-985, 1016-1027, 1031, 1040-1041, 1050-1053 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/resampling.py 88 63 28% 56-106, 123-129, 147-174, 180-196, 201-202, 218-227, 295-304 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/scan_ops.py 63 44 30% 38-144, 147, 151, 154, 178-181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/shuffle_ops.py 28 11 61% 34-50, 106-109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_aggregator.py 23 6 74% 64-77, 128, 140 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_ops.py 32 14 56% 44-48, 67-71, 90-94, 101-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_options.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/take_while_ops.py 25 11 56% 33-49, 52, 69-72 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/threading_options.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/unique.py 20 8 60% 45-48, 56-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/writers.py 22 7 68% 77-79, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/dataset_ops.py 1381 743 46% 203, 227-240, 252-279, 285, 289, 293, 306-318, 329, 354, 357, 367, 385, 406, 420, 423-427, 475-484, 640, 658-665, 668-673, 676, 734, 736, 744, 785-823, 840-846, 913, 958, 987, 1057-1084, 1105, 1137-1138, 1198, 1248, 1267, 1286, 1354, 1387, 1489-1497, 1620-1623, 1748-1751, 1780, 1804-1810, 1889-1891, 1919-1996, 2023-2024, 2051, 2064-2078, 2087, 2103, 2106-2150, 2186, 2189-2204, 2218, 2232, 2246, 2254, 2260, 2265, 2278, 2283, 2289, 2294, 2298, 2302, 2307, 2311, 2315, 2320, 2324, 2328, 2332, 2336, 2345, 2351-2355, 2393-2401, 2412, 2421, 2427, 2446, 2450, 2454, 2459, 2463, 2469, 2476-2477, 2480, 2483, 2486, 2489, 2492, 2496, 2499, 2505-2524, 2540-2545, 2574-2579, 2597-2600, 2619, 2639, 2659, 2750, 2754, 2756, 2758, 2761, 2771-2774, 2782, 2858-2873, 2877, 2885-2902, 2906, 2913-2914, 2917, 2921, 2927-2929, 2933, 2948, 2961, 2985, 2988, 2992, 2995, 2999-3002, 3013, 3016, 3021-3023, 3026-3028, 3031, 3034, 3037, 3088-3092, 3097, 3104, 3144, 3168, 3172-3173, 3181-3202, 3226, 3230-3231, 3244, 3250, 3256, 3325-3342, 3345, 3349, 3357-3390, 3393, 3397, 3405-3415, 3423-3430, 3434-3451, 3454, 3458, 3471-3474, 3477-3485, 3495-3501, 3506, 3514-3529, 3540-3543, 3546-3554, 3562-3566, 3571, 3601-3627, 3635-3641, 3649-3655, 3663-3672, 3680-3705, 3709, 3716, 3719, 3722, 3725, 3765-3774, 3794-3826, 3843-3849, 3855-3875, 3885-3952, 3956, 3974-3989, 3992, 3996, 3999, 4014-4051, 4054, 4058, 4061, 4073, 4085, 4100-4120, 4123, 4127, 4130, 4146-4209, 4213, 4217, 4220, 4228-4246, 4249, 4252, 4273, 4289-4313, 4317, 4324-4331, 4334, 4356, 4358, 4373-4383, 4391-4400, 4408-4415, 4437-4448, 4455-4459, 4463, 4471-4488, 4492, 4498-4534, 4545-4553, 4564-4572 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/iterator_ops.py 253 111 56% 70-72, 99-117, 189-220, 273-298, 311-316, 333-371, 418-428, 439-442, 457, 471, 485, 497, 501-504, 512-516, 540-541, 581, 591, 597, 628, 647-661, 671-672, 686, 700, 714, 740-741, 745-748, 762, 785, 797-806, 809-810, 828 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py 291 224 23% 43-140, 144, 148, 160-182, 186, 190, 196-208, 232-302, 306-320, 324-332, 335-340, 344-346, 350-365, 370, 381-385, 388-400, 412-414, 418, 421, 425-431, 435-437, 440, 450, 493-566, 574-582, 585, 588, 591-594, 597-602, 606, 610 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/optional_ops.py 77 26 66% 59, 75, 85, 98-103, 121, 132-133, 136, 141-143, 155, 159, 170, 174, 177, 181, 184, 188, 192, 195, 198, 201 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/readers.py 198 115 42% 46-64, 80-90, 114-127, 131, 160-172, 176, 188-190, 196, 200, 216-229, 233, 242-291, 295, 299, 302, 336-349, 356, 362, 366, 378-380, 390, 398, 402, 430-447, 451, 488-505, 509, 524-527, 533, 537, 545-547 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/convert.py 23 13 43% 30-34, 49-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/nest.py 101 35 65% 47-50, 69-70, 75, 88-89, 91, 176, 180, 186, 225-244, 297, 302, 308, 314-320, 462 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/options.py 59 30 49% 23, 38-43, 46-49, 52-55, 81-84, 115, 120, 124, 131-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/random_seed.py 21 10 52% 42-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/structure.py 178 72 60% 45, 51, 57, 64, 100-114, 146-172, 198, 252, 350, 394-404, 435-439, 445-450, 456-462, 476, 486, 489, 493, 496, 499, 502, 506, 509, 512, 515, 518, 521, 524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/traverse.py 21 14 33% 39-56 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/__init__.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/analyzer_cli.py 581 520 10% 84-127, 151-158, 173-418, 444-458, 473-476, 479, 502-598, 616-639, 656-674, 690-738, 757-823, 834, 850-872, 891-908, 925-1050, 1069-1086, 1089-1095, 1107, 1113-1168, 1183-1240, 1244-1271, 1301-1361, 1404-1469, 1495-1520, 1533-1544, 1556-1579, 1601-1659 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_config.py 81 60 26% 41-50, 53-55, 71-97, 114-118, 121, 124-128, 139-147, 150-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_shared.py 177 136 23% 69-82, 99-110, 126-140, 144-147, 185-208, 229, 248-258, 264-273, 301-383, 405-431, 445-493 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/command_parser.py 207 177 14% 37-40, 43-47, 50, 72-101, 118-148, 164-171, 187, 204-216, 234-240, 259-281, 299-310, 328-339, 359-403, 426-439, 454-468, 486-491, 506-550 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/debugger_cli_common.py 451 353 22% 44-45, 49, 73-77, 94-107, 110, 123-132, 145-151, 198-212, 217, 221, 225, 228, 246-267, 285-300, 310-332, 343-345, 348, 359-362, 373-375, 403-431, 456-527, 563-585, 625-655, 682-716, 727, 741-757, 767, 783-790, 793-795, 807-812, 825-843, 850, 883-896, 909-915, 929-934, 948-953, 974-981, 992-1001, 1019-1023, 1026-1040, 1043-1047, 1051, 1063-1075, 1088, 1103-1105, 1125-1127, 1131, 1135, 1139, 1142, 1145, 1148, 1161-1162, 1170, 1173, 1176, 1179, 1194-1199, 1221-1248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/evaluator.py 52 37 29% 69-103, 115-116, 131-152 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/profile_analyzer_cli.py 302 252 17% 58-77, 103-131, 134, 137, 140, 143, 173-195, 209-220, 236-380, 396-439, 447-474, 504-575, 586-592, 613-733, 737-742, 756-762, 765, 786-802 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/tensor_format.py 240 215 10% 67-69, 103-199, 233-279, 321-403, 407-426, 449-481, 485, 503-568 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/ui_factory.py 23 16 30% 51-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/check_numerics_callback.py 122 89 27% 103-106, 114-119, 156-212, 216, 226-232, 242-289, 314-328, 414-419, 437-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/common.py 22 11 50% 44, 59-71, 86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_data.py 530 403 24% 53-56, 70-71, 74-76, 80, 99-102, 121-140, 144-148, 152-156, 160, 164, 168, 182, 199, 219-231, 241-242, 252-254, 266-268, 307-330, 334, 341, 350, 361, 375, 385, 395, 405, 415, 425, 435, 442, 455, 485-498, 502-522, 555-576, 580-581, 584-590, 594-598, 602-606, 621-623, 636-655, 669-673, 684, 717-718, 723-730, 739, 748, 771-799, 802-809, 812-817, 834-886, 906-918, 922, 933-935, 953-956, 970-971, 985-986, 1007-1020, 1036-1046, 1062-1066, 1085-1093, 1121-1137, 1141-1151, 1193-1229, 1249-1258, 1266, 1284-1295, 1313-1322, 1339-1344, 1363-1378, 1397-1415, 1448-1464, 1490-1497, 1522-1528, 1560-1567, 1594-1601, 1618-1625 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_events_writer.py 52 29 44% 51-54, 64-66, 76-79, 89-92, 102-104, 113-115, 125-128, 132, 137, 146, 150, 154, 157-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_gradients.py 120 82 32% 38-39, 53-65, 85-98, 102, 106, 109, 112, 157-169, 215-222, 267-284, 287-290, 304-306, 324-329, 338, 341-346, 353, 360-363, 369, 403-417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_graphs.py 237 183 23% 40-46, 50-51, 66-67, 83, 98, 116-139, 170-178, 191-214, 217, 220, 225-234, 241-266, 277-309, 320-329, 333-337, 346-354, 358-365, 372-393, 401-405, 413-431, 435, 440, 445-446, 450, 454, 458, 462, 466, 470, 474, 478, 503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_utils.py 69 60 13% 61-79, 137-197, 252-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/dumping_callback.py 301 237 21% 61, 67-68, 73, 77, 89-115, 125-133, 137, 141-143, 147, 151, 155-159, 176-189, 202-205, 216-232, 242-267, 288-301, 334-421, 453-514, 526-571, 590-608, 611-617, 632-643, 740-807, 819-826 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/op_callbacks_common.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/profiling.py 41 25 39% 44-56, 62, 76-80, 90-100, 104, 108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/source_utils.py 135 110 19% 44, 48-49, 53-54, 58, 78-84, 112-125, 145-158, 192-225, 262-325, 353-383 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/dumping_wrapper.py 42 28 33% 69-90, 110-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/framework.py 279 191 32% 130-131, 148-149, 177-178, 204-209, 259-270, 298-305, 314, 346-379, 383, 387, 391, 395, 428-517, 530-588, 605-634, 640-641, 645, 650, 654, 658, 661, 667-676, 679-684, 688, 691, 703, 733-734, 753, 806, 809-811, 814, 818-819, 822, 828-831, 867-874, 878, 915-923, 928, 948-951, 975-984, 989 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/grpc_wrapper.py 58 38 34% 57-66, 100-118, 137, 140, 146-151, 155-159, 193-210, 220-224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/hooks.py 95 67 29% 60-65, 83-86, 89, 92-141, 146-148, 176-180, 183, 186-217, 220, 256-264, 277-301, 335-349, 352-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/local_cli_wrapper.py 237 197 17% 80-131, 136, 139-206, 218, 230, 242-277, 280-285, 289-304, 320-374, 377-378, 399-446, 449-453, 462-468, 471-485, 488-513, 525-576, 579-603, 613, 633-642 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/all_reduce.py 392 355 9% 44-57, 71-75, 95-128, 145-157, 176-190, 223-251, 277-294, 317-356, 370-374, 394-423, 469-477, 496-518, 532-555, 582-589, 608-626, 640-645, 666-682, 701-711, 730-762, 767-776, 781-785, 790-791, 797-800, 820-842, 848-853, 860-865 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/central_storage_strategy.py 34 11 68% 56-70, 75, 103, 144, 162, 180, 246, 255-260 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py 173 108 38% 36-39, 44-56, 99, 117, 144-154, 172, 183-198, 202, 218-223, 227, 231, 235, 239, 243, 262-265, 269, 273, 305-321, 347-395, 411-415, 419, 423, 427, 431, 435, 441, 446, 450 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py 77 49 36% 30-31, 83-104, 116-149, 152-162, 166, 170, 174, 180, 184, 188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py 52 34 35% 29, 76-94, 112-120, 135-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py 160 132 18% 39-86, 96-107, 121-125, 135, 146-155, 164, 233-275, 280, 284, 288, 298-301, 321-358, 372, 386-394, 401-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py 78 42 46% 36-39, 43, 47-48, 76-79, 83-87, 91-95, 99, 103, 107, 111-114, 118, 124-126, 135-138, 160-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py 106 67 37% 40, 76-85, 90-95, 150-167, 170, 173, 201-214, 217, 220, 246-256, 278-300, 305, 308-319, 324 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py 228 165 28% 99-110, 116-118, 135, 148-158, 169-176, 180-183, 187-242, 247-344, 356-391, 399-408, 411-412, 421-422, 434-435, 456-470, 473-508, 511-530, 537-540, 545, 549, 553, 557, 561, 565, 577 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_util.py 11 3 73% 62-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_ops.py 452 356 21% 56-59, 64-73, 79-102, 107-116, 122-138, 144-151, 157-161, 165, 169-174, 179-188, 194-211, 219, 224, 251-266, 298-318, 331-332, 359, 387, 402, 426-428, 432-443, 447, 472-479, 504-513, 529-531, 535-583, 587-617, 622-628, 633-635, 652-655, 659-663, 668-672, 679-690, 695-720, 724-730, 760-764, 791-795, 835-861, 867-907, 948-953, 957, 961-989, 993-1004, 1012-1024, 1031-1101, 1106-1137, 1151-1183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_utils.py 400 345 14% 44-53, 80-135, 158-170, 188-197, 214-229, 270-275, 280-282, 293-306, 318-319, 323-325, 329-331, 365-387, 414-433, 465-535, 545-576, 594-618, 635-655, 675-690, 705-713, 743-769, 785-801, 806-809, 813-818, 822-830, 835-842, 846-849, 863-874, 887-897, 910-916, 937-958, 976-986 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/device_util.py 47 29 38% 47-67, 72, 79-80, 87-90, 93, 96, 102-108, 113-114, 121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_coordinator.py 315 243 23% 74-79, 83-92, 97-99, 137-146, 149-153, 156-162, 167, 172-192, 196-205, 213-216, 244-260, 268, 273, 278, 283, 288, 293, 298, 303, 308, 313, 318, 323, 336-360, 373-382, 395-451, 457-494, 500-526, 538-552, 590-625, 752-868 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_coordinator_context.py 11 4 64% 28-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py 687 352 49% 149-152, 159-160, 163-164, 167-168, 186-194, 206, 209-214, 220-225, 232-240, 249, 254-258, 277-285, 289-300, 303-332, 374-376, 381, 386, 391, 407-411, 414, 463-464, 469, 474, 477, 507, 617-625, 640-644, 661, 666, 673-677, 705, 711-713, 874, 943, 957, 998-1056, 1078, 1097, 1102, 1107, 1126, 1132, 1136-1144, 1147, 1201, 1264, 1314, 1388, 1429, 1470, 1501, 1536, 1540, 1559, 1754-1777, 1785, 1789, 1818, 1857-1865, 1869, 1872, 1877, 1881, 1885, 1888, 1891, 1894, 1897, 1901, 1922-1931, 1934, 1953-1959, 1964, 2007-2013, 2016, 2033-2039, 2042, 2045, 2060, 2064-2072, 2077, 2082, 2089, 2095, 2110, 2118, 2121, 2140, 2165-2166, 2169, 2182-2186, 2189, 2233-2235, 2240, 2293, 2308, 2317, 2322, 2327, 2332, 2374, 2415-2420, 2424-2429, 2434, 2447-2448, 2458-2459, 2489-2511, 2526-2530, 2544, 2550-2551, 2571, 2583-2584, 2603-2604, 2613, 2616, 2619, 2624-2625, 2628-2637, 2640-2643, 2653-2654, 2659, 2664-2669, 2672, 2675, 2682, 2686, 2691, 2695, 2708-2712, 2715, 2724-2728, 2733, 2739, 2751-2756 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distributed_file_utils.py 33 21 36% 57-58, 62, 66-68, 73-86, 91-104 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribution_strategy_context.py 102 23 77% 51, 151, 176, 256-266, 275-280, 302-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/estimator_training.py 176 152 14% 42-46, 51-55, 61-86, 94-124, 130-177, 184-200, 216-283, 297-336, 346-384 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/input_lib.py 673 488 27% 83-91, 124-131, 148-150, 155, 159, 162, 165-169, 172, 175, 180-216, 231-240, 245-256, 263-289, 292, 295-298, 301, 305-377, 388-391, 400, 405, 410, 415, 420, 424-427, 432, 444-450, 454, 459, 463, 469-481, 495-508, 513-521, 524, 527, 536, 546-571, 576, 580, 589-590, 593, 597-621, 650-705, 709-729, 734, 746-747, 763, 770-773, 785, 791-794, 797-803, 806-810, 830-845, 849-873, 879-885, 893-899, 906-909, 912-920, 923-927, 953-975, 1007-1019, 1025-1065, 1082-1086, 1089, 1093-1095, 1110-1112, 1130-1156, 1165-1167, 1171, 1174, 1178-1181, 1184, 1187, 1197, 1223-1239, 1244-1249, 1254, 1258, 1271, 1283, 1295, 1305-1306, 1319-1323, 1327, 1331, 1335, 1342-1344, 1348-1350, 1354-1357, 1361-1364, 1368, 1374-1388, 1396-1415, 1422-1431, 1440-1456, 1467-1478, 1498-1500, 1515, 1519-1521, 1543-1561, 1567, 1571-1579, 1595-1608 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/input_ops.py 41 27 34% 46-53, 58-61, 65, 79-101 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/mirrored_strategy.py 526 409 22% 65-74, 78-80, 113-203, 220-236, 241-252, 265-281, 285, 305-325, 329-332, 336-341, 426-429, 439-442, 451-476, 481-488, 492-505, 510-545, 553-569, 573-607, 613, 616, 626-633, 638, 645, 649-657, 664-669, 674-725, 733-739, 742-769, 777-788, 791-793, 796, 799-810, 818, 824-835, 838-846, 850-853, 856-858, 861, 865, 869, 873, 877, 881, 885, 889, 893, 896-898, 910, 914, 922-975, 978-1001, 1006-1010, 1016-1020, 1024-1026, 1030-1032, 1048-1086, 1090-1092 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/multi_worker_util.py 74 57 23% 40-46, 72-93, 120-134, 150-168, 173-188, 211-227, 243, 256, 261, 266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/numpy_dataset.py 45 29 36% 34-73, 79-91, 98 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/one_device_strategy.py 155 82 47% 80, 107, 146, 164, 182, 215, 231, 242, 251-256, 259-268, 271, 277, 284, 289, 293-294, 299, 303, 313, 318-357, 360-362, 365-366, 371, 374-380, 384, 387, 390, 394, 398, 402, 406, 409-410, 414, 418, 422, 426, 432, 436, 443-444, 449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/parameter_server_strategy.py 285 204 28% 111-120, 131-136, 151-158, 165-168, 188-255, 270-305, 311, 314, 321, 332-344, 349, 353-367, 376, 385-390, 393, 398-456, 460, 464-471, 476-481, 489-491, 498-515, 518-529, 533-539, 542-544, 547-552, 557, 580-591, 594-612, 616, 620, 624, 628, 632, 635, 640, 644, 648, 652, 664 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/reduce_util.py 18 5 72% 42-51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/shared_variable_creator.py 37 28 24% 29-35, 63-97 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/summary_op_util.py 15 7 53% 40-48 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/tpu_strategy.py 450 334 26% 65-76, 81-85, 101-105, 151-161, 168-174, 199-204, 210, 266-270, 282-349, 359-374, 378-381, 384, 388, 398-405, 412, 417, 424-432, 439-444, 452-539, 544-545, 550-565, 569-577, 582-611, 615, 623, 627-668, 675-712, 715-735, 738-740, 743-745, 748, 751-770, 774-777, 782-792, 796-798, 802, 806, 810, 814, 818, 822, 825, 828-834, 841-843, 846-851, 863, 866-867, 870-957, 966, 975-977, 982-990, 994, 1001-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/tpu_values.py 143 89 38% 41-46, 51-61, 68-75, 78-81, 85-88, 92, 95-98, 101-104, 108, 114-118, 124, 127-129, 132-135, 138-141, 144-147, 151, 159-166, 171-183, 190-196, 200-202, 205-207, 210-212, 215, 218, 221, 224, 227, 230, 233, 236, 243-246, 251-254, 259-262, 266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/values.py 749 470 37% 48-55, 125, 129-133, 136, 142-151, 156, 160, 163-165, 168-170, 191-198, 203, 212, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300-304, 307-311, 314-318, 321-325, 335, 341, 352, 355, 359, 362-367, 370, 380, 383-387, 391-392, 396-397, 401-402, 416-433, 445-454, 458-465, 468, 472, 476, 480, 484, 488, 493, 497, 501, 505, 509, 513-518, 521, 525, 528, 531-532, 536, 540, 544, 547, 550, 557-560, 564, 567-568, 571, 575, 582-584, 591-595, 599-603, 607-612, 638-639, 643-644, 654-721, 728-729, 733-785, 789-790, 793-794, 797-798, 801-802, 805-806, 809-810, 813-814, 817-825, 828-836, 839-847, 851, 856, 859, 871-874, 880-881, 888, 896, 913-928, 935-939, 946-951, 959-960, 963-975, 978-990, 993-1004, 1007-1012, 1015-1018, 1023, 1026-1030, 1037-1040, 1052-1055, 1059-1060, 1067, 1085-1165, 1171-1181, 1187-1197, 1202-1230, 1245-1252, 1259-1264, 1267, 1271, 1274, 1277-1305, 1308-1309, 1312-1313, 1316-1317, 1321, 1324, 1328, 1332, 1335, 1338, 1342, 1346, 1350, 1354, 1358, 1362, 1366, 1370, 1374, 1378, 1381, 1384, 1387, 1390, 1393, 1396, 1399, 1402, 1405, 1408, 1411, 1414, 1417, 1420, 1423, 1426, 1429, 1432, 1435, 1438, 1441, 1444, 1447, 1450, 1453, 1456, 1459, 1462-1466, 1469-1473, 1476-1480, 1483-1487, 1490, 1493, 1497, 1500, 1507 /usr/local/lib/python3.8/dist-packages/tensorflow/python/dlpack/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/dlpack/dlpack.py 13 2 85% 45, 66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/backprop.py 415 331 20% 70-77, 87-97, 104-108, 111-115, 118, 144-159, 170, 226-249, 294-298, 303-326, 394-400, 424-432, 491-500, 539-572, 576-582, 589-612, 625-634, 639-647, 651, 659-681, 685-699, 715-717, 821-829, 833-834, 838-839, 843-852, 855-858, 861-867, 878-892, 917-924, 958-960, 964-966, 998-1056, 1108-1160, 1219-1281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/backprop_util.py 12 5 58% 26-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py 1014 426 58% 84-86, 89-95, 98, 101, 143-149, 170-172, 176-178, 332-335, 407, 440-451, 463, 498, 508, 511, 513, 522-523, 526-527, 534-536, 539, 560-572, 589-604, 620-623, 640, 653-656, 669-678, 705-721, 726, 732-735, 738-745, 771, 775, 815, 817, 822, 835, 840-857, 860-863, 867-868, 873-874, 884, 887, 891, 893, 896, 901, 904, 911, 921, 942, 963, 965-970, 972, 974-976, 990, 1009-1011, 1043, 1047-1048, 1088-1094, 1127-1128, 1139-1143, 1152, 1156, 1199, 1214-1247, 1253, 1259-1264, 1270-1296, 1300-1305, 1309-1328, 1332-1337, 1344, 1347-1349, 1354-1357, 1364, 1371, 1375-1377, 1381-1382, 1387-1389, 1397-1427, 1435-1437, 1441, 1445-1452, 1456, 1460-1467, 1471, 1475-1477, 1481, 1485-1493, 1498-1501, 1505-1513, 1519-1522, 1526-1534, 1539, 1544-1551, 1559-1560, 1564-1566, 1574-1575, 1579-1581, 1592-1599, 1607, 1610, 1631, 1637, 1648, 1659, 1677-1678, 1694-1699, 1721, 1731, 1791, 1856, 1861, 1897, 1931, 1941, 1947-1953, 1959-1965, 1970, 1977-1989, 2003-2009, 2026-2032, 2037, 2046, 2055, 2060, 2069, 2074, 2086, 2115-2127, 2131, 2135, 2139, 2143, 2178-2191, 2227, 2241 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/core.py 27 10 63% 29-33, 40-42, 45-46 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py 335 178 47% 64-66, 72, 154-255, 307, 313, 324-326, 452, 454-458, 492-496, 510, 518, 547-552, 564, 573-578, 589, 608-611, 620, 634-644, 652-708, 713, 721, 726-762, 786-804, 812-842, 862-881, 959-961, 965-988, 1214-1215 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py 150 99 34% 61-75, 109-133, 138-142, 150, 156, 160-163, 167-173, 179, 186, 194-195, 212-215, 218, 226, 231, 238-278, 282-284, 289-322 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/executor.py 24 11 54% 44, 47-56, 60, 63, 67, 71, 75-76 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/forwardprop.py 120 81 32% 48-49, 58-59, 89-140, 162-166, 304-314, 317-318, 321-322, 325-328, 331-334, 351-362, 382-393 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/forwardprop_util.py 22 5 77% 57, 74-78 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py 1204 606 50% 112-123, 151, 173-181, 204-230, 236-256, 263-274, 293-305, 322-346, 370, 381, 386, 396, 401, 417-419, 435-444, 482, 499-502, 535, 576, 583, 600, 664-671, 692-756, 760-795, 805, 832, 839-842, 859-860, 896-910, 917-1015, 1023-1110, 1117-1186, 1192-1205, 1230-1234, 1238-1312, 1328-1341, 1352-1356, 1383-1384, 1415-1443, 1487, 1531-1534, 1575, 1580, 1605, 1609-1645, 1691, 1704-1708, 1718-1736, 1753, 1775-1779, 1799, 1804, 1809, 1824, 1829, 1838, 1857, 1862-1868, 1894, 1903-1930, 1937-1946, 1963, 1982, 2022-2069, 2094, 2114, 2119, 2132, 2136, 2140, 2151-2153, 2180, 2182-2192, 2200, 2204-2205, 2220-2221, 2234-2239, 2255-2256, 2258, 2267-2313, 2341, 2425, 2429, 2444, 2451-2458, 2474, 2479-2491, 2510, 2515-2517, 2538-2568, 2593, 2614, 2618-2626, 2679-2709, 2741-2742, 2774, 2800-2806, 2812, 3202-3203, 3241-3242, 3246, 3250-3255, 3258-3261, 3266-3318, 3329, 3334-3335, 3353-3354 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/graph_only_ops.py 23 4 83% 34, 45-49 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/imperative_grad.py 15 5 67% 65-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/lift_to_graph.py 133 113 15% 37-39, 43, 84-139, 171-202, 237-359 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/monitoring.py 112 35 69% 119, 125-132, 137, 164, 200, 208, 212, 236, 248, 256, 260-263, 287, 311, 347, 355, 363-368, 380, 383, 402, 425, 430 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/profiler.py 68 37 46% 72-84, 99-108, 120-126, 136-142, 158-160, 174, 177, 180-181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/remote.py 76 53 30% 70-77, 136-219, 223 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/tape.py 67 26 61% 39, 42, 47-48, 53, 58, 63-70, 84, 97-109, 114, 155, 177, 184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/wrap_function.py 228 179 21% 49-52, 56, 60-83, 86, 90-94, 99-119, 124-142, 160-208, 223-229, 257-362, 377-389, 433-443, 447, 451, 501, 510-539, 599-603, 630-635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/dense_features.py 36 18 50% 92, 102, 111-113, 116, 137-150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/dense_features_v2.py 18 6 67% 82-87, 90-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/feature_column.py 877 622 29% 182-231, 297, 322-329, 332, 342, 346, 350, 354, 358, 362, 366, 492-504, 516-526, 543-548, 551-566, 569-577, 590-592, 595-601, 604, 608-612, 616, 633-661, 669, 672-700, 706-708, 744-752, 801-814, 904-929, 997-1009, 1083-1097, 1144-1155, 1242-1268, 1357-1385, 1444-1453, 1488, 1558-1560, 1670-1688, 1718-1722, 1731, 1734-1742, 1745, 1768, 1790, 1810, 1815, 1840, 1861, 1885, 1910, 1921-1931, 1947-1963, 1981, 2008, 2046-2067, 2085, 2128-2129, 2149-2171, 2191-2217, 2226-2233, 2254-2274, 2297-2323, 2335, 2339, 2346-2353, 2357, 2374-2378, 2388, 2392, 2395-2396, 2402, 2406-2409, 2418, 2423-2448, 2470, 2484-2486, 2490, 2493, 2497-2499, 2507-2531, 2540-2551, 2558-2575, 2580-2583, 2598-2600, 2604, 2608, 2611, 2615-2617, 2627-2681, 2690-2700, 2709-2724, 2730-2741, 2752, 2756, 2759-2780, 2786, 2790, 2803, 2807, 2810-2828, 2839, 2843, 2855, 2859, 2862-2880, 2890, 2894, 2907, 2911, 2914-2935, 2943, 2947, 2959, 2964-2969, 2973, 2976-2990, 2994-2997, 3008-3014, 3018-3024, 3027-3041, 3049, 3053, 3065-3071, 3086, 3100-3131, 3135, 3140, 3160-3175, 3181-3198, 3214-3221, 3236, 3240, 3243, 3247, 3251-3267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/feature_column_lib.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/feature_column_v2.py 1472 962 35% 213-214, 223-224, 233-234, 249-250, 261-262, 275-283, 293-319, 322-324, 327-329, 332-334, 348-364, 393-400, 407-414, 426, 429-432, 443-445, 449-450, 454-463, 468-475, 488-502, 509-542, 545-571, 575-586, 591-597, 705-706, 730, 734, 771-781, 831-843, 934-948, 1074-1141, 1257-1316, 1376-1388, 1464-1479, 1527-1538, 1626, 1717-1743, 1833-1861, 1924-1933, 1972-1977, 2048-2050, 2161-2180, 2204, 2228, 2248, 2276, 2297, 2306, 2316, 2330, 2386, 2389, 2413, 2417, 2430, 2454, 2459-2464, 2470-2478, 2488-2492, 2507, 2529, 2562-2572, 2596, 2639-2640, 2661-2683, 2703-2729, 2753-2773, 2796-2822, 2835, 2840, 2845, 2855, 2858-2864, 2869-2870, 2888-2889, 2894, 2900, 2916, 2921-2923, 2928, 2932-2936, 2941-2947, 2961, 2967, 2972, 2978, 2984-2985, 2991-2992, 2999, 3006, 3009, 3017-3018, 3023-3026, 3032, 3038, 3041-3065, 3069-3070, 3077-3080, 3085, 3089-3092, 3097-3102, 3127, 3141, 3147, 3152, 3158, 3162, 3167, 3172, 3178, 3182-3188, 3199-3217, 3227-3229, 3235-3246, 3264-3277, 3282-3296, 3301-3315, 3324-3341, 3347, 3351-3356, 3361-3370, 3374, 3390-3399, 3402, 3407-3424, 3428, 3448, 3458, 3463, 3468, 3472, 3476, 3479, 3484, 3489, 3496-3512, 3522-3532, 3535, 3539-3553, 3560, 3565, 3570-3581, 3593, 3598, 3603, 3609, 3613-3633, 3638-3640, 3645-3646, 3651, 3657, 3661, 3668-3670, 3675, 3679-3681, 3686-3689, 3702, 3707, 3712, 3718, 3722-3748, 3752-3754, 3759-3760, 3765, 3771, 3775, 3782-3784, 3789, 3793-3795, 3800-3803, 3817, 3822, 3827, 3833, 3837-3862, 3866-3868, 3873-3874, 3879, 3885, 3889, 3896-3898, 3903, 3907-3909, 3914-3917, 3930, 3935, 3940, 3946, 3950-3971, 3978-3980, 3985-3986, 3991, 3997, 4001, 4008-4010, 4015, 4019, 4024-4026, 4039, 4045, 4051-4056, 4062-4067, 4072, 4078, 4081-4094, 4098-4103, 4109-4111, 4115-4116, 4122-4125, 4130, 4134-4139, 4144-4150, 4162-4169, 4174-4180, 4185-4193, 4199, 4203-4218, 4227-4241, 4249, 4255, 4259, 4267-4269, 4274, 4278-4281, 4286-4293, 4305-4311, 4316-4324, 4329-4333, 4351, 4357, 4360-4385, 4402-4404, 4410-4411, 4417, 4423, 4428-4431, 4437, 4454-4466, 4471-4487, 4491-4506, 4517-4535, 4541, 4545-4549, 4554-4559, 4573-4582, 4598, 4604, 4609, 4615, 4619, 4625, 4630, 4636, 4639-4652, 4673-4675, 4681-4682, 4687, 4691-4695, 4700-4705, 4710-4711, 4730-4735, 4740-4741 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/sequence_feature_column.py 118 67 43% 101, 110, 113, 133-153, 176-202, 247, 293, 355, 418, 474-482, 492-499, 511, 516, 521, 537-540, 545, 556-576, 582, 586-588, 593-596 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/serialization.py 35 19 46% 84-90, 118-146, 166, 187-188, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/utils.py 64 47 27% 32-52, 56-57, 62-63, 94-122, 129-131, 135-137, 145-154 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/auto_control_deps.py 163 58 64% 171-175, 177-181, 184-186, 197, 244-268, 272, 275, 283, 329, 339, 342-349, 361, 366, 371-374, 377, 388, 466, 489-495 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/auto_control_deps_utils.py 82 21 74% 46-47, 53-54, 63, 67-69, 75, 121, 141-149, 166-169 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/c_api_util.py 100 27 73% 33, 38-39, 104, 107, 118-128, 133-134, 137, 140-146, 149-151, 154 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/common_shapes.py 35 26 26% 38-70, 84-86, 102-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/composite_tensor.py 26 9 65% 54, 71, 83-88, 107-112 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/composite_tensor_utils.py 57 41 28% 35, 43-47, 53-90, 97-110, 134-159 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/config.py 108 44 59% 37, 51, 64, 77, 91, 105, 121, 163, 178, 193, 209-219, 251-262, 275, 295-300, 406, 443, 472, 500, 541, 626, 632 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/constant_op.py 118 57 52% 39-46, 51-57, 62-65, 86-89, 93-94, 160, 273-292, 311-315, 337, 343-344, 347-350, 368-378 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/convert_to_constants.py 300 263 12% 56-70, 91-135, 147, 159, 179-185, 202-223, 248-302, 315-320, 330-335, 347-352, 366-374, 393-412, 447-650, 678-680, 705-708 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/cpp_shape_inference_pb2.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/device.py 57 8 86% 29, 42, 53, 55, 95, 117, 120, 169 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/device_spec.py 156 25 84% 46, 210, 333, 339, 344, 389, 403-404, 408-409, 413-414, 418-419, 423-424, 427-429, 432-436, 439-442, 453 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/dtypes.py 215 49 77% 79-82, 88, 95-101, 116-130, 140-154, 167-170, 200-201, 216, 628-629, 633-642 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/error_interpolation.py 202 168 17% 86-98, 119-140, 145, 168-189, 195, 214-223, 243-251, 256-257, 274-281, 299-337, 372-401, 416-425, 438-452, 467-484, 502-542 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/errors.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/errors_impl.py 210 72 66% 37-44, 82-84, 105, 110, 115, 119-163, 227, 246, 281, 316, 334, 349, 365, 382, 401, 419, 438, 454, 468, 485, 515, 520-524, 528-533, 545-546, 549-560 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/framework_lib.py 40 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/func_graph.py 504 152 70% 89-90, 95, 103-104, 115, 122, 214, 245, 251, 263, 293-319, 349-359, 390-391, 396, 419, 447, 452, 456, 468, 483, 508-528, 572, 581-582, 590, 626, 631-643, 662, 680-682, 686-690, 699, 704-705, 710-724, 728, 753, 769-772, 782, 786-793, 865, 873, 881, 891-893, 928, 933-934, 938-939, 966-970, 1004-1008, 1039-1045, 1064-1065, 1070, 1105-1113, 1169, 1173-1182, 1196, 1205, 1213-1216, 1226-1246, 1249, 1261 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/function.py 527 443 16% 123-128, 132-184, 198, 201-212, 270-304, 310-311, 316-329, 333-334, 338-340, 345, 350, 355, 360-361, 370-371, 375-376, 380-477, 489-493, 510-540, 544-560, 563-579, 617-625, 638-667, 670-678, 695-723, 730, 748-760, 785-805, 817-820, 832-838, 843, 847-873, 876-877, 881-902, 945-992, 1009-1043, 1076-1095, 1114-1133, 1151-1197, 1202-1211, 1217-1220, 1226-1262, 1273-1277, 1289-1293, 1305-1309, 1313-1315, 1346-1351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/function_def_to_graph.py 127 26 80% 112-119, 159, 172, 204, 207-218, 226, 228-231, 260, 266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/gpu_util.py 14 5 64% 51-57 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/graph_io.py 22 10 55% 60-75 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/graph_to_function_def.py 94 77 18% 30, 35-51, 56, 61, 65, 72-96, 105-119, 148-190 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/graph_util.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/graph_util_impl.py 310 269 13% 61, 81-109, 120-123, 128-131, 136-154, 159-160, 165-177, 197-218, 231-237, 259-302, 306, 343-525, 551-617 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/importer.py 208 87 58% 39, 61-73, 91-96, 110, 121-123, 132-135, 139-142, 155-175, 194-200, 212-222, 225-230, 280-285, 287, 318-333, 399-400, 470, 476-477, 499-501, 516-518, 526-529, 536 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py 150 82 45% 93-95, 100, 105, 110, 119-122, 127, 132, 137, 142, 147, 150, 156, 160-169, 177-183, 187, 220-227, 230, 235-242, 245-248, 251-258, 282, 310-319, 349-360, 386, 412-436 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/kernels.py 19 10 47% 29-33, 42-46 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/load_library.py 56 22 61% 70, 100, 105-120, 143-157 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/meta_graph.py 413 365 12% 71-103, 118-135, 150-171, 191-197, 209-219, 243-252, 268-278, 300-334, 352-365, 384-440, 445-454, 470-501, 540-608, 623-640, 691, 752-900, 959-1065, 1087-1099 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_callbacks.py 46 33 28% 105-115, 139-143, 148-149, 185-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py 384 157 59% 42, 49, 57, 73-77, 125-138, 142-145, 150, 154-155, 161, 168, 176-177, 193-198, 201-204, 209-211, 219, 221, 227-229, 250-251, 287-296, 303, 311-312, 323-325, 361-367, 383, 391, 394, 404, 417-444, 471-503, 515-516, 527, 533, 539-540, 544-545, 552-553, 568, 585-586, 604, 607-611, 620-622, 625-633, 642-644, 647, 650, 659, 665-669, 678, 682, 684, 686, 690, 702, 704, 708-711, 720-721, 732, 753-757 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_registry.py 27 1 96% 50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py 2194 763 65% 117, 120-130, 136, 151, 183, 186, 202, 238, 242, 246, 262-270, 289, 402, 440-444, 449, 515-517, 521, 525, 530, 535, 540-548, 551-557, 560-571, 577, 582, 591, 595, 656-658, 663, 692-697, 706, 711, 719, 723-729, 733-736, 748, 752, 778, 788, 814, 818, 859, 869, 872, 875, 878, 881, 884, 889, 892, 896, 900-901, 904, 908, 913-919, 922, 928-929, 974, 977, 992, 1004, 1017, 1020, 1024, 1029-1039, 1043-1055, 1066-1067, 1073, 1077, 1082, 1087, 1101, 1104-1105, 1112, 1122, 1127, 1131, 1135, 1139, 1143, 1147, 1215-1217, 1278, 1287, 1307, 1315, 1330-1332, 1336, 1347, 1351, 1357, 1396, 1436, 1491-1496, 1532-1543, 1568, 1574, 1622, 1631, 1642, 1655-1657, 1736, 1739, 1750, 1753, 1759, 1762, 1768, 1775-1784, 1853, 1857-1872, 1876, 1892, 1902, 1944, 1978-1979, 1992-1998, 2002-2005, 2009-2012, 2051-2057, 2075-2082, 2099, 2115-2117, 2124, 2136-2141, 2144, 2147, 2167-2173, 2189-2192, 2209-2212, 2257, 2262, 2282-2283, 2287-2290, 2294-2299, 2303-2305, 2310, 2338, 2343, 2346, 2348, 2354-2359, 2363-2367, 2371-2375, 2393, 2435, 2475, 2486-2497, 2502, 2522-2523, 2527, 2531, 2535, 2539, 2542-2549, 2602, 2604, 2607, 2609, 2635-2640, 2851, 2892, 2917, 2929-2933, 2947-2952, 2966, 2976, 2988, 3008-3018, 3048-3108, 3136-3137, 3159, 3180, 3256, 3304, 3368-3379, 3384, 3395, 3413-3423, 3435-3442, 3509, 3524, 3530, 3534, 3545-3546, 3552, 3557-3558, 3565, 3571, 3575-3600, 3616, 3638, 3660, 3686, 3708, 3773, 3789, 3810-3812, 3864, 3868-3877, 3891-3894, 3914-3919, 4027, 4032, 4122-4126, 4167, 4189, 4197, 4278, 4294, 4352-4357, 4423, 4428, 4441-4442, 4470-4471, 4588-4598, 4633, 4643, 4651, 4701-4726, 4775-4800, 4806, 4810, 4814, 4818-4821, 4852, 4856-4859, 4884, 4899, 4915, 4927, 4934, 4951-4953, 4957, 5014, 5039, 5051, 5054, 5091-5093, 5107, 5114, 5117, 5121-5124, 5141, 5168-5170, 5195, 5199, 5212, 5217, 5268, 5286, 5309-5326, 5344-5361, 5422-5424, 5439, 5443, 5530-5532, 5534-5540, 5552, 5657, 5672-5676, 5701-5749, 5776-5777, 5794, 5821, 5839-5841, 5854-5855, 5895-5926, 6072, 6128, 6152, 6186, 6227-6231, 6241, 6255, 6276, 6284-6290, 6297-6300, 6305, 6336, 6349-6351, 6354, 6357, 6415, 6421, 6461-6475, 6489-6502, 6511-6514, 6540, 6542, 6550-6553, 6566-6569, 6574, 6583-6601, 6605, 6651-6653, 6682-6687, 6730-6732, 6735, 6738-6742 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/random_seed.py 44 16 64% 38, 66, 69-79, 82, 88, 192-195, 346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/registry.py 33 3 91% 57-58, 71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/smart_cond.py 32 11 66% 47, 49, 54, 58, 75-79, 84-87, 117 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/sparse_tensor.py 195 109 44% 109-112, 129-182, 190, 200, 209, 214, 219, 224, 233, 238, 241, 264-267, 275, 284-287, 290, 315-316, 319, 324, 329, 333-335, 341-343, 346-350, 361, 364-365, 370-374, 379-401, 404, 409-411, 414, 417, 420, 424-432, 461-470, 485 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_conversion_registry.py 44 3 93% 100, 102, 106 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_like.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_shape.py 375 190 49% 93-94, 126, 172-176, 190, 196-197, 202, 205, 208-209, 215-216, 219, 223-229, 232, 237, 241, 275, 337-344, 355, 379-386, 397-401, 425-433, 444, 468-475, 486-490, 506, 521, 537, 553, 578-582, 593-594, 615-619, 640-644, 665-669, 690-694, 697, 767-769, 776, 780-786, 789-800, 807, 829, 841-847, 867, 872-897, 901-907, 927, 935-936, 939-941, 944-946, 965-969, 984, 996-997, 1014-1017, 1033-1036, 1052-1055, 1098, 1101, 1117, 1141, 1146, 1160-1161, 1188-1192, 1196-1204, 1207, 1210, 1235, 1237, 1241 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_spec.py 143 57 60% 61, 79, 84, 92, 98, 102, 108, 111, 114, 117, 142, 146-151, 158-159, 162, 167, 181-183, 186, 191-193, 236-262, 266-269, 274, 279, 282-283, 287-288, 292, 295, 299 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_util.py 480 358 25% 39-40, 45, 49, 55, 61, 66, 71, 124-159, 190, 199-202, 215, 222-225, 229-234, 248, 253-258, 310-321, 325-330, 339, 344, 351, 415, 417, 429, 433-436, 439, 449, 457, 464, 467-470, 476, 481, 485, 494-511, 520-524, 532-549, 556, 591-642, 659-666, 671-794, 821-832, 854-975, 1008, 1052-1054 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py 1247 1010 19% 92, 101, 106-109, 125-136, 156, 181, 187-205, 211-244, 253-263, 270-276, 280, 284, 288, 292, 296, 300, 304, 317-323, 342-358, 376-390, 403-409, 423-435, 440, 445, 464-472, 519-527, 542-546, 559-569, 590-652, 674-714, 719-817, 834-892, 910-917, 936-950, 955-965, 985-1017, 1023-1032, 1099-1144, 1150-1166, 1191-1203, 1227-1252, 1260-1268, 1290-1321, 1343-1358, 1376-1391, 1409-1424, 1438-1459, 1495-1510, 1516-1521, 1527-1528, 1534-1535, 1541-1542, 1549, 1553-1555, 1583, 1601-1612, 1619-1627, 1643-1672, 1678-1710, 1716-1734, 1752-1761, 1767-1786, 1792-1816, 1822, 1836-1848, 1851-1870, 1873-1876, 1879-1881, 1897-1899, 1935-1945, 1959-1960, 1973-1985, 1995-1997, 2007-2010, 2013-2034, 2037-2039, 2050-2058, 2098-2103, 2148-2155, 2166-2183, 2204-2211, 2215-2218, 2226, 2235-2238, 2249, 2265-2271, 2289-2291, 2308, 2325-2327, 2330, 2342, 2346-2353, 2356-2397, 2407-2468, 2494-2496, 2529-2544, 2559-2563, 2574-2610, 2621-2625, 2636-2637, 2648-2649, 2660-2661, 2672-2673, 2696-2704, 2732-2759, 2779-2784, 2796-2800, 2822-2841, 2847, 2861-2865, 2876-2878, 2884-2894, 2898-2905, 2908-2915, 2918-2925, 2942-2958, 2963-2997, 3005-3031, 3092-3120, 3135-3138, 3145-3149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/traceable_stack.py 46 5 89% 61, 70-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/type_spec.py 196 89 55% 86, 98-102, 123, 145, 161, 177, 215-216, 229, 254, 269, 275, 278, 285, 289, 293, 298-304, 317, 319, 321, 328-339, 346-348, 353-363, 395, 398, 402-405, 410, 412, 415-417, 448, 457, 461-464, 480-494, 512-520, 526 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/versions.py 28 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/grappler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/grappler/cluster.py 47 24 49% 49-57, 60-62, 65, 69, 73-75, 80, 83, 86, 95-100, 110, 119-122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/grappler/tf_optimizer.py 15 6 60% 50-59 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/__init__.py 17 1 94% 38 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/activations.py 75 30 60% 70-78, 98, 156, 169, 182, 195, 235, 257, 287, 309, 335, 382-385, 418, 458-467 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/activations/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/__init__.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/densenet/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/imagenet_utils/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/inception_resnet_v2/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/inception_v3/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/mobilenet/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/mobilenet_v2/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/nasnet/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/resnet/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/resnet50/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/resnet_v2/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/vgg16/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/vgg19/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/applications/xception/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/backend/__init__.py 153 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/callbacks/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/constraints/__init__.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/datasets/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/datasets/boston_housing/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/datasets/cifar10/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/datasets/cifar100/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/datasets/fashion_mnist/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/datasets/imdb/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/datasets/mnist/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/datasets/reuters/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/estimator/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/experimental/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/initializers/__init__.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/layers/__init__.py 123 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/layers/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/layers/experimental/preprocessing/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/losses/__init__.py 52 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/metrics/__init__.py 76 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/mixed_precision/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/mixed_precision/experimental/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/models/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/optimizers/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/optimizers/schedules/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/preprocessing/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/preprocessing/image/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/preprocessing/sequence/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/preprocessing/text/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/regularizers/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/utils/__init__.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/wrappers/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v1/keras/wrappers/scikit_learn/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/__init__.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/activations/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/__init__.py 35 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/densenet/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/imagenet_utils/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/inception_resnet_v2/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/inception_v3/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/mobilenet/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/mobilenet_v2/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/nasnet/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/resnet/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/resnet50/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/resnet_v2/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/vgg16/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/vgg19/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/applications/xception/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/backend/__init__.py 148 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/callbacks/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/constraints/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/datasets/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/datasets/boston_housing/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/datasets/cifar10/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/datasets/cifar100/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/datasets/fashion_mnist/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/datasets/imdb/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/datasets/mnist/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/datasets/reuters/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/estimator/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/experimental/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/initializers/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/layers/__init__.py 118 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/layers/experimental/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/layers/experimental/preprocessing/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/losses/__init__.py 48 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/metrics/__init__.py 71 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/mixed_precision/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/mixed_precision/experimental/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/models/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/optimizers/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/optimizers/schedules/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/preprocessing/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/preprocessing/image/__init__.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/preprocessing/sequence/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/preprocessing/text/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/regularizers/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/utils/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/wrappers/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/_v2/keras/wrappers/scikit_learn/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/__init__.py 30 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/activations/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/__init__.py 38 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/densenet/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/imagenet_utils/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/inception_resnet_v2/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/inception_v3/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/mobilenet/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/mobilenet_v2/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/nasnet/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/resnet/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/resnet50/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/resnet_v2/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/vgg16/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/vgg19/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/applications/xception/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/backend/__init__.py 153 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/callbacks/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/constraints/__init__.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/datasets/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/datasets/boston_housing/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/datasets/cifar10/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/datasets/cifar100/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/datasets/fashion_mnist/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/datasets/imdb/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/datasets/mnist/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/datasets/reuters/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/estimator/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/experimental/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/initializers/__init__.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/layers/__init__.py 123 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/layers/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/layers/experimental/preprocessing/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/losses/__init__.py 52 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/metrics/__init__.py 76 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/mixed_precision/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/mixed_precision/experimental/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/models/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/optimizers/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/optimizers/schedules/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/preprocessing/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/preprocessing/image/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/preprocessing/sequence/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/preprocessing/text/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/regularizers/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/utils/__init__.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/wrappers/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/api/keras/wrappers/scikit_learn/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/densenet.py 124 85 31% 67-69, 83-95, 109-125, 191-313, 325, 338, 351, 368, 388 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/imagenet_utils.py 166 138 17% 100-109, 136-155, 178-229, 252-284, 313-376, 389-398, 413-421 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/inception_resnet_v2.py 142 119 16% 103-241, 268-283, 326-371, 387, 406 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/inception_v3.py 169 146 14% 111-359, 385-404, 420, 439 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/mobilenet.py 132 108 18% 158-307, 346-358, 407-437, 453, 472 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/mobilenet_v2.py 172 148 14% 173-409, 414-482, 486-492, 497, 502 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/nasnet.py 197 164 17% 149-318, 374, 442, 474-515, 532-600, 615-679, 694-779, 795, 814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/resnet.py 173 136 21% 123-214, 232-259, 275-278, 296-327, 343-347, 372-416, 433-441, 456-462, 477-483, 498-504, 522, 543 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/resnet_v2.py 43 20 53% 38-44, 71-77, 104-110, 138, 158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/vgg16.py 74 52 30% 107-216, 232, 252 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/vgg19.py 77 55 29% 112-226, 242, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/applications/xception.py 119 97 18% 112-310, 326, 345 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/backend.py 2044 1480 28% 166, 197, 301, 321-329, 350, 365, 384-391, 412-437, 455-468, 473, 479-498, 524-528, 559-572, 583, 587-595, 614, 618-620, 623, 634-637, 653-657, 666-673, 687-689, 709, 722, 771, 831-851, 856-859, 866-867, 914-915, 921-925, 930-951, 967-970, 1016-1021, 1063, 1068, 1070-1071, 1074, 1076-1084, 1102-1103, 1106-1107, 1136-1154, 1178, 1207-1208, 1233-1236, 1290, 1327-1334, 1361-1368, 1394-1397, 1424, 1449, 1462, 1487-1495, 1521-1529, 1552, 1580, 1588, 1602, 1616, 1631-1632, 1670-1696, 1744-1884, 1915, 1947, 1968, 1986, 2004, 2022, 2036, 2050, 2068-2070, 2094-2096, 2114-2116, 2131-2132, 2147-2148, 2162, 2176, 2189, 2202, 2215-2218, 2231, 2244, 2265, 2280, 2293, 2307, 2322-2330, 2344, 2358, 2372, 2386, 2400, 2414, 2438, 2452, 2465, 2478, 2499-2501, 2522-2544, 2565-2579, 2598-2609, 2633-2669, 2698-2702, 2705, 2707, 2738, 2769, 2791-2832, 2853-2864, 2890-2925, 2957-2960, 2993-2998, 3013-3015, 3040, 3064-3065, 3079, 3093, 3108-3110, 3129-3141, 3169-3185, 3211, 3230, 3245-3247, 3295-3310, 3326-3333, 3351-3371, 3386-3409, 3439-3446, 3473-3525, 3540-3574, 3577-3579, 3589-3592, 3595-3644, 3659-3768, 3772-3798, 3821-3834, 3848, 3865-3867, 3944-4264, 4285-4328, 4350-4373, 4394, 4421-4450, 4464-4468, 4483, 4496, 4509, 4513-4515, 4561-4583, 4607-4664, 4681-4700, 4713, 4730-4735, 4748, 4766-4768, 4782, 4799, 4815-4821, 4838-4844, 4857-4863, 4878-4884, 4911-4934, 4961-4977, 5008-5050, 5078-5117, 5148-5174, 5202-5223, 5250-5266, 5294-5325, 5355-5384, 5412-5437, 5477-5514, 5543-5544, 5585, 5612-5631, 5657-5661, 5681-5685, 5706-5710, 5734-5738, 5760-5790, 5813-5822, 5856-5873, 5892, 5909, 5926, 5932, 5941-5942, 5955-5960, 5963-5974, 5980-6019, 6024, 6030-6035, 6039, 6045-6062, 6067-6070, 6124-6127, 6140, 6166, 6176 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/backend_config.py 30 2 93% 108, 146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/callbacks.py 1035 758 27% 63-64, 96-126, 152-174, 179, 185-191, 244-247, 250-251, 265, 269, 285-304, 311-316, 320-325, 328-330, 333-335, 347-350, 363-365, 377-379, 388-390, 400-402, 411-413, 424-425, 435-436, 445-447, 456-458, 467-469, 478-480, 505, 601, 615, 771-772, 775-776, 779-793, 796-803, 812-817, 839-855, 858-865, 869, 872-873, 876, 879-881, 884, 887-888, 891, 894, 897-898, 901, 904-905, 909-928, 931-935, 952, 955-962, 1046-1100, 1103-1108, 1112-1140, 1145-1155, 1158-1163, 1166, 1169-1182, 1191-1231, 1238-1257, 1264-1267, 1315-1358, 1362, 1427-1457, 1461-1466, 1469-1485, 1488-1489, 1492-1498, 1529-1535, 1538-1559, 1594-1608, 1612-1613, 1709-1739, 1743-1765, 1770-1801, 1806-1840, 1847-1850, 1865-1869, 1873-1886, 1890-1899, 1905-1909, 1928-1947, 1950-1952, 1955, 1968-1983, 1988-1990, 1993, 1997-2003, 2006-2018, 2026-2030, 2038-2039, 2046-2054, 2062-2063, 2073-2106, 2110-2122, 2126-2146, 2149-2151, 2200-2220, 2225-2237, 2240, 2243-2271, 2274, 2299-2311, 2314-2321, 2326-2363, 2366-2367, 2431-2456 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/callbacks_v1.py 218 186 15% 130-163, 167-175, 180-230, 235-310, 313-314, 325-343, 351-367, 370-372, 378-385, 394-451, 454-457 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/constraints.py 102 47 54% 38, 41, 68-69, 72-75, 78, 87, 109, 112, 118, 152-155, 158-163, 166, 204-216, 221-249, 268, 273, 284-292 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/boston_housing.py 25 16 36% 58-79 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar.py 19 12 37% 37-51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar10.py 31 19 39% 50-82 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar100.py 27 15 44% 59-84 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/fashion_mnist.py 26 15 42% 66-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/imdb.py 56 42 25% 99-158, 171-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/mnist.py 15 6 60% 57-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/reuters.py 47 33 30% 106-151, 164-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/distributed_training_utils.py 473 386 18% 68-80, 113-137, 142-166, 193-213, 236, 253-272, 308-322, 343-359, 363-366, 372-375, 381-399, 404-410, 424-426, 435, 447-449, 459-469, 502-573, 577-581, 585-588, 592-595, 600-620, 636-669, 684-689, 694-703, 732-773, 779-783, 790-825, 830-835, 840-849, 854-880, 885-902, 907-913, 918-947, 953-983, 993-1025, 1033-1040, 1045-1049, 1054-1062, 1066-1071, 1075-1076, 1080-1081, 1085-1086, 1090-1091, 1095-1096, 1101-1102, 1121-1133, 1137, 1151-1169, 1176-1192, 1197-1201 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/multi_worker_training_state.py 85 54 36% 41-45, 50-53, 57, 61-62, 66-69, 81-108, 118-141, 150-164, 172-177, 197-206, 219-221, 226-227 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer.py 1150 508 56% 367, 370, 433-438, 494, 499, 509, 513, 520, 529, 538-542, 548-558, 587-592, 617-637, 655, 673-699, 723-737, 754-758, 789, 796-799, 813-820, 830-831, 840, 848, 869, 889, 929-942, 945, 952, 960-972, 998, 1011-1013, 1018, 1023, 1067, 1119-1135, 1148-1166, 1237-1247, 1258-1268, 1274, 1282, 1285-1289, 1303-1308, 1327-1378, 1407-1464, 1510-1544, 1581-1588, 1600-1608, 1620-1628, 1644-1648, 1664-1668, 1686-1690, 1708-1712, 1731, 1751, 1770, 1789, 1807, 1827, 1847-1855, 1874-1878, 1901, 1908, 1948, 1955, 1972, 1977, 1986, 1988, 1999-2000, 2046-2048, 2052, 2062-2082, 2096-2097, 2111-2118, 2124-2147, 2150-2184, 2191-2193, 2196-2197, 2206-2216, 2243, 2248-2250, 2256, 2261, 2264, 2270, 2280, 2286-2291, 2302-2303, 2382, 2385, 2401-2406, 2424-2431, 2434-2444, 2463-2467, 2524-2527, 2529-2532, 2541, 2550-2551, 2586, 2597, 2606, 2610-2612, 2628, 2635-2642, 2646-2652, 2663, 2691, 2734, 2738, 2742, 2745, 2749, 2757-2760, 2763-2766, 2809-2825, 2828-2830, 2833-2839, 2842-2869, 2874, 2877-2886, 2899-2901, 2904-2905, 2908-2910, 2922-2924, 2927-2928, 2931-2936 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py 265 109 59% 46-48, 110, 113-114, 154-157, 165, 186-187, 207-258, 263-269, 315, 317, 325, 356, 358, 362-364, 458, 466-468, 484, 515-609, 633, 638, 645, 689, 700, 707, 723-757, 761, 764-769, 772, 775-778 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py 973 742 24% 161-255, 273-275, 288, 303-308, 363-469, 487-506, 524, 542-567, 591-605, 621-629, 656-826, 830, 834, 841, 847, 852, 856, 860-862, 867, 872, 876, 883-887, 891-895, 899-908, 917, 921-937, 950-961, 1031-1076, 1080-1084, 1104-1152, 1181-1223, 1269-1303, 1340-1347, 1358-1366, 1377-1385, 1400-1404, 1419-1423, 1440-1444, 1461-1465, 1483, 1502, 1520, 1538, 1555-1558, 1575-1577, 1595-1603, 1621-1630, 1647-1655, 1667, 1673, 1695, 1702, 1713, 1717, 1721, 1729-1752, 1767, 1781-1801, 1806-1826, 1836, 1840-1841, 1844, 1847-1852, 1855-1862, 1865-1899, 1904-1914, 1920-1931, 1934-1971, 1975-1985, 1988-1996, 1999-2006, 2012-2030, 2044-2070, 2096-2107, 2111-2139, 2142-2150, 2158-2164, 2168-2172, 2177-2179, 2195-2196, 2204-2246, 2249-2321, 2324-2333, 2340-2347, 2351-2357, 2362, 2368, 2372-2379, 2387, 2392-2396, 2401, 2406, 2411-2417, 2423, 2427, 2431, 2434, 2438, 2446-2448, 2451-2453 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_preprocessing_layer.py 114 66 42% 59, 75-78, 103-116, 120-123, 127, 140-197, 213-218, 223-257, 284, 304, 320, 332, 354, 368, 382 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_preprocessing_layer_v1.py 26 14 46% 51-54, 58-61, 66-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/compile_utils.py 318 253 20% 41-44, 63-68, 85-98, 101, 104, 126-132, 136-149, 153-161, 183-247, 261-269, 272, 275, 295-297, 301-335, 342-370, 375-383, 387-416, 420-421, 434-477, 481-484, 488-490, 495, 500, 528-545, 575-592, 597-602, 607-617, 623-632 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/data_adapter.py 639 339 47% 56-57, 61-62, 115, 140, 156, 172, 185, 198, 203, 211, 216, 220-225, 229, 241, 249, 264-371, 388-408, 411, 414, 417, 420, 423, 427, 452, 467, 470-476, 493-523, 533, 539, 542, 546-548, 562-593, 596, 599, 602, 605, 608, 611, 622, 628, 630, 641-650, 660, 663, 666, 669, 672, 675, 691-699, 702, 705, 708, 711, 714, 720, 726-735, 762, 765, 779-780, 789, 801-802, 823, 830-831, 836-849, 855, 858, 861, 864, 867, 870, 891, 894, 917-922, 925-932, 944, 960, 965, 975-984, 1009-1011, 1028-1059, 1068, 1117, 1126, 1138-1147, 1162, 1184, 1190-1208, 1212, 1229-1268, 1301-1342, 1348, 1353-1356, 1366, 1375-1387, 1392-1399 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/input_layer.py 92 29 68% 100-104, 107-113, 115, 125-127, 138, 140, 146, 159-165, 180-187, 191, 268, 277, 280-281, 287, 290, 303 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/input_spec.py 100 45 55% 66-67, 76-77, 80-83, 87-93, 96, 106, 121-129, 155, 161, 167, 174-176, 182-184, 191, 199-200, 210, 212, 219-224, 232-236 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/network.py 939 481 49% 73-74, 78-79, 224, 254, 358-360, 383, 392, 405-408, 417-421, 426, 431-435, 447-451, 463, 468, 471-473, 486-491, 518-525, 551-556, 559, 563, 622-693, 714, 722-798, 830, 863, 869-874, 881-882, 910-913, 921-926, 945, 947, 953-954, 966-968, 986-991, 1051, 1113-1169, 1223, 1230-1235, 1237-1249, 1251, 1254, 1261, 1263, 1274-1283, 1298-1299, 1322-1325, 1346, 1360, 1368-1369, 1378-1379, 1390, 1400-1405, 1409-1410, 1433-1497, 1522-1525, 1553, 1559, 1565-1573, 1576-1582, 1587, 1591, 1603-1606, 1612, 1678, 1741-1744, 1787, 1802, 1818-1821, 1827, 1833-1847, 1852-1857, 1863-1877, 1883-1893, 1912-2056, 2069-2157 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/node.py 51 9 82% 76, 120-122, 158-159, 176-177, 182-184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/partial_batch_padding_handler.py 62 46 26% 34-36, 40-53, 58-62, 66-87, 91-111 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/sequential.py 196 102 48% 126, 167-169, 172, 190-199, 204, 215, 226-227, 241-255, 262-266, 270-299, 302-305, 311-312, 329-335, 359-363, 366-383, 387-403, 407-409, 413, 417-422 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py 459 256 44% 65-72, 86, 171, 196-197, 246-248, 347, 398-410, 450, 472, 480-494, 498, 527-544, 566-582, 785-881, 906-915, 936-952, 1039-1098, 1143, 1146-1151, 1273, 1290-1291, 1340-1359, 1400-1417, 1434-1439, 1464-1465, 1497-1500, 1525-1526, 1542, 1544, 1548-1549, 1559, 1568, 1572, 1576, 1581-1584, 1600, 1624-1627, 1634-1635, 1641, 1645, 1649-1660, 1663-1669, 1677-1688, 1691, 1701, 1721-1729, 1737, 1739, 1744, 1750-1760, 1785-1814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_arrays.py 259 222 14% 42-43, 126-458, 462-467, 471-477, 482-484, 501-535, 539-542, 546-550, 555-557, 564-583, 621-649, 678-687, 705-708 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_distributed.py 322 282 12% 46-47, 52-56, 73-120, 164-290, 315-420, 444-574, 599-672, 698-720, 737-754, 766-779, 786, 789, 793, 798 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_eager.py 119 98 18% 37-39, 55-82, 114-219, 250-283, 308-322, 349-366 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_generator.py 239 198 17% 123-336, 349-362, 398-418, 449-484, 493-509, 514-530, 535-538, 571-574, 604-606, 626-627, 659-666, 692-695, 706-707, 738-766, 791-800, 816-819 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_utils.py 834 682 18% 79-83, 92, 105, 110, 124, 131, 135-140, 143-145, 156-157, 161, 168-174, 178-192, 209-212, 241-244, 253-258, 262-286, 289-300, 303-310, 321-342, 345-347, 350-353, 358-364, 385-400, 428-436, 441-457, 486-583, 601-626, 633, 638, 657-697, 713-750, 771-808, 844-891, 907-915, 944-1042, 1046-1048, 1052-1056, 1069-1091, 1106-1133, 1142-1157, 1162-1186, 1210-1213, 1216-1228, 1233-1235, 1256-1270, 1278-1289, 1297-1306, 1333-1355, 1359-1364, 1380-1391, 1405, 1420-1421, 1435-1461, 1482-1503, 1525-1544, 1552, 1556, 1584-1613, 1639-1670, 1682-1689, 1694, 1700-1705, 1709-1711, 1723-1725, 1737-1759, 1787-1817, 1827-1840, 1850, 1857-1887, 1891-1892, 1896, 1923-1925, 1929, 1947, 1951, 1965-1970, 1975-1977, 1997-2007, 2012-2030, 2049-2078, 2110, 2123, 2133 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_v1.py 1067 853 20% 71-72, 143-163, 167, 175-180, 228-233, 301-463, 475-476, 481-486, 494-506, 522-545, 549, 556-586, 754-766, 874-879, 953-957, 970-976, 1025-1070, 1110-1146, 1166-1192, 1217, 1249-1251, 1276, 1288-1299, 1313-1337, 1348-1350, 1362-1380, 1386-1423, 1427-1437, 1460-1468, 1471-1478, 1494-1515, 1528, 1532, 1546-1627, 1632-1642, 1646-1650, 1673-1742, 1747-1756, 1760-1768, 1791-1799, 1805, 1818-1827, 1831-1856, 1876-1882, 1914-1938, 1949-1953, 1959-2003, 2006-2030, 2033-2041, 2049-2057, 2099-2170, 2242-2302, 2312-2436, 2440-2498, 2501-2534, 2572-2591, 2596-2636, 2643-2647, 2652, 2660, 2668, 2676, 2684, 2692, 2696-2702, 2706, 2710, 2714, 2732-2735, 2743-2746, 2753-2754, 2774-2775, 2781-2787, 2791, 2794-2802, 2806, 2813-2814, 2817, 2820, 2825-2829, 2832-2835, 2842-2846, 2886-2893, 2897, 2901, 2905, 2909, 2913, 2917, 2921, 2925, 2942-2972, 2979, 2983, 2987, 2991, 2995, 2999, 3002, 3005, 3009, 3012, 3016-3018, 3023-3042, 3048, 3054-3075, 3097-3099, 3103, 3107, 3111, 3115, 3135-3151, 3165-3175, 3179-3180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/estimator/__init__.py 23 12 48% 115-122, 212-219 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/initializers.py 89 8 91% 93, 118, 142, 166, 181, 194, 202, 207 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/__init__.py 180 21 88% 54-59, 157-159, 214-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/advanced_activations.py 135 78 42% 68-70, 73, 76-78, 82, 126-136, 140-157, 160-162, 165-172, 176, 203-205, 208, 211-213, 217, 244-246, 249-250, 253-255, 259, 279-281, 284, 287-289, 293, 345-358, 363, 369-375, 379 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py 730 587 20% 121-149, 152-191, 194-222, 225-250, 254-273, 277-282, 285-288, 291-295, 298-304, 319-325, 429, 582, 726, 874-898, 903-934, 937-994, 997-1027, 1030-1032, 1167-1190, 1195-1226, 1229-1295, 1298-1332, 1335-1338, 1430-1453, 1456-1496, 1499, 1502-1543, 1656, 1680-1719, 1841, 1866-1887, 1985-2001, 2004-2036, 2039-2056, 2060-2078, 2081-2093, 2134-2136, 2139-2141, 2144-2145, 2148-2150, 2216-2223, 2226-2239, 2243, 2248-2254, 2302-2305, 2308-2325, 2329, 2333-2335, 2384-2386, 2389-2393, 2396, 2399-2401, 2472-2492, 2495-2516, 2520, 2524-2526, 2584-2610, 2613-2642, 2646, 2650-2652, 2692-2694, 2697-2702, 2705-2708, 2711-2713, 2768-2788, 2791-2802, 2813-2833, 2838-2840, 2899-2925, 2928-2958, 2964-3014, 3020-3022 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional_recurrent.py 345 261 24% 165-181, 185-222, 228-275, 279-292, 302-350, 353-420, 510-537, 541-584, 587-644, 647-654, 657-660, 663-692, 842-869, 872-873, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 963-995, 999 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/core.py 458 303 34% 101-104, 107, 110-115, 118, 121-123, 179-183, 189-196, 199-212, 215, 218-224, 260-261, 264-266, 311-318, 321-325, 369-376, 379-383, 415-417, 420, 423, 426-428, 472-473, 495-515, 518-527, 530, 534-536, 571-578, 581-586, 589, 592-594, 630-632, 635-667, 670-679, 682-684, 714-716, 719-720, 723, 726-728, 825-841, 845-870, 874-890, 893-932, 937, 940-942, 945-966, 969-985, 989-1016, 1022-1048, 1129, 1150, 1154, 1176, 1183-1188, 1192, 1199, 1202-1208, 1211-1225, 1246-1250, 1253, 1256-1258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/cudnn_recurrent.py 189 143 24% 65-81, 84-121, 124-133, 137, 141-143, 147-149, 153, 156, 215-236, 240, 243-269, 272-316, 319-337, 400-422, 426, 429-465, 468-518, 521-540 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/dense_attention.py 132 95 28% 77-80, 92, 119-134, 138-167, 170-176, 180-196, 201-206, 307-308, 312-321, 332-335, 338-340, 443-444, 447-460, 473-480, 484-486, 491-495, 499-503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/embeddings.py 71 46 35% 101-123, 133-148, 151-154, 158-178, 181-185, 188-203 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/kernelized.py 80 56 30% 138-154, 157-200, 203-207, 210-216, 219-228, 234-251, 255-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/local.py 209 172 18% 151-171, 175-259, 263-274, 277-298, 301-334, 465-485, 489-581, 585-600, 603-625, 628-661, 704-724, 770-778, 807-816, 834-841 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/merge.py 342 244 29% 49, 67-87, 92-118, 122, 124-181, 187-202, 205-217, 251-254, 284-286, 290-293, 320-323, 357-360, 387-390, 417-420, 493, 496, 504-519, 526-536, 541-564, 567-571, 639-654, 659-674, 680-700, 704-722, 725, 728-733, 767, 796, 810, 846, 878, 892, 927, 947 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/noise.py 80 47 41% 60-62, 66-73, 76-78, 82, 111-113, 116-127, 130-132, 136, 170-174, 177, 180-203, 206-208, 212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization.py 511 442 14% 199-247, 258-271, 275-279, 283, 287-289, 292-295, 300-303, 306, 311-503, 506-516, 519-521, 525-635, 640-693, 696, 699-708, 711-722, 725-890, 893, 896-928, 1010-1034, 1042-1055, 1058-1105, 1109-1193, 1196, 1199-1212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization_v2.py 43 23 47% 136, 161-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/pooling.py 256 162 37% 60-70, 73-81, 84-98, 101-108, 193, 235, 272-282, 285-297, 300-315, 319-326, 458, 508, 544-554, 557-574, 577-596, 600-607, 654, 704, 714-717, 720-724, 727, 730-732, 775-777, 780-789, 792, 840-841, 849-852, 855-859, 862, 865-867, 906-909, 947-950, 957-960, 963-967, 970, 973-975, 1008-1011, 1043-1046 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding.py 218 164 25% 83-129, 136, 139-145, 163-170, 173-178, 181-187, 190-193, 196-205, 208-217, 220-234, 237-292, 317-318, 322-341, 345-364, 379, 394-412, 416, 421-429, 433-447, 452-458 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding_v1.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py 485 384 21% 78-83, 86-89, 92-96, 99-100, 104-110, 135-138, 141-144, 147-171, 174-175, 179-184, 214-219, 222-277, 280-281, 285-291, 315-316, 319-320, 323, 326-330, 367-383, 386-402, 405, 408-413, 467-506, 509-541, 544, 547-555, 570-578, 647-670, 696-704, 768-790, 793-816, 819, 822-829, 879-918, 921-952, 955, 958-966, 987-997, 1043-1054, 1057-1067, 1070, 1073-1078, 1118-1132, 1135-1157, 1160-1161, 1165-1171, 1213-1227, 1230-1252, 1255-1256, 1260-1266, 1270-1273, 1277-1282 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup.py 230 173 25% 104-190, 193-208, 211-212, 215, 218-222, 225-231, 234-235, 239-241, 245-246, 250-257, 260, 263-268, 283-285, 288-294, 297-305, 312, 333-353, 356-358, 361-389, 392-401, 404-429, 451, 455-465, 469-477, 489-495, 499, 504-507, 511-518, 523-524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup_v1.py 38 22 42% 63-66, 69, 72-76, 79-81, 84-85, 89-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization.py 95 56 41% 63-71, 75-104, 109-111, 114, 117, 120-122, 126-128, 149, 155-173, 178-197, 202, 212-220, 225-230, 234-235, 241 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization_v1.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py 309 235 24% 214-320, 325, 328-329, 332, 336-338, 342-343, 347, 350-359, 362-364, 379-404, 407, 410-420, 427, 461-521, 528-534, 537-543, 546-592, 595-633, 663-665, 669-694, 698-715, 730, 745-762, 766, 771-781, 785-802, 807-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization_v1.py 26 9 65% 84, 87, 91-97 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent.py 1046 802 23% 85-104, 108, 113-118, 121-131, 135-160, 165-180, 183-191, 195-200, 397-439, 444-447, 454, 457-505, 513-519, 522-592, 606-621, 625-645, 648-707, 717-814, 820-854, 857-866, 871-873, 890-943, 946-965, 969-974, 978, 1052, 1061, 1066, 1069, 1096-1099, 1110, 1121, 1124, 1131, 1153-1156, 1174-1177, 1266-1290, 1294-1319, 1322-1340, 1343, 1346-1378, 1489-1526, 1529-1530, 1535, 1539, 1543, 1547, 1551, 1555, 1559, 1563, 1567, 1571, 1575, 1579, 1583, 1587, 1590-1625, 1629-1631, 1709-1740, 1744-1778, 1781-1879, 1882-1907, 1910, 2032-2071, 2074-2075, 2080, 2084, 2088, 2092, 2096, 2100, 2104, 2108, 2112, 2116, 2120, 2124, 2128, 2132, 2136, 2140, 2144, 2147-2188, 2192-2194, 2275-2312, 2316-2353, 2357-2367, 2371-2376, 2379-2436, 2439-2477, 2480, 2518-2530, 2536-2549, 2552-2559, 2678-2717, 2720-2721, 2726, 2730, 2734, 2738, 2742, 2746, 2750, 2754, 2758, 2762, 2766, 2770, 2774, 2778, 2782, 2786, 2790, 2793-2834, 2838-2840, 2844-2852, 2877-2913, 2918, 2923-2926, 2931-2944, 2964-2991, 3008-3011 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent_v2.py 321 259 19% 165, 345-385, 388-398, 403-454, 462-503, 547-587, 593-673, 713-791, 895, 1059-1102, 1107-1207, 1233-1238, 1288-1321, 1359-1444, 1485-1569, 1596-1602, 1625-1626, 1631-1635, 1642-1645, 1649-1650 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/rnn_cell_wrapper_v2.py 43 17 60% 42-43, 66, 71-72, 75-82, 86-89, 98-100, 113, 124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/serialization.py 57 18 68% 68, 84-105 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/wrappers.py 370 318 14% 52-54, 57-60, 64-67, 70-77, 81-86, 126-137, 163-172, 175-184, 187-195, 199-251, 290-327, 400-456, 460-469, 480-495, 499-518, 522-593, 602-677, 680-681, 684-688, 691-707, 711-715, 718-728, 733-745 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/losses.py 266 127 52% 105, 107, 140-144, 157, 161, 177, 181-201, 243-246, 249-253, 312, 372, 433, 494, 570-576, 729, 793, 855, 916, 974, 1032, 1093, 1162, 1196-1198, 1228-1230, 1262-1266, 1300-1304, 1309-1319, 1347-1350, 1379-1382, 1411-1415, 1438-1444, 1481-1487, 1517-1527, 1555-1557, 1585-1594, 1632-1636, 1667-1669, 1714-1716, 1782, 1797-1803, 1816, 1831, 1853-1863 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/metrics.py 759 457 40% 151, 164-167, 186-207, 212, 216, 224, 244, 253, 268, 274, 290, 328-374, 377-385, 425, 516-518, 533-547, 551-554, 595-604, 608-618, 624-628, 665, 710, 765, 809, 889, 913-918, 936, 944-948, 951-952, 956-958, 1004, 1054, 1104, 1154, 1225-1237, 1256, 1269-1271, 1274-1275, 1279-1285, 1351-1363, 1382, 1395-1397, 1400-1401, 1405-1411, 1423-1450, 1465, 1478-1479, 1537-1541, 1546-1556, 1561-1566, 1623-1627, 1632-1642, 1647-1652, 1701-1705, 1713-1723, 1728-1730, 1782-1786, 1796-1807, 1810-1813, 1920-1981, 1985-2023, 2038-2067, 2129-2164, 2167-2214, 2219-2223, 2228-2244, 2295, 2326, 2359, 2390, 2423, 2456, 2491, 2523, 2555, 2570-2575, 2579, 2610, 2641, 2672, 2729-2734, 2754-2776, 2780-2799, 2803, 2806-2808, 2840-2844, 2847-2857, 2861, 2865, 2877-2908, 2911-2916, 2919-2920, 2973, 3112, 3136, 3155-3157, 3160-3166, 3170-3174, 3178-3184, 3200-3202, 3219, 3239-3252, 3268, 3285-3294, 3310-3312, 3327-3330, 3335, 3340, 3345, 3355-3364, 3368 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py 243 135 44% 63-69, 73-75, 82-85, 90, 93-96, 99-100, 104-105, 109-110, 113, 117-127, 131, 134-143, 161, 165, 169, 173, 176, 179, 183, 187, 190-191, 194-195, 198-199, 202-203, 206-207, 210-211, 214-215, 218-219, 222-223, 226-227, 230-231, 234-235, 238-239, 242-243, 246, 250, 254, 258, 262, 266, 270, 274, 277, 284, 289, 292, 304, 308, 312, 316, 325, 328, 331, 334, 337, 340, 343, 346, 349, 352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388-392, 395-399, 402-406, 409-413, 439-462, 481-483 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/device_compatibility_check.py 66 48 27% 54-61, 73-128, 154-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/get_layer_policy.py 11 3 73% 38-41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale.py 15 4 73% 29, 33-38, 48 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale_optimizer.py 140 81 42% 48, 123-155, 160, 182-189, 212-214, 218-224, 227-229, 232, 238-245, 251-272, 279, 284-286, 293-298, 301-303, 313, 317, 320, 323, 327, 330, 333, 336, 345, 349, 353, 357, 366, 372, 395-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/policy.py 133 60 55% 328, 331, 339, 341, 348, 361-371, 374, 376, 378, 382-388, 460, 463-471, 475-479, 508-509, 519-520, 549-558, 573-578, 582-586, 606, 612-617, 621-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/models.py 277 231 17% 57, 61, 67-74, 90-129, 162-215, 234-247, 265-276, 307-381, 419-426, 452-533, 550-557, 570-589, 638-723 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adadelta.py 49 28 43% 100-104, 108-111, 114-115, 121-127, 130-136, 147-153, 165-172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adagrad.py 60 35 42% 91-100, 103-107, 110-111, 118-124, 143-147, 150-155, 164-169, 179-186 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adam.py 84 61 27% 144-150, 155-161, 164-173, 185-192, 195-217, 232-269, 272-281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adamax.py 63 41 35% 103-108, 112-115, 118-126, 137-144, 157-181, 184-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/ftrl.py 60 42 30% 108-136, 141-146, 149-150, 162-181, 194-214, 228-245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/gradient_descent.py 53 24 55% 112, 118-120, 123-124, 128-143, 148-156, 161-166, 177-184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/learning_rate_schedule.py 249 184 26% 44, 48, 60, 135-140, 143-154, 158, 225-233, 236-256, 259, 360-367, 370-392, 399, 480-486, 489-502, 505, 573-578, 581-594, 597, 668-675, 678-716, 719, 803-810, 813-831, 835, 925-934, 937-965, 969, 983, 988 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/nadam.py 96 72 25% 92-105, 108-124, 127-146, 164-165, 168-188, 191-228, 231-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py 448 310 31% 72-76, 268, 272, 293, 301, 333-336, 340-356, 385-402, 418-430, 472-504, 520-545, 550-604, 607-613, 618, 622-630, 633-643, 646, 661-663, 675, 677, 684, 686, 692, 696-730, 733-735, 738-754, 757-759, 763-765, 768-782, 787-795, 799-803, 807-815, 829-834, 853-859, 863-870, 874, 879, 908-909, 941-958, 969-1000, 1004, 1019-1023, 1034, 1041, 1055, 1080-1082, 1104, 1107-1109, 1112-1114, 1119, 1124, 1132-1140, 1163-1199, 1206-1224, 1243-1247, 1253-1254, 1269-1270, 1274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/rmsprop.py 101 76 25% 133-146, 149-156, 159-162, 171-214, 217-272, 275-281, 284-293 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizers.py 434 336 23% 62-72, 88, 91, 107-121, 137-151, 159, 162-167, 171, 189-196, 199-202, 205-230, 233-240, 258-267, 270-272, 275-297, 300-307, 332-340, 343-346, 349-371, 374-380, 411-420, 423-427, 430-459, 462-469, 495-506, 509-516, 519-555, 558-567, 590-600, 604-610, 613-644, 647-655, 681-691, 694-699, 702-742, 745-753, 760-767, 772, 775, 778, 781-806, 810, 813, 816, 832, 849-865, 892-902 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/linear.py 59 39 34% 88-96, 99-127, 130-147, 150-160, 164-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/wide_deep.py 98 75 23% 87-91, 94-109, 113-136, 140-194, 197-205, 209-215 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/image.py 95 29 69% 27-28, 80-87, 152-154, 230-238, 299-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/sequence.py 18 1 94% 156 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/text.py 19 2 89% 42-43 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/regularizers.py 65 23 65% 152, 172, 192, 215-222, 225, 244, 280, 285, 290, 302, 304-311, 315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/hdf5_format.py 354 269 24% 43-44, 82-130, 158-217, 251-258, 272, 287-310, 316, 318, 320, 323-393, 397-404, 446, 468-473, 483-519, 527-572, 585-598, 610-613, 623-644, 661, 665, 682, 699, 729-789, 809-831, 851-856, 880 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/model_config.py 28 14 50% 29-30, 50-55, 86-90, 114-116 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/save.py 45 21 53% 39-40, 113-137, 181-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/base_serialization.py 33 10 70% 34, 43, 54, 74, 87-95, 106, 122, 172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/constants.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/json_utils.py 32 19 41% 38-41, 44, 48-56, 60, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/layer_serialization.py 74 40 46% 36, 41, 48-69, 72, 76, 81-96, 100-105, 111-119, 127, 131, 141, 144, 152, 155-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/load.py 460 367 20% 116-137, 143-148, 175-202, 209-215, 219, 223-234, 239-291, 295-313, 317-344, 348-363, 367-393, 401-431, 434-450, 454-466, 470-472, 486-510, 514-520, 523-539, 544-572, 577-579, 609-623, 627-646, 650-654, 662-687, 694-719, 723-725, 730-738, 745-753, 758-778, 786-791, 802-831, 835, 838-841, 848-863, 872-882, 885, 890-902, 915-922, 931-948, 953-958, 966-968, 972 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/metric_serialization.py 18 7 61% 30, 33-40, 43 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/model_serialization.py 27 11 59% 32, 35-39, 42-55, 62 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/network_serialization.py 14 5 64% 30, 33-39 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/save.py 29 14 52% 59-81 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/save_impl.py 271 210 23% 72-78, 97-116, 146-197, 201-205, 233-290, 296-302, 309-316, 320-323, 338-363, 375-397, 406-422, 427-434, 437-441, 445-448, 452, 457-491, 495-504, 509-527, 534-537, 540-542, 545-547, 564-567, 572-576, 582-586, 590-594, 603-607, 614-616, 624-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/serialized_attributes.py 78 34 56% 145-154, 158-160, 165, 171, 177, 183-186, 190-203, 207-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/utils.py 112 88 21% 56-96, 101-113, 117-120, 125-128, 150-199, 214-220, 224-230, 234-239, 243-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model_experimental.py 135 89 34% 133-145, 151-155, 160-163, 168-221, 226-227, 231, 255-326, 331-361, 371, 416-430 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saving_utils.py 138 110 20% 48-53, 77-87, 91, 112-142, 147-191, 197-199, 204-233, 245-264, 270-275, 280-285, 291-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/all_utils.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/conv_utils.py 172 152 12% 29-48, 68-87, 103-113, 128-137, 160-186, 190-197, 201-208, 227-233, 279-309, 358-400, 439-456, 475-482 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/data_utils.py 425 321 24% 56-57, 62-64, 69-104, 111-114, 133-160, 214-282, 286-294, 316-325, 342-350, 357-367, 370, 373, 376-384, 389-393, 453, 462, 467, 471-472, 485-486, 506-514, 519-525, 531-533, 538, 559-645, 662, 689-715, 718, 728-738, 743, 753-759, 762-763, 768, 780, 791, 807-808, 819-826, 831-833, 837-860, 872-880, 894-907, 923, 946-947, 958-964, 968-974, 987-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/generic_utils.py 363 275 24% 72-73, 76-79, 82-83, 114, 135, 140, 167-189, 207-210, 216-221, 251-257, 263-296, 301-305, 314-347, 356, 360-382, 386, 388, 392, 397-402, 415-426, 441-474, 490-493, 518-540, 553-675, 678, 691-692, 717-739, 754-756, 766, 774, 779-781, 792, 797 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/io_utils.py 84 52 38% 32-33, 84-99, 102, 105-134, 143, 152, 161, 170, 182-185, 201-209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/layer_utils.py 202 85 58% 50, 55, 62-69, 79-90, 136, 152-162, 205-208, 222-223, 228, 262, 287-294, 310-326, 345-357, 382-396, 400-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/losses_utils.py 58 39 33% 48-49, 54-55, 61-67, 91-112, 117-121, 137-148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/metrics_utils.py 216 159 26% 75-93, 119-149, 157-166, 170-173, 179-182, 199-204, 227-234, 296-456, 472-475, 495-539 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/mode_keys.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/multi_gpu_utils.py 82 65 21% 31, 35-36, 157-266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/np_utils.py 26 16 38% 49-61, 76-78 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/tf_utils.py 203 105 48% 61-64, 83-91, 95-99, 116-152, 178, 181, 226, 231, 248, 251, 266-295, 319, 345, 352, 355-356, 359, 392, 397-404, 408, 427-428, 452, 458-462, 467-472, 478, 481, 485, 490-496, 521 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/version_utils.py 34 4 88% 69, 79-85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/vis_utils.py 150 125 17% 45-53, 57-59, 64-65, 98-249, 278-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/scikit_learn.py 106 77 27% 75-77, 88-106, 117-119, 130-132, 150-168, 181-187, 214-223, 240-242, 263-270, 293-308, 332-333, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/base.py 218 171 22% 105-110, 148, 153, 195-234, 244-246, 250-258, 262-269, 273-279, 282-294, 300-302, 305-314, 376-481, 507-552, 555-569, 573, 578, 582-593 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/convolutional.py 84 21 75% 98, 198-218, 297, 404-424, 504, 612-632, 717, 829, 947-971, 1072-1096, 1164, 1260-1279, 1344, 1434-1453 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/core.py 39 9 77% 98, 173-187, 219, 226, 270-271, 331-332 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/layers.py 46 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/normalization.py 22 4 82% 147, 172, 312-336 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/pooling.py 78 30 62% 50-52, 90-95, 120-122, 160-165, 194-196, 235-238, 267-269, 308-311, 342-344, 385-388, 419-421, 460-463 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/utils.py 129 109 16% 28-47, 67-86, 90-95, 99-103, 119-129, 144-153, 168-175, 197-200, 219-227, 232-233, 238-243, 260-285 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/file_io.py 257 108 58% 58, 66, 71, 76, 84, 120, 139-165, 169-170, 174-181, 189-191, 202, 205-208, 211, 220-221, 232, 316-320, 333-334, 350, 366-374, 396, 412, 458, 474, 526-535, 589-590, 610-614, 653, 681, 701-729, 782-790, 808-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/python_io.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/tf_record.py 81 48 41% 90-99, 114-125, 129-149, 170-171, 212, 294-298, 313, 317, 321 /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/module.py 96 58 40% 107-121, 130, 135-139, 154, 169, 193, 249-252, 287-291, 295, 299, 303, 310, 314, 326-378 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_grad.py 573 409 29% 44, 50, 72-212, 218, 228, 247-260, 266-285, 301-305, 319, 324-329, 337, 342, 347, 352, 358, 364-368, 374-382, 389-399, 407-424, 430-460, 466-500, 505-507, 516, 525, 531-538, 552-564, 569-585, 593-615, 627-687, 692-700, 705-713, 719, 728, 737, 742, 747, 755, 767, 773, 778, 784-785, 791-792, 810-832, 842-854, 864-865, 876-877, 882-883, 889-890, 898, 906-907, 915, 923-928, 934-939, 947-948, 953-954, 959, 964, 970, 975-1027, 1032-1090, 1095-1097, 1102-1107, 1112-1115, 1120-1123, 1128-1130, 1135-1150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_ops.py 1121 797 29% 193-195, 277, 341-344, 426, 475, 503, 530, 576, 602, 620-630, 649, 683, 715, 732-753, 787, 802-811, 825-836, 840, 902-973, 1037, 1137-1178, 1224, 1270-1277, 1328-1342, 1357-1393, 1407-1414, 1419-1425, 1446-1455, 1501-1511, 1594, 1601-1605, 1659-1693, 1746, 1786-1790, 1832, 1882, 1943-1961, 2042, 2112-2129, 2190-2210, 2368-2371, 2514-2517, 2653, 2661-2667, 2710, 2712, 2721-2723, 2728-2730, 2732, 2774, 2819, 2825-2850, 2883, 2918, 2923-2931, 2959-2981, 3023, 3092-3139, 3199, 3260-3294, 3311-3316, 3362-3392, 3404-3438, 3443-3456, 3519-3527, 3541, 3553, 3565, 3607-3650, 3661-3670, 3678, 3687, 3695, 3704, 3712, 3720-3729, 3845-3848, 3966-4009, 4016-4025, 4064-4090, 4141-4145, 4197, 4243-4251, 4340-4348, 4406-4410, 4424, 4512-4524, 4535, 4554-4560, 4589-4682, 4835-4846, 4852, 4860-4934, 4956-4975, 5007-5019, 5043-5060, 5106-5113, 5172-5184, 5307, 5350-5352, 5405, 5410-5416, 5441-5455, 5499-5565, 5571-5578, 5583-5591, 5641-5644 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/batch_ops.py 25 13 48% 79-111 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bitwise_ops.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/boosted_trees_ops.py 144 76 47% 63-66, 74-89, 93-95, 111-126, 129, 133, 138-140, 143, 147, 150, 153-156, 159, 162, 176-190, 203-204, 214-227, 234, 238, 245-247, 250, 254-255, 259-262, 271-276, 290, 303 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/candidate_sampling_ops.py 37 12 68% 83-84, 148-149, 208-209, 299-300, 337-338, 386-387 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/check_ops.py 590 425 28% 73-78, 83-85, 91, 238-271, 285-297, 327-372, 392-402, 435, 442-455, 487, 494-506, 539, 547-560, 593, 601-614, 648, 654-658, 696, 705, 758, 810-838, 873, 879, 915, 923, 959, 966, 1003, 1012, 1037-1061, 1094, 1124-1156, 1189, 1222-1255, 1259, 1263-1269, 1294-1321, 1353, 1386-1416, 1436, 1462-1475, 1494, 1515-1525, 1542-1555, 1561-1564, 1568-1574, 1578, 1648, 1715-1866, 1872-1883, 1910, 1947-1951, 1989-1993, 2012-2040, 2068-2074, 2096, 2120-2130, 2175-2178, 2183-2184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/clip_ops.py 107 77 28% 105-122, 132-151, 193-213, 239-263, 316-357, 391-404 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/clustering_ops.py 263 210 20% 134-149, 165-172, 186-199, 216-225, 241-263, 267, 289-315, 321-325, 354-392, 397-433, 450-499, 516-535, 584-597, 601-607, 612-615, 634-701, 708, 716-718, 721-733, 737-750, 753-760, 767 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/collective_ops.py 25 16 36% 50-52, 83-85, 127-136, 167-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/cond_v2.py 435 374 14% 60-94, 109-188, 220-297, 311-337, 358-375, 380, 406-435, 440-451, 470-478, 483-493, 513-538, 548-563, 587-614, 621-679, 684-689, 707-715, 720-721, 734-735, 749-750, 755-756, 763-801, 805-811, 820-826, 845-860, 865, 870, 873-942, 947-976, 986-1064, 1088-1124, 1138-1148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/confusion_matrix.py 62 42 32% 59-92, 152-201, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_grad.py 121 89 26% 42-88, 98-136, 143, 149-182, 194, 199, 209-232, 237, 243 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_ops.py 1318 1123 15% 86-104, 148-176, 189-198, 202-211, 240-259, 274-283, 305-317, 341-362, 390-424, 431-434, 438-442, 450-457, 473-492, 506-529, 545-562, 578-591, 596-618, 641-654, 663-679, 684, 689, 693, 697, 707, 718-724, 727, 732-734, 738-740, 744-745, 749-750, 754-758, 762-764, 768-785, 791-792, 796, 799, 802, 805, 808, 833-849, 858-867, 872, 876, 880, 884-886, 890-892, 895, 906-922, 927-933, 936, 940-972, 975, 979-1032, 1036-1050, 1053-1059, 1063-1083, 1086, 1090-1093, 1175-1296, 1310-1314, 1392, 1397-1401, 1433-1440, 1456-1475, 1484-1529, 1534, 1539, 1544, 1549, 1554, 1559, 1564, 1569, 1580-1609, 1612, 1625-1630, 1633, 1636-1638, 1642-1694, 1708-1719, 1727-1768, 1773-1788, 1810-1838, 1860-1904, 1928-1986, 2001-2086, 2093-2098, 2103-2223, 2230-2265, 2268-2296, 2300, 2481, 2668-2770, 2789-2803, 2807-2810, 2838-2848, 2854-2859, 2883-2921, 2957, 2993-3027, 3038-3048, 3064-3081, 3105-3144, 3177-3196, 3220-3257, 3282-3291, 3388, 3496, 3579, 3586-3587, 3592, 3596, 3599, 3602, 3615-3621 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_state.py 402 338 16% 58-105, 124-206, 211, 216, 221, 230-235, 240, 245, 254-261, 266, 271, 276, 281, 286, 291, 296, 332-386, 404-435, 451-491, 498, 502-511, 538-551, 555-557, 561-563, 573-590, 607-645, 660-716, 727-757, 771-781, 786-807, 812-831, 836-839 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_util.py 156 108 31% 47, 54, 59-65, 69-70, 75-79, 88-89, 94, 99, 104, 109, 114-130, 135-147, 152-155, 160-163, 168, 173-177, 187, 208-209, 226-229, 244-247, 252-255, 259, 263, 287-341, 344-363, 368-371 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_util_v2.py 98 66 33% 44-50, 62-65, 78, 82, 105-109, 124-126, 155-199, 215-218, 223, 228, 251-261, 266-287 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_v2_func_graphs.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_v2_toggles.py 20 3 85% 58, 67, 94 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/critical_section_ops.py 137 98 28% 55-62, 66, 71-74, 81-85, 109-120, 198-205, 209-228, 232, 264-337, 342-374, 379-381, 406-415 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ctc_ops.py 417 343 18% 57-60, 64-68, 184, 211-236, 246-253, 268, 283, 328-331, 383-391, 437, 460-502, 519-542, 548-556, 564-576, 583-621, 648-682, 686-689, 694-702, 712-720, 729, 787-816, 881-921, 997-1055, 1077-1117, 1132-1146, 1178-1187, 1204-1214, 1238-1301, 1345-1417, 1422 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/cudnn_rnn_grad.py 21 9 57% 27-30, 52-55, 79-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/custom_gradient.py 189 135 29% 64, 206, 211-214, 251-255, 258, 264-281, 288-298, 304-402, 408-454, 480-507, 551-560 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/data_flow_grad.py 51 20 61% 33-45, 53-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/data_flow_ops.py 622 469 25% 50-56, 64-88, 92-99, 104-112, 160-182, 200-219, 228, 233-235, 240, 245, 250, 272-293, 304-309, 334-347, 377-395, 412-419, 441-457, 484-500, 528-541, 565-573, 590-595, 606-611, 614-616, 683-706, 754-765, 818-829, 834, 840, 904-919, 974-987, 1055-1075, 1080, 1085-1087, 1106-1108, 1153-1178, 1202-1204, 1219-1221, 1233-1235, 1259-1268, 1273, 1278, 1283, 1294-1297, 1313, 1346-1356, 1375-1379, 1404-1407, 1438-1444, 1464, 1508-1509, 1540, 1564-1566, 1581-1584, 1600, 1618-1647, 1652, 1657, 1662, 1667, 1672, 1677, 1707-1757, 1764-1772, 1790-1800, 1811-1816, 1894, 1915-1933, 1936-1940, 1964-1974, 1994-2004, 2015-2018, 2034-2037, 2140-2161, 2190-2205, 2208-2235, 2255-2270, 2295-2298, 2316-2331, 2351-2370, 2381-2384, 2400-2403, 2419-2422, 2477-2489, 2496-2516 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/default_gradient.py 37 24 35% 28-35, 40-49, 54-57, 62-65, 79-84 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/bernoulli.py 70 32 54% 83-90, 101, 106, 111, 114, 117, 120, 123, 126-130, 133-152, 155, 159, 162, 166, 182-186 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/beta.py 114 53 54% 192-203, 216, 223, 228, 233, 236, 239, 242, 245, 248-263, 267, 271, 275, 279, 282-283, 287, 292, 300, 303, 311-320, 333-335, 343-345, 368-379, 395-407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/categorical.py 108 69 36% 38-59, 193-234, 247, 252, 257, 260, 263, 266, 269, 272-282, 285-304, 307-314, 318, 322-325, 341-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/dirichlet.py 93 44 53% 196-202, 215, 220, 223, 226, 229, 232, 235-240, 244, 248, 251-252, 255, 258-259, 268, 271-272, 278-280, 284, 292-302, 311-313, 327-329, 351-410 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/dirichlet_multinomial.py 87 43 51% 203-223, 236, 241, 246, 249, 252, 255, 259, 262-278, 282-286, 291, 294, 316-317, 323-325, 331-332, 336-340, 348-351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/distribution.py 405 242 40% 91, 135-140, 170, 179, 186, 189, 203, 245, 259, 463-477, 481, 493-495, 515-516, 540-555, 559, 564, 569, 577, 591, 608, 613, 630-631, 634, 649-654, 657, 671, 674, 686-691, 694, 705, 716-717, 730-731, 736, 740-750, 766, 769, 773-782, 795, 798, 802-811, 824, 827, 831-840, 863, 866, 870-879, 898, 901, 906-915, 939, 942, 946-955, 976, 979, 984-985, 988, 993-994, 997, 1001-1004, 1023, 1026, 1048-1055, 1058, 1081-1088, 1091, 1130-1131, 1134, 1139-1140, 1143, 1168-1169, 1172, 1200-1201, 1204, 1220, 1234-1237, 1241-1266, 1271-1310, 1314-1323 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/distributions.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/exponential.py 44 16 64% 102-116, 120, 124, 127, 130-144, 159-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/gamma.py 93 40 57% 165-176, 188, 195, 200, 203, 208, 213, 216, 222, 230, 233-236, 239-240, 243, 247, 254, 257, 260, 267-275, 283-286, 305-314, 330-336 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/kullback_leibler.py 55 26 53% 41-52, 99-121, 159-160, 207, 209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/laplace.py 87 36 59% 112-119, 130, 137, 142, 145, 149, 153, 156, 159-173, 177, 180, 183, 186, 189-190, 194, 197, 201-202, 205, 208, 211, 214, 217, 234-242 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/multinomial.py 88 46 48% 194-208, 222, 227, 232, 235, 238, 241, 244, 247-277, 281, 284-285, 288-289, 292, 295-297, 303-305, 309-312 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/normal.py 100 46 54% 146-153, 164, 171, 176, 179, 184, 189, 192, 195-198, 201, 204, 207, 210, 213, 216, 219, 223-224, 227, 230, 233, 236, 240-241, 245-246, 263-271, 287-294 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/special_math.py 113 89 21% 137-143, 148-156, 179-185, 195-279, 332-365, 376-379, 384-398, 415-421, 426, 458-474 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/student_t.py 103 52 50% 191-200, 211, 219, 224, 229, 232, 238, 244, 247, 256-266, 269, 272-273, 276, 284-287, 290-294, 306-316, 337-358, 368, 386-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/uniform.py 69 31 55% 114-123, 135, 142, 147, 151-152, 155, 160, 165, 168, 171-175, 178-180, 189-196, 199, 202, 205, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/util.py 438 377 14% 61-75, 85-86, 93-105, 118-134, 152-161, 193-242, 247, 256, 269, 274-283, 288-292, 297-299, 343-377, 430-485, 513-519, 572-581, 620-657, 686-701, 720-748, 760, 772, 784-787, 792-795, 841-910, 951-979, 1015-1047, 1114-1142, 1164-1199, 1209-1213, 1243-1280, 1308-1351, 1370-1388, 1427-1437, 1443, 1448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/embedding_ops.py 239 200 16% 57-78, 118-249, 314-320, 373, 456-547, 626, 683, 746-826, 854-872, 877-885, 890-894 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/functional_ops.py 275 203 26% 103-161, 230, 296-355, 424, 535-683, 797, 832, 860-861, 867-884, 914-949, 977-1024, 1055-1079, 1118, 1121, 1124, 1127, 1130-1144, 1179 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_array_ops.py 5009 4493 10% 35-62, 68-79, 92-118, 124-133, 146-172, 178-187, 201-227, 233-243, 274-304, 310-321, 458-502, 508-520, 587-628, 634-644, 661-687, 693-703, 726-753, 759-769, 805-845, 851-861, 881-922, 928-938, 957-985, 991-1001, 1019-1050, 1056-1071, 1098-1128, 1134-1148, 1174-1181, 1184, 1192-1195, 1204-1219, 1237-1264, 1270-1280, 1294-1323, 1329-1339, 1356-1382, 1388-1397, 1414-1428, 1434, 1446-1472, 1478-1487, 1593-1628, 1634-1648, 1724-1768, 1774-1798, 1833-1872, 1878-1887, 1923-1962, 1968-1977, 2016-2055, 2061-2078, 2097-2128, 2134-2147, 2165-2192, 2198-2208, 2259-2271, 2277-2280, 2289-2299, 2327-2374, 2380-2406, 2436-2494, 2500-2521, 2558-2620, 2626-2649, 2672-2741, 2747-2771, 2811-2866, 2872-2890, 2924-2986, 2992-3011, 3052-3111, 3117-3135, 3171-3237, 3243-3262, 3309-3328, 3334-3344, 3391-3417, 3423-3433, 3474-3508, 3514-3528, 3648-3675, 3681-3691, 3740-3773, 3779-3794, 3816-3855, 3861-3870, 3891-3898, 3904-3906, 3915-3924, 3955-3993, 3999-4007, 4026-4060, 4066-4078, 4097-4123, 4129-4140, 4159-4185, 4191-4202, 4221-4247, 4253-4264, 4296-4335, 4341-4350, 4394-4425, 4431-4444, 4479-4510, 4516-4529, 4591-4634, 4640-4651, 4691-4717, 4723-4732, 4774-4800, 4806-4815, 4905-4932, 4938-4949, 5078-5108, 5114-5128, 5245-5274, 5280-5293, 5447-5480, 5486-5502, 5530-5556, 5562-5572, 5665-5691, 5697-5708, 5842-5872, 5878-5892, 5940-5968, 5974-5985, 6022-6050, 6056-6067, 6177-6209, 6215-6230, 6244-6270, 6276-6285, 6322-6357, 6363-6380, 6417-6444, 6450-6460, 6500-6528, 6534-6545, 6580-6613, 6619-6634, 6657-6669, 6673, 6679-6682, 6691-6703, 6723-6752, 6758-6768, 6786-6798, 6805-6808, 6817-6828, 6852-6882, 6888-6900, 6918-6970, 6976-7001, 7097-7156, 7162-7193, 7216-7267, 7273-7298, 7459-7513, 7519-7547, 7580-7649, 7655-7690, 7728-7786, 7792-7820, 7847-7876, 7882-7894, 7921-7947, 7953-7962, 7975-7989, 7995, 8069-8096, 8102-8112, 8141-8187, 8192-8219, 8279-8305, 8311-8321, 8395-8430, 8436-8451, 8518-8558, 8564-8574, 8675-8717, 8723-8734, 8788-8816, 8822-8834, 8857-8887, 8893-8905, 8921-8955, 8961-8977, 9001-9031, 9037-9049, 9077-9104, 9110-9121, 9134-9160, 9166-9175, 9283-9313, 9319-9331, 9469-9513, 9519-9531, 9630-9665, 9671-9685, 9704-9731, 9737-9747, 9768-9797, 9803-9814, 9850-9884, 9890-9906, 9941-9980, 9986-9995, 10137-10193, 10199-10227, 10256-10295, 10301, 10331-10387, 10393-10421, 10504-10547, 10553-10564, 10647-10690, 10696-10707, 10801-10844, 10850-10861, 10890-10949, 10955-10985, 11030-11070, 11076-11086, 11104-11130, 11136-11146, 11163-11190, 11196-11206, 11250-11281, 11287-11299, 11367-11399, 11405-11418, 11458-11489, 11495-11507, 11580-11612, 11618-11631, 11662-11692, 11698-11710, 11751-11790, 11796-11806, 11841-11872, 11878-11891, 11964-11990, 11996-12005, 12018-12044, 12050-12059 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_audio_ops.py 216 187 13% 67-104, 110-125, 167-220, 226-242, 267-306, 312-322, 356-410, 416-440 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_batch_ops.py 262 237 10% 87-167, 173-212, 289-372, 378-419, 456-495, 501-520, 553-590, 596-614 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_bitwise_ops.py 319 274 14% 56-95, 101-111, 147-186, 192-202, 238-277, 283-293, 348-387, 393-402, 449-488, 494-504, 524-550, 556-565, 615-654, 660-670 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_boosted_trees_ops.py 1223 1116 9% 46-80, 86-101, 121-162, 168-192, 241-285, 291-310, 362-409, 415-439, 484-532, 538-566, 588-619, 625-639, 657-681, 686-695, 715-744, 749-761, 781-805, 810-819, 833-869, 875-890, 913-953, 959-977, 996-1028, 1034-1044, 1068-1097, 1103-1113, 1134-1167, 1173-1189, 1215-1259, 1265-1286, 1308-1346, 1352-1369, 1389-1416, 1421-1434, 1452-1480, 1485-1498, 1523-1551, 1556-1567, 1586-1619, 1625-1635, 1649-1685, 1691-1706, 1727-1756, 1762-1772, 1816-1858, 1864-1881, 1934-1981, 1987-2008, 2045-2089, 2095-2115, 2156-2229, 2234-2291, 2338-2442, 2447-2533, 2547-2575, 2581-2591, 2607-2635, 2641-2651 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_candidate_sampling_ops.py 423 383 9% 65-108, 114-133, 169-209, 215-232, 314-400, 406-451, 499-549, 555-576, 624-673, 679-700, 748-799, 805-826, 874-922, 928-949 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_checkpoint_ops.py 103 84 18% 77-119, 125-142, 215-259, 265-284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_clustering_ops.py 116 94 19% 42-68, 74-85, 113-145, 151-164, 194-221, 227-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_collective_ops.py 215 191 11% 38-85, 91-109, 127-173, 179-197, 215-260, 266-284, 305-368, 374-406 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_control_flow_ops.py 434 371 15% 40-68, 73-84, 98-117, 122-127, 151-190, 196-213, 228-254, 260-269, 286-312, 318-327, 354-386, 392-406, 419-445, 451-460, 474-505, 510-515, 539-564, 570, 585-599, 605, 631-650, 656, 669-683, 689, 704-723, 729, 756-770, 776, 802-829, 835-845 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ctc_ops.py 214 186 13% 57-101, 107-127, 164-198, 204-217, 260-315, 321-345, 389-443, 449-472 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_cudnn_rnn_ops.py 910 857 6% 83-145, 151-184, 262-329, 335-364, 446-514, 520-550, 640-721, 727-765, 818-892, 898-948, 1003-1081, 1087-1137, 1183-1247, 1253-1289, 1349-1414, 1420-1456, 1519-1596, 1602-1644, 1711-1773, 1779-1812, 1886-1961, 1967-2007 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py 3989 3627 9% 39-47, 52, 64-78, 84, 101-110, 115, 138-154, 160, 195-236, 242, 265-277, 282, 294-308, 314, 338-347, 352, 364-378, 384, 425-462, 468, 497-528, 534, 547-566, 571-577, 634-678, 684-695, 774-827, 833-857, 885-926, 932, 959-1015, 1021-1051, 1064-1078, 1084, 1096-1122, 1128-1137, 1150-1176, 1182-1191, 1206-1233, 1239-1249, 1266-1306, 1311-1334, 1351-1401, 1407-1433, 1455-1508, 1514-1541, 1558-1608, 1614-1640, 1666-1708, 1713-1740, 1762-1815, 1821-1848, 1876-1930, 1936-1965, 1982-2022, 2027-2050, 2067-2119, 2125-2152, 2175-2229, 2235-2263, 2280-2330, 2336-2362, 2390-2433, 2438-2465, 2487-2541, 2547-2575, 2603-2659, 2665-2694, 2730-2771, 2777, 2812-2868, 2874-2904, 2980-3020, 3026-3050, 3084-3125, 3131, 3164-3220, 3226-3256, 3278-3290, 3295, 3316-3342, 3347-3356, 3382-3405, 3411, 3444-3468, 3474, 3507-3547, 3553-3571, 3609-3633, 3639, 3676-3716, 3722-3740, 3766-3805, 3811-3828, 3853-3864, 3869, 3898-3909, 3914, 3943-3966, 3971-3981, 4006-4029, 4034-4044, 4060-4074, 4080, 4095-4121, 4127-4136, 4149-4163, 4169, 4181-4207, 4213-4222, 4259-4313, 4319, 4355-4426, 4432-4472, 4498-4564, 4570-4600, 4619-4641, 4646-4655, 4668-4694, 4700-4710, 4728-4750, 4755-4763, 4787-4817, 4823-4835, 4867-4915, 4921-4942, 4972-4986, 4991, 5024-5041, 5047, 5075-5107, 5113, 5126-5145, 5151, 5163-5170, 5175, 5187-5206, 5211-5217, 5231-5246, 5252, 5265-5292, 5298-5308, 5323-5342, 5348, 5363-5394, 5400-5413, 5432-5466, 5472-5485, 5513-5547, 5552-5571, 5588-5628, 5633-5656, 5678-5731, 5737-5763, 5780-5830, 5836-5862, 5880-5914, 5920, 5932-5939, 5944, 5956-5975, 5980-5986, 6003-6022, 6027-6033, 6056-6077, 6083, 6105-6141, 6147-6161, 6201-6237, 6243-6257, 6274-6295, 6301, 6317-6352, 6358-6373, 6397-6432, 6438-6453, 6468-6484, 6490, 6504-6532, 6538-6549, 6612-6641, 6647-6658, 6693-6725, 6731-6744, 6760-6780, 6786, 6801-6817, 6823, 6838-6866, 6872-6884, 6901-6929, 6935-6947, 6963-6978, 6984, 6999-7026, 7032-7044, 7064-7091, 7097-7109, 7123-7137, 7143, 7156-7182, 7188-7198, 7214-7240, 7246-7256, 7272-7287, 7293, 7308-7335, 7341-7353, 7390-7417, 7423-7435, 7450-7465, 7471, 7488-7538, 7544-7568, 7613-7672, 7678-7706, 7722-7737, 7743, 7758-7785, 7791-7803, 7821-7848, 7854-7866, 7886-7937, 7943-7968 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_dataset_ops.py 3330 3013 10% 34-74, 80-98, 129-160, 166-184, 203-230, 236-244, 266-315, 321-346, 367-394, 400-411, 429-470, 476-496, 517-566, 572-597, 620-661, 667-687, 704-745, 751-772, 788-830, 836-856, 875-926, 932-955, 973-1015, 1021-1037, 1053-1094, 1100-1119, 1141-1152, 1157-1164, 1178-1197, 1202-1209, 1226-1252, 1257-1271, 1285-1305, 1310-1318, 1335-1355, 1360-1367, 1379-1405, 1411-1419, 1434-1476, 1482-1502, 1528-1574, 1580-1601, 1626-1658, 1664-1678, 1696-1730, 1736-1751, 1784-1819, 1825-1846, 1869-1888, 1891, 1896, 1910-1919, 1928-1952, 1980-2028, 2034-2057, 2073-2119, 2125-2146, 2166-2211, 2217-2241, 2256-2301, 2307-2331, 2349-2363, 2366, 2371, 2380, 2382-2385, 2393-2412, 2427-2468, 2474-2494, 2514-2555, 2561-2580, 2594-2620, 2626-2636, 2652-2698, 2704-2725, 2750-2761, 2766-2773, 2792-2850, 2856-2884, 2919-2970, 2976-3001, 3031-3073, 3079-3105, 3129-3182, 3188-3214, 3232-3279, 3285-3309, 3331-3378, 3384-3405, 3422-3452, 3458-3470, 3484-3512, 3518-3528, 3565-3618, 3624-3650, 3681-3725, 3731-3759, 3772-3798, 3804-3813, 3828-3867, 3873-3892, 3905-3931, 3937-3946, 3958-3984, 3990-3998, 4023-4066, 4072-4095, 4124-4175, 4181-4208, 4256-4310, 4316-4344, 4398-4454, 4460-4489, 4549-4608, 4614-4645, 4671-4736, 4742-4775, 4801-4867, 4873-4906, 4936-4979, 4985-5012, 5032-5073, 5079-5100, 5125-5180, 5186-5212, 5230-5271, 5277-5297, 5312-5346, 5352-5364, 5384-5432, 5438-5463, 5493-5537, 5543-5567, 5598-5650, 5656-5682, 5699-5743, 5749-5770, 5788-5829, 5835-5855, 5870-5897, 5903-5915, 5936-5964, 5970-5981, 6000-6041, 6047-6067, 6089-6114, 6120-6135, 6149-6182, 6188-6203, 6223-6251, 6257-6268, 6281-6307, 6313-6322, 6393-6436, 6442-6465, 6478-6504, 6510-6519, 6541-6587, 6593-6618 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_debug_ops.py 422 389 8% 50-90, 96-116, 144-185, 191-211, 240-290, 296-322, 358-415, 421-451, 479-529, 535-561, 627-696, 702-739, 815-858, 864-883 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_decode_proto_ops.py 80 64 20% 102-165, 171-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_encode_proto_ops.py 59 44 25% 81-123, 129-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_experimental_dataset_ops.py 3926 3668 7% 36-78, 84-105, 134-176, 182-202, 231-280, 286-311, 327-368, 374-395, 417-459, 465-488, 509-578, 584-620, 636-687, 693-719, 735-761, 767-776, 792-818, 824-833, 852-872, 877-885, 908-951, 957-979, 999-1047, 1053-1080, 1096-1140, 1146-1167, 1196-1246, 1252-1278, 1294-1338, 1344-1365, 1387-1430, 1436-1460, 1476-1529, 1535-1562, 1578-1606, 1612-1622, 1641-1664, 1669-1678, 1701-1746, 1752-1774, 1794-1844, 1850-1877, 1918-1987, 1993-2023, 2048-2110, 2116-2144, 2159-2202, 2208-2228, 2241-2267, 2273-2283, 2298-2339, 2345-2365, 2381-2424, 2430-2451, 2490-2547, 2553-2582, 2601-2661, 2667-2696, 2709-2735, 2741-2751, 2768-2813, 2819-2840, 2855-2898, 2904-2924, 2957-3012, 3018-3045, 3087-3169, 3175-3223, 3240-3284, 3290-3311, 3331-3372, 3378-3399, 3423-3472, 3478-3503, 3522-3575, 3581-3608, 3626-3673, 3679-3702, 3718-3760, 3766-3787, 3811-3858, 3864-3887, 3906-3948, 3954-3976, 3990-4026, 4032-4047, 4060-4087, 4093-4103, 4129-4177, 4183-4205, 4222-4266, 4272-4293, 4315-4366, 4372-4394, 4409-4450, 4456-4476, 4491-4532, 4538-4558, 4599-4665, 4671-4700, 4725-4785, 4791-4818, 4833-4874, 4880-4899, 4912-4938, 4944-4953, 4981-5021, 5027-5046, 5062-5103, 5109-5129, 5162-5221, 5227-5257, 5296-5353, 5359-5387, 5400-5426, 5432-5441, 5458-5502, 5508-5529, 5544-5585, 5591-5611, 5668-5719, 5725-5752, 5797-5908, 5914-5983, 6033-6146, 6152-6222, 6239-6281, 6287-6308, 6339-6379, 6385-6405, 6429-6478, 6484-6508, 6536-6577, 6583-6605, 6625-6683, 6689-6719, 6737-6782, 6788-6811, 6827-6869, 6875-6895, 6919-6965, 6971-6993, 7030-7157, 7163-7233, 7252-7294, 7300-7321, 7335-7369, 7375-7389, 7403-7438, 7444-7459, 7473-7494, 7499-7507, 7520-7546, 7552-7562, 7588-7634, 7640-7661, 7678-7720, 7726-7746, 7768-7819, 7825-7846, 7861-7902, 7908-7927, 7942-7983, 7989-8008 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_functional_ops.py 540 492 9% 58-106, 112-138, 157-186, 192-202, 226-252, 258-269, 297-344, 350-373, 395-441, 447-471, 489-522, 528-542, 565-613, 619-643, 674-721, 727-750, 781-823, 829-849, 883-914, 920-934, 960-986, 992-1001, 1029-1073, 1079-1098 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_image_ops.py 1972 1820 8% 36-64, 70-82, 108-135, 141-151, 173-199, 205-215, 238-264, 270-280, 343-387, 393-414, 470-507, 513-532, 566-596, 602-618, 654-688, 694-711, 764-825, 831-861, 884-913, 919-931, 953-979, 985-994, 1045-1105, 1111-1140, 1172-1206, 1212-1227, 1256-1282, 1288-1298, 1329-1356, 1362-1373, 1423-1498, 1504-1543, 1560-1587, 1593-1604, 1631-1661, 1667-1679, 1736-1782, 1788-1812, 1830-1860, 1866-1878, 1926-1967, 1973-1992, 2014-2053, 2059-2068, 2097-2134, 2140-2157, 2197-2230, 2236-2250, 2292-2322, 2328-2340, 2384-2416, 2422-2435, 2489-2529, 2535-2552, 2616-2657, 2663-2677, 2719-2751, 2757-2771, 2803-2843, 2849-2869, 2904-2943, 2949-2958, 2987-3021, 3027-3043, 3074-3105, 3111-3124, 3146-3184, 3190-3207, 3228-3266, 3272-3289, 3311-3349, 3355-3372, 3393-3432, 3438-3455, 3475-3514, 3520-3537, 3557-3596, 3602-3620, 3709-3789, 3795-3838, 3927-4002, 4008-4048, 4066-4104, 4110-4128, 4146-4185, 4191-4209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_io_ops.py 1163 1009 13% 46-85, 91, 119-181, 187-217, 238-260, 266, 286-320, 326-340, 358-379, 385, 405-444, 450-459, 486-512, 517-527, 543-582, 588-597, 613-627, 633, 648-674, 680-690, 703-717, 723, 735-763, 769-779, 805-820, 826, 854-870, 876, 904-933, 939-950, 976-1004, 1010-1020, 1033-1040, 1045, 1057-1076, 1081-1087, 1106-1114, 1119, 1137-1157, 1162-1169, 1185-1199, 1205, 1220-1246, 1252-1262, 1301-1335, 1341-1355, 1387-1422, 1428-1443, 1487, 1493-1515, 1522, 1534, 1559-1579, 1584-1592, 1635-1657, 1662-1671, 1696-1718, 1723-1732, 1749-1776, 1782-1793, 1807-1834, 1840-1850, 1869-1895, 1901, 1919-1959, 1965-1983, 2003-2030, 2036, 2055-2096, 2102-2120, 2141-2163, 2169, 2189-2223, 2229-2243, 2264-2296, 2301-2308 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_linalg_ops.py 1226 1097 11% 33-59, 65-74, 88-114, 120-130, 143-169, 175-185, 199-229, 235-247, 262-293, 299-312, 328-360, 366-380, 396-431, 437-454, 467-493, 499-508, 529-560, 566-578, 601-639, 645-661, 692-731, 737-746, 769-795, 801-811, 848-880, 886-899, 991-1024, 1030-1045, 1076-1103, 1109-1118, 1161-1205, 1211-1223, 1244-1283, 1289-1298, 1311-1337, 1343-1352, 1382-1425, 1431-1443, 1473-1499, 1505-1514, 1543-1587, 1593-1606, 1663-1694, 1700-1714, 1746-1785, 1791-1800, 1873-1907, 1913-1929, 1968-2012, 2018-2030, 2052-2078, 2084-2093, 2129-2160, 2166-2178, 2221-2258, 2264-2280, 2306-2333, 2339-2349, 2379-2412, 2418-2431 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_list_ops.py 716 641 10% 42-74, 80-91, 119-155, 161-174, 189-218, 224-235, 270-304, 310-322, 339-367, 373-384, 403-431, 437-448, 471-502, 508-520, 540-571, 577-589, 605-631, 637-646, 675-707, 713-724, 744-771, 777-787, 801-828, 834-845, 865-897, 903-914, 932-958, 964-974, 998-1026, 1032-1044, 1067-1096, 1102-1114, 1142-1171, 1177-1190, 1210-1237, 1243-1254, 1277-1305, 1311-1323, 1345-1381, 1387-1401 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py 492 436 11% 48-63, 68-78, 108-142, 148-162, 193-225, 231-245, 267-293, 299-309, 364-400, 406-422, 445-476, 482-496, 519-560, 566-586, 604-630, 635-647, 665-691, 697-707, 729-773, 779-802, 820-848, 854-865, 884-923, 929-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_lookup_ops.py 714 631 12% 47-78, 84, 110-157, 163-184, 200-208, 213, 247-266, 271, 305-341, 346-363, 379-399, 404-412, 434-452, 458, 479-510, 516-527, 548-564, 570, 590-618, 624-635, 653-661, 666, 683-703, 708-716, 734-742, 747, 764-784, 789-797, 814-834, 839-846, 859-873, 879, 891-917, 923-932, 970-1015, 1021, 1059-1128, 1134-1168, 1195-1227, 1233, 1258-1296, 1302, 1327-1383, 1389-1414, 1441-1489, 1495-1516 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_manip_ops.py 46 31 33% 64-92, 98-109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py 5995 5342 11% 37-63, 69-78, 103-136, 142-157, 172-211, 217-226, 249-288, 294-303, 330-361, 367-377, 397-428, 434-448, 465-491, 497-507, 530-561, 567-580, 612-642, 648-660, 683-714, 720-733, 748-778, 784-797, 827-859, 865-878, 908-940, 946-959, 990-1029, 1035-1044, 1068-1107, 1113-1122, 1153-1192, 1198-1207, 1229-1268, 1274-1284, 1310-1349, 1355-1364, 1403-1437, 1443-1459, 1502-1536, 1542-1558, 1578-1617, 1623-1632, 1652-1691, 1697-1706, 1738-1777, 1783-1793, 1819-1845, 1851-1862, 1889-1921, 1927-1941, 1956-1988, 1994-2007, 2020-2046, 2052-2061, 2086-2113, 2119-2129, 2169-2195, 2201-2211, 2241-2271, 2277-2290, 2309-2339, 2345-2357, 2384-2410, 2416-2425, 2450-2489, 2495-2504, 2528-2567, 2573-2582, 2605-2644, 2650-2660, 2712-2748, 2754-2771, 2823-2859, 2865-2882, 2922-2958, 2964-2981, 2999-3038, 3044-3053, 3070-3096, 3102-3112, 3130-3156, 3162-3172, 3200-3233, 3239-3253, 3269-3308, 3314-3323, 3339-3378, 3384-3393, 3406-3432, 3438-3447, 3471-3502, 3508-3521, 3561-3587, 3593-3602, 3632-3671, 3677-3686, 3701-3740, 3746-3755, 3775-3814, 3820-3830, 3853-3892, 3898-3908, 3939-3978, 3984-3994, 4025-4064, 4070-4080, 4117-4148, 4154-4168, 4199-4238, 4244-4254, 4268-4294, 4300-4310, 4340-4379, 4385-4395, 4421-4451, 4457-4469, 4484-4510, 4516-4525, 4542-4568, 4574-4584, 4611-4650, 4656-4665, 4692-4731, 4737-4746, 4773-4812, 4818-4827, 4858-4897, 4903-4913, 4944-4983, 4989-4999, 5025-5064, 5070-5079, 5110-5150, 5156-5167, 5196-5235, 5241-5250, 5273-5312, 5318-5327, 5344-5370, 5376-5386, 5414-5445, 5451-5460, 5479-5518, 5524-5534, 5563-5577, 5580, 5583, 5590-5593, 5602-5619, 5643-5674, 5680-5693, 5715-5754, 5760-5770, 5794-5825, 5831-5844, 5868-5899, 5905-5918, 5940-5979, 5985-5995, 6015-6041, 6047-6057, 6082-6100, 6106-6116, 6133-6159, 6165-6175, 6188-6214, 6220-6229, 6246-6285, 6291-6300, 6324-6363, 6369-6379, 6397-6431, 6437-6451, 6476-6515, 6521-6531, 6554-6580, 6586-6596, 6620-6651, 6657-6670, 6720-6753, 6759-6772, 6803-6837, 6843-6860, 6905-6955, 6961-6988, 7019-7053, 7059-7076, 7106-7132, 7138-7148, 7174-7204, 7210-7222, 7243-7282, 7288-7298, 7316-7355, 7361-7370, 7387-7413, 7419-7429, 7458-7486, 7492-7503, 7531-7565, 7571-7584, 7623-7657, 7663-7677, 7709-7746, 7752-7768, 7794-7833, 7839-7848, 7864-7890, 7896-7905, 7920-7946, 7952-7961, 7978-8004, 8010-8020, 8062-8102, 8108-8118, 8161-8201, 8207-8217, 8259-8299, 8305-8315, 8357-8397, 8403-8413, 8455-8495, 8501-8511, 8567-8593, 8599-8610, 8625-8651, 8657-8668, 8683-8709, 8715-8724, 8741-8767, 8773-8783, 8804-8830, 8836-8845, 8869-8908, 8914-8923, 8947-8986, 8992-9001, 9026-9056, 9062-9076, 9104-9151, 9157-9181, 9203-9231, 9237-9248, 9271-9300, 9306-9318, 9344-9376, 9382-9396, 9417-9445, 9451-9462, 9485-9514, 9520-9533, 9561-9593, 9599-9613, 9660-9688, 9694-9705, 9752-9784, 9790-9804, 9819-9845, 9851-9860, 9877-9903, 9909-9919, 9939-9978, 9984-9993, 10013-10052, 10058-10068, 10093-10111, 10117-10127, 10151-10182, 10188-10201, 10226-10265, 10271-10280, 10304-10343, 10349-10358, 10375-10401, 10407-10417, 10441-10480, 10486-10496, 10518-10557, 10563-10573, 10623-10667, 10673-10685, 10731-10775, 10781-10793, 10838-10882, 10888-10900, 10946-10990, 10996-11008, 11024-11063, 11069-11079, 11093-11119, 11125-11135, 11151-11190, 11196-11206, 11228-11267, 11273-11283 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nccl_ops.py 124 103 17% 49-85, 91-104, 127-154, 160-170, 193-227, 233-249 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py 5462 5063 7% 49-95, 101-125, 153-199, 205-229, 259-306, 312-337, 366-413, 419-444, 479-517, 523-537, 580-619, 625-639, 669-681, 690-693, 702-715, 741-772, 778-790, 811-837, 843-853, 917-983, 989-1027, 1072-1142, 1148-1187, 1232-1302, 1308-1347, 1387-1436, 1442-1469, 1495-1541, 1547-1571, 1613-1687, 1693-1722, 1748-1794, 1800-1824, 1863-1916, 1922-1950, 1971-2008, 2014-2029, 2049-2086, 2092-2107, 2160-2228, 2234-2261, 2306-2388, 2394-2423, 2467-2549, 2555-2584, 2632-2674, 2680-2702, 2728-2772, 2778-2801, 2827-2870, 2876-2899, 2917-2956, 2962-2971, 2987-3013, 3019-3029, 3088-3148, 3154-3185, 3224-3262, 3268-3283, 3366-3426, 3432-3463, 3498-3537, 3543-3558, 3603-3655, 3661-3685, 3735-3782, 3788-3808, 3858-3905, 3911-3934, 3988-4036, 4042-4065, 4110-4162, 4168-4193, 4239-4291, 4297-4322, 4359-4398, 4404-4423, 4465-4512, 4518-4542, 4574-4601, 4607-4618, 4651-4677, 4683-4694, 4714-4753, 4759-4768, 4803-4861, 4867-4889, 4913-4958, 4964-4987, 5001-5030, 5036-5048, 5066-5096, 5102-5115, 5133-5159, 5165-5174, 5201-5247, 5253-5277, 5305-5351, 5357-5381, 5413-5460, 5466-5492, 5524-5572, 5578-5603, 5634-5681, 5687-5712, 5743-5791, 5797-5822, 5853-5889, 5895-5911, 5939-5991, 5997-6025, 6056-6091, 6097-6113, 6141-6193, 6199-6226, 6253-6288, 6294-6309, 6350-6406, 6412-6439, 6467-6497, 6503-6516, 6550-6594, 6600-6623, 6688-6742, 6748-6773, 6807-6840, 6846-6861, 6909-6964, 6970-7001, 7032-7098, 7104-7144, 7177-7251, 7257-7299, 7332-7403, 7409-7451, 7489-7545, 7551-7583, 7615-7682, 7688-7729, 7761-7831, 7837-7878, 7912-7989, 7995-8038, 8072-8148, 8154-8197, 8234-8317, 8323-8370, 8403-8474, 8480-8522, 8559-8642, 8648-8695, 8733-8789, 8795-8827, 8866-8925, 8931-8964, 9004-9076, 9082-9123, 9168-9245, 9251-9294, 9343-9396, 9402-9432, 9457-9513, 9519-9549, 9599-9653, 9659-9689, 9743-9804, 9810-9842, 9875-9934, 9940-9972, 10006-10050, 10056-10079, 10105-10140, 10146-10160, 10186-10221, 10227-10241, 10268-10304, 10310-10325, 10345-10384, 10390-10399, 10412-10438, 10444-10453, 10470-10496, 10502-10512, 10529-10555, 10561-10571, 10594-10633, 10639-10648, 10664-10690, 10696-10706, 10724-10750, 10756-10765, 10792-10821, 10827-10838, 10853-10892, 10898-10907, 10923-10949, 10955-10965, 10980-11019, 11025-11034, 11050-11076, 11082-11092, 11123-11153, 11159-11170, 11210-11242, 11248-11261, 11299-11330, 11336-11349 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_parsing_ops.py 1066 994 7% 52-103, 109-137, 164-209, 215-227, 252-291, 297-306, 326-361, 367-381, 400-434, 440-453, 520-582, 588-628, 708-782, 788-834, 925-1095, 1101-1211, 1314-1499, 1505-1632, 1689-1755, 1761-1800, 1886-2018, 2024-2118, 2138-2179, 2185-2195, 2211-2250, 2256-2265, 2289-2319, 2325-2337 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_array_ops.py 58 42 28% 76-119, 125-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_conversion_ops.py 189 163 14% 61-106, 112-130, 157-192, 198-214, 279-325, 331-355, 386-425, 431-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_math_ops.py 53 37 30% 63-95, 101-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_random_ops.py 511 460 10% 43-83, 89-109, 141-180, 186-204, 232-267, 273-289, 303-329, 335-345, 361-396, 402-418, 453-493, 499-519, 548-581, 587-602, 626-662, 668-684, 717-745, 751-767, 799-835, 841-858, 884-920, 926-942 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_resource_variable_ops.py 631 526 17% 38-57, 62-69, 87-106, 111-118, 144-155, 160-167, 189-208, 213-219, 247-264, 269-278, 330-356, 362-371, 389-422, 428-442, 467-479, 486-488, 497-507, 538-579, 585-603, 618-647, 653-664, 701-721, 726-734, 771-791, 796-804, 841-861, 866-874, 911-931, 936-944, 981-1001, 1006-1014, 1051-1071, 1076-1084, 1111-1131, 1136-1144, 1173-1203, 1209-1226, 1247-1265, 1271-1280, 1303-1332, 1338-1350 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_rnn_ops.py 350 306 13% 84-130, 136-157, 220-257, 263-275, 338-375, 381-393, 460-501, 507-525, 597-625, 631-641, 753-782, 788-798, 866-912, 918-938, 987-1022, 1028-1039 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_script_ops.py 136 104 24% 39-80, 86-104, 128-140, 144, 152, 154-157, 165-179, 194-226, 232-247 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sdca_ops.py 322 287 11% 37-76, 82-91, 170-329, 335-421, 497-617, 623-709, 730-751, 756 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sendrecv_ops.py 97 79 19% 42-91, 97-116, 138-173, 178-193 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_set_ops.py 187 160 14% 56-94, 100-116, 167-208, 214-232, 258-292, 298-312, 378-423, 429-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sparse_ops.py 1272 1150 10% 68-108, 114-132, 169-209, 215-232, 298-327, 333-343, 409-439, 445-455, 484-519, 525-539, 559-594, 600-614, 663-695, 701-716, 750-781, 787-799, 870-924, 930-965, 1040-1103, 1109-1143, 1173-1200, 1206-1218, 1242-1269, 1275-1287, 1315-1342, 1348-1360, 1427-1456, 1462-1474, 1505-1533, 1539-1550, 1586-1621, 1627-1642, 1686-1723, 1729-1744, 1780-1815, 1821-1836, 1880-1917, 1923-1938, 1973-2002, 2008-2019, 2061-2089, 2095-2106, 2153-2181, 2187-2200, 2225-2256, 2262-2274, 2309-2336, 2342-2353, 2387-2418, 2424-2438, 2472-2503, 2509-2523, 2572-2607, 2613-2630, 2652-2680, 2686-2698, 2734-2773, 2779-2799, 2845-2882, 2888-2904, 2985-3025, 3031-3049 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_special_math_ops.py 172 145 16% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_spectral_ops.py 714 630 12% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290, 303-329, 335-344, 364-403, 409-418, 438-477, 483-492, 512-551, 557-566, 586-625, 631-640, 660-699, 705-714, 734-773, 779-788, 819-849, 855-868, 900-930, 936-949, 981-1011, 1017-1030, 1058-1089, 1095-1108, 1137-1168, 1174-1187, 1216-1247, 1253-1266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_state_ops.py 513 432 16% 46-69, 75, 96-114, 120, 141-159, 165, 181-197, 203, 226-242, 248, 263-277, 283, 300-329, 335-346, 403-427, 432-444, 501-525, 530-542, 602-627, 632-645, 689-709, 715, 756-776, 782, 825-845, 851, 894-914, 920, 961-981, 987, 1044-1064, 1070, 1129-1149, 1155, 1213-1233, 1239, 1282-1302, 1308, 1355-1375, 1381, 1413-1435, 1441, 1456-1482, 1488, 1512-1538, 1544 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateful_random_ops.py 353 314 11% 37-67, 73-85, 106-126, 131-139, 157-191, 197-214, 233-264, 270-284, 304-335, 341-356, 378-409, 415-430, 451-482, 488-502, 522-553, 559-574, 601-630, 636-649 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateless_random_ops.py 354 315 11% 40-75, 81-95, 123-155, 161-177, 200-228, 234-246, 268-299, 305-318, 343-373, 379-393, 416-447, 453-467, 489-521, 527-541, 565-593, 599-612, 636-667, 673-687 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_string_ops.py 1301 1175 10% 64-128, 134-159, 178-217, 223-232, 257-299, 305-317, 363-399, 405-421, 451-477, 483-493, 518-550, 556-570, 592-619, 625-635, 655-691, 697-712, 734-775, 781-800, 826-861, 867-884, 912-941, 947-959, 980-1022, 1028-1040, 1083-1137, 1143-1166, 1212-1243, 1249-1262, 1313-1343, 1349-1362, 1378-1417, 1423-1432, 1453-1481, 1487-1497, 1526-1569, 1575-1586, 1625-1675, 1681-1697, 1718-1760, 1766-1778, 1884-1913, 1919-1933, 1989-2043, 2049-2073, 2134-2190, 2196-2221, 2271-2313, 2319-2337, 2363-2402, 2408-2417, 2495-2570, 2576-2598, 2647-2699, 2705-2720 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_summary_ops.py 386 335 13% 33-52, 57-63, 80-104, 109-119, 136-160, 165-176, 189-208, 213-219, 233-252, 257-264, 278-312, 318-332, 350-376, 381-394, 409-429, 434-442, 458-478, 483-492, 510-536, 541-554, 569-589, 594-602, 618-638, 643-652, 669-689, 694-704 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_tpu_ops.py 3018 2798 7% 59-98, 104-118, 141-169, 175-185, 206-267, 273-300, 315-335, 340-346, 370-397, 403-413, 436-468, 473-489, 537-598, 603-647, 701-779, 784-841, 855-884, 890-900, 916-954, 960-978, 1002-1037, 1042-1062, 1079-1104, 1109-1119, 1144-1181, 1186-1208, 1237-1278, 1283-1304, 1335-1379, 1384-1406, 1435-1477, 1482-1503, 1534-1578, 1583-1605, 1632-1673, 1678-1698, 1727-1770, 1775-1796, 1827-1868, 1873-1895, 1924-1965, 1970-1991, 2022-2066, 2071-2093, 2124-2167, 2172-2194, 2221-2261, 2266-2286, 2315-2358, 2363-2384, 2411-2452, 2457-2477, 2506-2549, 2554-2575, 2604-2643, 2648-2669, 2700-2743, 2748-2770, 2795-2836, 2841-2860, 2880-2916, 2922-2935, 2958-3003, 3009-3031, 3044-3063, 3068-3074, 3089-3108, 3113-3119, 3138-3175, 3181-3200, 3221-3261, 3267-3288, 3311-3342, 3348-3358, 3388-3436, 3442-3463, 3494-3543, 3549-3570, 3600-3648, 3654-3675, 3706-3755, 3761-3782, 3811-3859, 3865-3886, 3916-3965, 3971-3992, 4023-4072, 4078-4099, 4129-4177, 4183-4204, 4235-4284, 4290-4311, 4342-4391, 4397-4418, 4447-4495, 4501-4522, 4552-4601, 4607-4628, 4657-4706, 4712-4733, 4763-4812, 4818-4839, 4869-4917, 4923-4944, 4975-5024, 5030-5051, 5073-5121, 5127-5148, 5175-5207, 5212-5231, 5245-5264, 5269-5275, 5291-5317, 5323-5331, 5359-5393, 5399-5412, 5428-5454, 5460-5468, 5486-5525, 5531-5550, 5580-5663, 5668-5722, 5749-5793, 5799-5820, 5844-5872, 5878-5888, 5905-5931, 5937-5946 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_user_ops.py 43 28 35% 32-57, 63-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker.py 145 117 19% 39-45, 49-54, 83-132, 160-193, 202-208, 221-242, 254-268, 321-335, 339-345, 393-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker_v2.py 140 116 17% 37-43, 59-64, 78-93, 110-127, 150-197, 221-261, 266-281, 287-293, 332-335, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_impl.py 59 25 58% 168-169, 298-299, 342-357, 392-424, 433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_util.py 446 380 15% 61-69, 97-133, 137, 162-227, 231-234, 250-254, 279-289, 295-299, 303, 308-318, 323-355, 362-374, 383, 388-392, 405-411, 428, 444-457, 471-476, 490-716, 721-728, 734-766, 771-781, 786-805, 810-813, 817-821, 826-838, 846-867, 932-987 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/histogram_ops.py 28 13 54% 77-100, 146-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_grad.py 91 69 24% 39-50, 64-69, 84-91, 105-113, 131-156, 167, 188-381 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops.py 51 32 37% 195-203, 228-239, 245-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops_impl.py 1010 802 21% 75-81, 93, 108-113, 133-150, 171, 193, 212-236, 258, 277-301, 315-320, 360, 401, 422-447, 481, 515, 537-546, 581-594, 610-625, 641-657, 706-715, 772-847, 907-956, 990-1036, 1070-1155, 1183-1255, 1315-1333, 1470-1511, 1523-1590, 1628-1631, 1668-1671, 1698-1717, 1748-1752, 1784-1792, 1830-1843, 1885-1899, 1945-1963, 2012-2062, 2086-2097, 2122-2132, 2168-2175, 2230-2241, 2277-2289, 2327-2339, 2377-2385, 2426-2437, 2455-2457, 2471-2473, 2540, 2586-2656, 2694-2728, 2823-2824, 2930-2931, 2988-2992, 3064-3079, 3129-3133, 3175-3179, 3211-3215, 3238-3242, 3278-3282, 3305-3309, 3329-3354, 3393-3408, 3444-3466, 3471-3483, 3519-3565, 3627-3640, 3690-3771, 3826-3844, 3861-3885, 3893, 3906, 3919, 4036, 4053-4055, 4137, 4222, 4295-4300, 4354-4356, 4408, 4427 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops.py 509 330 35% 68, 76, 97, 113, 117, 132-134, 137, 215-222, 225-229, 237, 260-263, 266-268, 272, 300-303, 306-308, 312, 346-349, 352-354, 358, 407-409, 412-427, 431, 480, 482, 487, 495-518, 522, 563-565, 568-595, 598, 626-628, 631-663, 666, 688-690, 693, 696, 708-715, 726-733, 758-771, 785, 803-813, 828-845, 860-877, 903-913, 926, 939-944, 959-973, 988-1002, 1028-1041, 1056, 1075-1093, 1109-1129, 1144-1165, 1185-1186, 1189-1201, 1204, 1236, 1265, 1269, 1318, 1345, 1370, 1394, 1410-1425, 1443 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops_v2.py 241 105 56% 59, 67, 88-89, 168-171, 239-243, 257-259, 263, 300-303, 316-319, 323, 364-367, 380-381, 385, 429-432, 445-446, 450, 509, 511, 515, 518, 541, 544, 546, 551-552, 554-555, 561, 612-614, 628-650, 653, 684, 698-708, 711, 757, 796, 803, 864, 907, 947, 987, 1004, 1006, 1013-1017, 1037, 1048, 1054-1058, 1064, 1072-1076 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/initializers_ns.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/inplace_ops.py 41 16 61% 53-64, 90, 116, 142, 160-161, 191, 221, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/io_ops.py 120 54 55% 68-71, 93-94, 131-137, 142, 161-171, 193-206, 224-228, 240-244, 259-262, 278-282, 287, 298-301, 337-338, 369-371, 410-417, 446-451, 479-481, 511-512 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/adjoint_registrations.py 50 18 64% 37, 48, 54, 60-64, 76-80, 92, 106, 118-123, 134 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/cholesky_registrations.py 31 6 81% 36, 46, 57, 70, 83, 96 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/inverse_registrations.py 68 31 54% 40, 51, 57, 68, 74, 87, 132-185, 200, 213, 225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg_impl.py 400 323 19% 95-97, 126-128, 135-145, 150-161, 166-178, 183-203, 208-229, 262-339, 439-490, 496-538, 591-618, 623-635, 657-673, 746-802, 848-896, 949-959, 1000-1036, 1041-1068, 1073-1094 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator.py 387 259 33% 188-213, 218-222, 227, 232, 238, 242, 246, 250, 257-264, 269, 282, 288, 303-308, 322, 338-339, 344-349, 365-366, 381-382, 387-391, 404-407, 424-425, 430-435, 448-451, 468-469, 474-479, 483-493, 500-508, 527-528, 532-544, 560-561, 564-568, 586-587, 591-592, 598, 629-653, 656, 659-661, 690-696, 699-704, 718-723, 726-733, 747-752, 756-763, 768-771, 814-846, 850-852, 893-900, 914-917, 937-945, 966-970, 974-986, 990-991, 995, 1022-1023, 1026, 1039-1040, 1044, 1056-1059, 1062, 1078-1081, 1084-1093, 1105-1106, 1109, 1122-1127, 1137, 1142, 1155, 1161, 1166-1169, 1174-1176, 1190-1202, 1212-1215, 1220 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_addition.py 160 105 34% 99-138, 144-149, 168-187, 195-211, 217-223, 233-235, 253, 258, 264, 280-290, 301-302, 307-317, 330-331, 334, 346-347, 350-355, 367, 371-375, 412-424 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_adjoint.py 72 39 46% 116-155, 160, 163, 166, 169, 173-174, 178-179, 183, 187, 190-192, 195, 198-200, 203, 207, 210-212, 215, 218-221, 224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_algebra.py 112 49 56% 37-48, 53, 58, 63, 68, 73, 90-96, 113-119, 138-144, 163-169, 186-192, 228, 231, 270, 273, 315, 318, 361, 364, 404, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_diag.py 153 119 22% 156-215, 219, 223-238, 242-260, 263-273, 276-279, 282-285, 288-299, 302-308, 311-314, 317-341, 344, 348, 352, 356-362, 378-386 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_lower_triangular.py 191 152 20% 224-241, 250-252, 259-283, 290-301, 304-310, 314-318, 324, 328-344, 348-367, 370-417, 420-425, 428-431, 460-510, 513-520, 523-526, 529-550, 553, 557-563, 579-587 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_circulant.py 204 144 29% 93-126, 130-138, 171, 177, 180-186, 190, 194, 210-224, 239-260, 272-273, 285-286, 300-302, 305-321, 324-331, 345-350, 356, 366-368, 373, 381-402, 405-425, 428-430, 433-436, 439-450, 455-478, 490-512, 747, 758, 927, 1077, 1089-1095 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_composition.py 86 62 28% 147-187, 191, 195-211, 215-233, 239-248, 251-254, 257-260, 270-279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_diag.py 83 49 41% 143-168, 172-173, 178-179, 182-184, 188, 191, 196-205, 210, 217-220, 223-224, 227, 230-234, 237-240, 243, 246, 249-251, 254, 257-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_full_matrix.py 38 17 55% 137-151, 155-172, 177, 180, 183, 187, 190 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_householder.py 86 52 40% 126-156, 160-162, 168-169, 172-174, 177, 180, 185, 200-207, 211-212, 219, 223, 228, 231-236, 240-243, 247-254, 258, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_identity.py 234 171 27% 48-74, 79-83, 87, 91-98, 255-292, 295-301, 304-308, 311, 314, 317, 322-350, 354-358, 361, 364, 367, 371-380, 384, 396-400, 403, 406, 411-437, 442-470, 599-630, 634-638, 641-644, 647, 651, 656-657, 664-668, 671-675, 678, 681, 685-689, 693-702, 706, 718-731, 734, 739, 747 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_inversion.py 52 27 48% 117-168, 173, 176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 206 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_kronecker.py 205 169 18% 38, 46, 55-62, 171-231, 235, 239-257, 260-277, 317-387, 399-405, 409-415, 419-422, 433-504, 507-522, 525-544, 550-563, 566-570, 575-579 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py 148 108 27% 185-263, 268-281, 286-294, 300, 305, 310, 315, 320, 325, 328-331, 334-340, 344-360, 363-374, 380-394, 397-435, 442-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py 49 22 55% 141-159, 164-165, 171, 175, 178, 181, 184, 189, 193, 196, 200-201, 205, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_permutation.py 81 48 41% 143-156, 166-178, 183-184, 187-189, 192, 195-196, 199-220, 226, 231, 234-235, 240-241, 247, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_toeplitz.py 82 50 39% 142-159, 163-170, 176-178, 181-187, 190, 210-224, 229, 234-235, 239-263, 267, 271, 275-278 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_tridiag.py 122 87 29% 175-190, 199-213, 216-230, 236-266, 270-287, 290-294, 299-331, 334-341, 344-359, 369, 373 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_util.py 150 122 19% 105-116, 122-125, 130-135, 151, 160-161, 182-187, 201-209, 228-236, 241-243, 251-255, 311-354, 359-373, 383-467, 491-502 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_zeros.py 157 118 25% 179-232, 235-241, 244-248, 251, 256, 261, 266-294, 297-320, 323-326, 330-333, 336, 348, 353-399, 404-432, 437-441, 445, 449-456, 459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/matmul_registrations.py 65 28 57% 37-50, 65-66, 73-74, 82, 102-105, 112-115, 125, 142, 159, 175, 191, 209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/registrations_util.py 29 21 28% 27-44, 49-62, 71-78, 86-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/solve_registrations.py 55 22 60% 37-50, 67, 75-76, 83-84, 92, 112, 129, 146, 162, 181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/conjugate_gradient.py 52 37 29% 76-136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/gen_sparse_csr_matrix_ops.py 624 562 10% 48-77, 83-95, 109-137, 143-154, 177-207, 213-224, 239-266, 272-283, 303-329, 335-347, 399-459, 465-495, 518-544, 550-560, 573-599, 605-614, 677-703, 709-719, 739-766, 772-782, 797-825, 831-843, 937-965, 971-983, 1083-1133, 1139-1164, 1183-1215, 1221-1234, 1248-1275, 1281-1291, 1307-1335, 1341-1353 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_grad.py 127 101 20% 30-34, 40-41, 65-66, 73, 80-81, 95-169, 175-222, 231-233 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_ops.py 157 96 39% 56, 61-69, 74, 80-87, 105-116, 124-144, 181-241, 253, 257, 261, 265, 269, 273, 277, 281, 286, 289, 294, 301, 307, 310, 330-352, 357, 360-370, 373, 376-377 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_grad.py 389 345 11% 53-54, 61-366, 372-378, 392-443, 449-454, 462-480, 486-510, 516-524, 537-601, 609-633, 639-675, 690-811, 816-819, 824-827, 833-848, 854-867, 881-898, 917-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops.py 166 121 27% 65-79, 139-140, 181-186, 227, 293-367, 393-398, 419-424, 446-447, 469-470, 535-540, 607, 682-761 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops_impl.py 35 24 31% 42-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/list_ops.py 141 87 38% 49-52, 60, 68, 76, 85, 97, 110-114, 127, 139, 148, 161-170, 176, 184-189, 194, 201-209, 214-217, 227-243, 249-259, 265-274, 279-281, 287-294, 301-311, 317-325, 350-371 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/logging_ops.py 147 89 39% 48, 112, 120-124, 129, 258-375, 383, 387-390, 426-429, 487-491, 542-552, 583-586, 606-610, 624-634, 667-670 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/lookup_ops.py 638 452 29% 63, 81-84, 99-103, 117-119, 122, 127, 132, 137, 141, 145, 165-179, 182, 187, 198-199, 217-236, 243, 279-291, 294-303, 307, 319-325, 364, 372, 385-386, 391, 396, 400, 405-411, 428-445, 461-467, 587-626, 641-654, 658-669, 712, 765, 807-814, 818-820, 896-933, 936-938, 941-944, 948-951, 956, 960-962, 966, 970-975, 979-988, 1005-1034, 1106-1144, 1147-1149, 1152-1155, 1159-1161, 1165, 1169-1174, 1191-1215, 1223-1226, 1311-1347, 1408-1446, 1520-1537, 1588-1599, 1645-1667, 1673-1694, 1698, 1709-1711, 1729-1737, 1756-1762, 1781-1789, 1801-1806, 1810, 1820-1826, 1829-1833, 1903-1930, 1936-1950, 1954, 1965-1967, 1986-1993, 2012-2020, 2039, 2057-2066, 2084, 2096-2102, 2106, 2116-2122, 2125-2129 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/loss_reduction.py 16 1 94% 68 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/losses.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/losses_impl.py 237 180 24% 61, 71-72, 86-87, 112-129, 134-135, 174-203, 245-255, 296-311, 348-361, 414-437, 480-493, 548-591, 633-643, 690-707, 756-778, 805-830, 873-887 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/util.py 88 59 33% 58-121, 139-145, 158-175, 189-190, 204, 217, 231-235, 264-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/manip_grad.py 12 4 67% 28-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/manip_ops.py 12 1 92% 30 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/map_fn.py 85 63 26% 149-287, 417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/math_grad.py 1177 911 23% 37, 42-43, 48-49, 56-64, 91-136, 144, 152-214, 219-235, 241, 246, 252-266, 277-315, 321, 327-334, 340-341, 349-350, 358-359, 366-367, 374-375, 382-383, 389-399, 405, 411, 426-445, 452-464, 470, 476, 482, 504-530, 535-536, 542, 548-549, 555-556, 561-566, 571-576, 581-586, 591-592, 597-605, 611-612, 618-625, 631-637, 643-650, 656-662, 668-674, 680-690, 697-707, 714-724, 731-734, 740-743, 749-752, 758-761, 767-773, 779-785, 790-793, 799-803, 809-814, 820-822, 829-831, 838-844, 850-857, 863-866, 872-874, 880-882, 888-890, 896-901, 907-914, 920-938, 944-963, 970-971, 978-1004, 1014-1032, 1040-1058, 1065-1068, 1073-1077, 1083-1084, 1090-1093, 1099-1102, 1108-1116, 1122-1132, 1138-1148, 1154-1160, 1166-1173, 1180, 1185-1189, 1197-1225, 1231-1259, 1265-1300, 1306-1315, 1324-1340, 1350, 1356-1366, 1371, 1377-1393, 1404-1420, 1431-1488, 1492-1498, 1503-1535, 1541, 1547, 1553-1587, 1605-1608, 1614-1633, 1638-1649, 1654-1665, 1671-1698, 1705-1746, 1755, 1760, 1765, 1771, 1777-1797, 1803-1835, 1845-1850, 1857-1858, 1864-1865, 1871-1878, 1884, 1890, 1899-1908, 1913-1915, 1920-1923, 1931-1940, 1945-1974, 1980-1988 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/math_ops.py 1023 622 39% 138-140, 171-173, 187-189, 224-226, 264-268, 276, 292-293, 296, 299, 302, 327-332, 381, 389, 399, 410, 436, 461-471, 477-478, 505-506, 541-552, 580-591, 619-625, 653-658, 693-698, 725-729, 775-792, 814-825, 844, 863, 882, 901, 920, 939, 958, 985-997, 1000-1002, 1008-1010, 1015-1016, 1054-1070, 1075-1090, 1107-1118, 1151, 1177, 1195-1198, 1215-1223, 1259-1260, 1273-1276, 1285-1288, 1354, 1399, 1445, 1481, 1486-1494, 1499-1505, 1564-1592, 1597-1598, 1609-1627, 1632, 1637-1640, 1692-1697, 1741, 1750-1751, 1792, 1864-1873, 1928-1934, 2001-2006, 2061-2062, 2110-2114, 2157-2160, 2191-2192, 2236-2241, 2281-2286, 2322-2323, 2367-2372, 2419, 2428-2429, 2480-2485, 2525-2526, 2579-2584, 2624-2625, 2680-2685, 2726-2745, 2787-2789, 2912-2983, 3075-3083, 3096-3106, 3113-3123, 3142-3147, 3166-3182, 3223-3239, 3282-3310, 3317, 3366-3368, 3387-3389, 3447-3464, 3497, 3565-3567, 3618-3620, 3673-3675, 3709-3720, 3734-3748, 3765-3778, 3819-3824, 3868-3873, 3941-3949, 4017, 4056-4064, 4102, 4132-4140, 4174, 4232-4361, 4416-4431, 4460-4463, 4499-4500, 4517-4518, 4532-4533, 4560, 4597, 4643, 4669-4670, 4694 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/metrics.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/metrics_impl.py 796 681 14% 76, 111-161, 180-209, 226-228, 255-277, 282-311, 358-393, 446-457, 508-621, 625-626, 720-859, 912-919, 972-994, 1045-1099, 1154-1202, 1257-1270, 1324-1331, 1382-1417, 1465-1471, 1502-1518, 1556-1569, 1612-1626, 1665-1678, 1721-1735, 1774-1787, 1830-1844, 1883-1896, 1939-1953, 2006-2048, 2103-2129, 2180-2222, 2226-2232, 2246-2265, 2286-2288, 2323-2334, 2375-2385, 2417-2429, 2470-2480, 2558-2565, 2630-2657, 2710-2735, 2788-2806, 2868-2908, 2930-2962, 2985-3000, 3035-3089, 3142-3182, 3199-3220, 3236, 3306-3322, 3360-3372, 3413-3423, 3481-3512, 3526, 3613-3620, 3690-3750 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nccl_ops.py 85 57 33% 47, 64-72, 93, 110, 127, 146, 163-170, 183-186, 202-207, 212-236, 241-251, 257-260, 264-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn_grad.py 344 217 37% 43, 71, 105, 127, 148-149, 171-172, 194-195, 216, 227, 238, 250, 266, 300-302, 319-320, 342-346, 364-384, 406-407, 413, 418-419, 426-427, 434, 439-440, 446-448, 453-455, 461, 466, 471, 480-484, 489, 494-495, 510-511, 522-536, 548-562, 568-582, 608, 630, 644-648, 656, 667, 678, 690-692, 704-705, 717, 733-735, 751, 782, 804, 830-833, 861-908, 913, 918, 923, 954-1002, 1020-1040, 1045, 1050-1051, 1065, 1081-1105, 1126-1138 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn_impl.py 390 293 25% 86-109, 161-186, 240, 299-315, 382-383, 419-439, 471-477, 495-500, 520-536, 584-589, 616-617, 642-646, 661-667, 690-708, 763-794, 852, 920-957, 1021, 1061-1088, 1116, 1137-1149, 1187-1214, 1250, 1271-1328, 1347, 1415-1421, 1498-1545, 1594-1597, 1641, 1660-1663, 1726-1848, 1941, 2046-2063, 2142, 2240-2258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn_ops.py 1086 861 21% 62-81, 130-142, 169-214, 220, 231, 296-299, 317-319, 468-482, 512-588, 593-635, 638, 646-658, 686-717, 739-760, 887-890, 909, 935-1001, 1030-1075, 1085, 1095-1106, 1200-1274, 1371, 1523, 1545-1566, 1637-1664, 1721, 1783-1817, 1910, 1997-2006, 2071-2072, 2133-2136, 2202-2206, 2274-2283, 2343-2448, 2460-2462, 2481-2482, 2549-2551, 2614-2623, 2700-2714, 2729, 2769-2774, 2785-2794, 2821-2824, 2853-2856, 2861, 2882-2884, 2910-2916, 2921-2943, 2968-3022, 3062-3065, 3090-3092, 3119-3122, 3147-3149, 3154-3158, 3218, 3277-3338, 3405-3413, 3472-3539, 3590, 3621-3647, 3680-3691, 3722-3730, 3763-3781, 3806-3814, 3848-3874, 3910-3923, 3954-3972, 3996-4004, 4039-4047, 4110-4116, 4137-4144, 4160-4172, 4179-4190, 4198-4208, 4214-4217, 4235-4240, 4259-4264, 4269-4289, 4330-4343, 4419-4464, 4494, 4524, 4608, 4685-4693, 4758, 4814-4820, 4828-4838, 4888-4890, 4954-4959, 4999-5000, 5005 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/numerics.py 39 21 46% 46-49, 64-69, 99-122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/op_selector.py 193 169 12% 26-29, 34-40, 54-59, 72-77, 96-115, 126-131, 148-161, 174-175, 188-194, 213-226, 230-234, 267-308, 319-321, 325, 339-363, 393-419 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/optional_grad.py 12 2 83% 27, 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/control_flow_ops.py 165 134 19% 64-106, 111-114, 122-132, 182-201, 206-219, 224-317, 398-407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/gradients.py 57 45 21% 48-80, 112-147 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/pfor.py 1990 1462 27% 75-80, 103-111, 126-225, 229, 234, 239-242, 247, 252, 257, 261-265, 269, 274, 297-340, 344-354, 369-396, 400-464, 474-483, 487-514, 520-559, 604-679, 694-696, 705-711, 721-742, 746, 750, 753-754, 757-768, 771-781, 785, 789, 792, 796, 799-800, 906, 915-919, 946-953, 957-982, 990-993, 997, 1001, 1022-1060, 1075, 1088, 1101, 1105-1106, 1167-1184, 1188-1192, 1224-1239, 1243-1257, 1262-1266, 1279-1292, 1296-1297, 1300-1302, 1306-1329, 1332-1516, 1521, 1526, 1530, 1534, 1544, 1556-1558, 1563-1565, 1570-1572, 1580-1582, 1587-1589, 1594-1605, 1625-1633, 1641-1662, 1667-1683, 1704-1725, 1740-1787, 1792-1812, 1820-1832, 1837-1891, 1897-1898, 1909-1910, 1915-1916, 1923-1926, 1931-1941, 1946-1959, 1966-1969, 1975-1980, 1986-1989, 1996-1999, 2009-2019, 2024-2031, 2038-2046, 2053-2062, 2067-2074, 2079-2086, 2092-2097, 2102-2105, 2110-2114, 2120-2125, 2130-2133, 2138-2142, 2147-2151, 2156-2159, 2164-2167, 2172-2175, 2180-2182, 2188-2249, 2255-2259, 2264-2268, 2275-2293, 2308-2328, 2348-2396, 2405-2415, 2420-2427, 2438-2443, 2449-2453, 2459-2465, 2470-2493, 2501-2523, 2529-2535, 2548-2578, 2586-2599, 2604-2606, 2709-2712, 2717-2722, 2727-2732, 2737-2741, 2746-2747, 2754-2759, 2764-2766, 2773, 2779-2780, 2785-2788, 2793-2803, 2820-2829, 2834-2849, 2854-2859, 2866-2867, 2880-2893, 2899-2922, 2927-2956, 2974, 2983-3017, 3022-3023, 3028-3029, 3034-3036, 3041-3047, 3052-3059, 3064-3068, 3076-3081, 3089-3094, 3139-3157, 3162-3167, 3177-3184, 3190, 3195-3237, 3242-3284, 3289-3295, 3300-3337, 3342-3380, 3385-3403, 3445-3451, 3455-3459, 3464-3485, 3492-3503, 3511-3520, 3532-3548, 3553-3580, 3587-3610, 3616-3628, 3632-3638, 3642-3662, 3668-3715, 3728, 3738-3741 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_config.py 305 240 21% 207-228, 303, 323, 358, 412-434, 454-463, 467, 471, 475, 491-516, 520-531, 535-537, 541-548, 553-568, 572-589, 593-603, 607-619, 623-627, 632-665, 704-758, 778-803, 827-885, 893-901 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_ops.py 165 110 33% 62-76, 304-313, 318, 339-370, 406, 442-447, 545-567, 597-689, 779-801, 830-833, 867-875, 907-918, 965, 1013-1020, 1033-1047 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/partitioned_variables.py 68 48 29% 104-154, 181-218, 233-237, 288-311 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/proto_ops.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_array_ops.py 187 158 16% 92-201, 230-243, 273-305, 332-373, 431-448, 474-477, 502-506, 520-528, 574-634, 660-689 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_ops.py 46 34 26% 64-124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py 61 44 28% 69-141, 147-183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_concat_ops.py 110 87 21% 67-70, 115-118, 136-202, 219-238, 253-289, 295-297, 302-310, 315-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_config.py 6 1 83% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_conversion_ops.py 58 39 33% 36-39, 49-52, 57, 64-106, 113-137, 141, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_dispatch.py 243 116 52% 82, 90-102, 114, 119-160, 175, 182-231, 246, 252-253, 258-261, 264-284, 417, 427, 435-437, 441, 445-447, 452-456, 515, 548, 550, 559-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_factory_ops.py 121 99 18% 78-84, 133-143, 168-240, 260-271, 277-310, 336-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_functional_ops.py 39 25 36% 70-91, 112-128 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_gather_ops.py 98 80 18% 88-118, 167-261, 270-295 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_getitem.py 161 136 16% 97-103, 124-187, 200-226, 244-340, 360-361, 381-389, 394-406, 437-456, 462-471 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_map_ops.py 132 103 22% 169-331, 360-364, 372-386, 395-403, 409-418, 423-427, 434-459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_math_ops.py 172 110 36% 88-105, 112-114, 193-256, 262, 271, 280, 289, 298-308, 313-324, 469-542, 551, 561, 572, 583, 594-608, 612, 618-619, 626-627 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_operators.py 44 1 98% 74 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_squeeze_op.py 53 40 25% 52-122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_string_ops.py 192 153 20% 59-78, 121-175, 219-220, 280-281, 322-325, 385-394, 400-452, 493-512, 563-575, 626-643, 648, 721-803 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_tensor.py 823 674 18% 268-311, 363-428, 472-497, 531-553, 587-608, 637-659, 718-795, 837-858, 888-897, 927-936, 959-983, 992, 1011-1020, 1031-1032, 1056, 1079, 1105, 1131-1134, 1161-1166, 1192-1196, 1227-1233, 1255-1266, 1290-1291, 1315-1316, 1345-1360, 1377-1383, 1410-1435, 1456-1466, 1491-1494, 1509-1533, 1570-1583, 1652-1793, 1828-1839, 1878-1905, 1927-1930, 1985-2001, 2030-2031, 2038-2041, 2075-2086, 2096-2099, 2105-2108, 2112-2117, 2140-2143, 2151, 2158, 2162, 2167, 2182-2212, 2226, 2241-2257, 2260, 2264-2277, 2280-2283, 2286-2297, 2308, 2311-2321, 2324-2333, 2336-2353, 2356, 2365, 2369, 2372, 2375, 2379, 2416-2432, 2440-2444, 2448-2449, 2453-2455, 2459, 2486-2489, 2511-2537, 2545-2557, 2561, 2566, 2571-2574, 2591-2641, 2646, 2664-2670, 2686-2691, 2717-2730 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_tensor_shape.py 270 221 18% 104-144, 147, 165-177, 183-190, 197-203, 207-215, 221-225, 234, 243, 248, 253, 258, 276-292, 319-372, 376-381, 385-410, 414-423, 426-432, 435-439, 456-473, 493-502, 508-513, 519-598, 603-625 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_tensor_value.py 50 27 46% 43-53, 68-71, 76-81, 86-87, 92, 95, 98, 103-107 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_util.py 30 16 47% 48-52, 67, 98-113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_where_op.py 52 37 29% 97-108, 113-134, 139-153, 161-164 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/segment_id_ops.py 50 34 32% 55-71, 98-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_grad.py 32 18 44% 29-32, 58-71, 99-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py 147 77 48% 86-96, 136-153, 187-197, 276, 278-280, 292, 298-299, 340-341, 371-386, 415-416, 443-444, 449-451, 459-465, 544-558, 598, 637-643 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py 686 328 52% 65-70, 76-81, 97-102, 126-143, 151, 161-163, 184-191, 254-255, 270, 291-300, 318-320, 447-452, 464-468, 471, 474, 477, 480-491, 506, 519, 522-524, 527-530, 535-538, 548, 554, 559, 564-566, 576, 581, 589, 593, 597-599, 602-604, 628, 641-643, 665-669, 673-688, 692-698, 713-743, 747-749, 765, 786-792, 810-816, 851, 855, 878-880, 898-900, 919-921, 940-942, 960-962, 980-982, 1000-1002, 1050-1052, 1101, 1150, 1199, 1206-1207, 1222, 1225, 1228, 1231, 1236, 1240, 1245, 1251, 1257, 1262, 1267, 1272, 1277, 1415-1421, 1512, 1517, 1527, 1531, 1534-1536, 1549-1550, 1571-1576, 1586, 1595-1632, 1639-1642, 1649, 1655, 1657, 1671-1731, 1790-1791, 1800-1807, 1846, 1853-1856, 1865-1868, 1871, 1874, 1877-1881, 1884-1885, 1889-1890, 1894-1895, 1899-1900, 1904-1905, 1909-1910, 1914-1915, 1919-1920, 1924-1925, 1929-1930, 1934-1935, 1939-1940, 1943-1944, 1947-1948, 1954, 1963, 1967-1973, 1980-1987, 1992, 1997-1999, 2050-2061, 2076, 2079, 2082-2083 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resources.py 38 19 50% 53-57, 62, 67, 86-104, 118-120 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn.py 455 407 11% 55-66, 82-93, 110-123, 127-130, 135-142, 163, 230-315, 330-357, 449-514, 638-716, 760-936, 1105-1259, 1330-1444, 1482-1538, 1594-1635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell_impl.py 509 368 28% 63-68, 86-101, 127-164, 170-179, 206-212, 232-244, 247-260, 269, 274, 279, 282-309, 328-340, 344, 385, 420-436, 440, 444, 448-462, 466-471, 474-480, 526-544, 548, 552, 556-582, 586-603, 606-614, 633-637, 697-719, 723, 728, 732-746, 762-794, 797-805, 896-941, 945, 949, 953-993, 1019-1071, 1074-1089, 1100-1104, 1123, 1147, 1151-1158, 1162-1168, 1179, 1190, 1200, 1234-1255, 1261-1264, 1268, 1271-1277, 1281-1287, 1291-1301, 1305-1327, 1345-1346, 1350-1354 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell_wrapper_impl.py 224 164 27% 109-173, 179-183, 187, 191, 195, 198-199, 202-203, 209-215, 225-250, 271-289, 293-308, 312-319, 337-338, 342, 346, 349-350, 369-381, 385-396, 400-407, 424-425, 429, 433, 436-438, 442-443, 446-448, 453-465, 471-494, 498-504, 508-515 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_grad.py 14 5 64% 26-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/script_ops.py 174 76 56% 59-67, 83-85, 102-119, 124-150, 171-172, 183, 203-212, 230-253, 257, 282, 295, 319, 336, 347, 357-364, 452-457, 517-524, 536, 555 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sdca_ops.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/session_ops.py 128 85 34% 38, 56-60, 63-64, 67, 71-76, 85, 90, 94-99, 103-108, 117-118, 123-124, 129-130, 135, 173-178, 214-219, 239-243, 247, 251, 256-267, 272-288, 293-302 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets_impl.py 55 31 44% 51-57, 81-90, 118-133, 200-201, 277-280, 356-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/dct_ops.py 77 60 22% 33-47, 97-179, 223-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/fft_ops.py 217 146 33% 35-42, 48-60, 65-108, 116-140, 150-170, 198, 203-204, 209-212, 217-218, 223-226, 231-232, 237-240, 250-320, 336-360, 393-403, 434-444 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mel_ops.py 57 39 32% 46-48, 64-66, 73-89, 161-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mfcc_ops.py 20 9 55% 89-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/reconstruction_ops.py 60 48 20% 54-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/shape_ops.py 82 68 17% 33-54, 107-214 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/signal.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/spectral_ops.py 131 103 21% 70-94, 120-155, 224-276, 281-285, 331-365, 426-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/util_ops.py 33 20 39% 47-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/window_ops.py 77 52 32% 47-51, 70-90, 110-117, 135-141, 165, 192, 217-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sort_ops.py 53 33 38% 65-66, 107-109, 128-140, 155-204, 209-211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_grad.py 130 88 32% 50-60, 83-96, 101-103, 109-115, 136-145, 166-201, 206, 212-241, 247, 253, 273-286, 291, 297, 306-312, 317-322 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_ops.py 516 356 31% 66-70, 87-91, 95-101, 118-124, 147-179, 202-211, 324-331, 336-366, 430-434, 490-517, 550, 595, 614-655, 678-680, 720-731, 784-830, 840, 889-914, 955, 995-1008, 1062, 1129-1144, 1211-1216, 1259-1269, 1319-1333, 1387-1392, 1435-1445, 1490-1494, 1552-1564, 1663, 1672-1718, 1751-1765, 1829-1871, 1925-1935, 1958, 1977-1979, 2013, 2041-2043, 2107-2114, 2179-2187, 2401-2419, 2476-2480, 2508-2519, 2546-2557, 2595-2619, 2643-2645, 2683-2685, 2756-2777, 2795-2806 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/special_math_ops.py 357 299 16% 87-101, 129-130, 157-158, 186-187, 214-215, 242-243, 266-267, 290-291, 296-303, 319-324, 404, 409-468, 490-551, 580-679, 684-687, 693-700, 707-714, 723-726, 731-802, 807-853, 861-869, 881-973 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/standard_ops.py 83 1 99% 115 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_grad.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_ops.py 131 81 38% 44-52, 74, 101-114, 130-133, 161-164, 192-195, 224-228, 249-251, 301-304, 363-366, 415-418, 478-481, 532-535, 596-599, 648-651, 700-703, 755-758, 810-813, 872-914 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/stateful_random_ops.py 279 177 37% 90, 95-97, 110-136, 140-145, 149-151, 155, 168-182, 206-207, 212-216, 220-223, 230-231, 235, 239, 243-250, 254, 257, 270-278, 369-387, 410, 439-444, 467-473, 500-507, 517-519, 529-530, 541-548, 552, 557, 562, 565, 585-589, 600, 621-626, 629, 657-664, 667, 705-721, 737-741, 789-794, 810, 832-840, 880-890, 913-916, 939 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/stateless_random_ops.py 104 65 38% 99-129, 187-197, 280-294, 349-355, 386-394, 430-439, 478-479, 517-518, 524-525 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/string_ops.py 141 56 60% 62-68, 99-108, 181-188, 236-249, 294-304, 309-318, 332-339, 380-383, 421, 427, 436, 444, 452, 494, 503-505, 537, 546-548, 582 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/summary_op_util.py 32 19 41% 36-39, 61-68, 93-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/summary_ops_v2.py 465 329 29% 93-101, 110, 115, 134-139, 145-151, 156, 161, 172, 191, 202, 208, 212, 216, 220, 232-244, 248-250, 255-265, 269-274, 278-280, 284-292, 299, 303, 306, 309, 312, 342-354, 376-406, 444-456, 497-508, 524, 528-530, 535, 549-551, 563-567, 604-611, 643-678, 704-730, 745-762, 768-783, 806-815, 821-830, 836-849, 855-866, 899-910, 932, 951-961, 969, 976-978, 982-985, 989-993, 1006-1015, 1042-1055, 1081-1098, 1128-1145, 1180-1199, 1225-1255, 1262-1273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/template.py 245 168 31% 154, 212-225, 236-239, 289-314, 317-378, 381-393, 398, 403, 408, 413-419, 424, 429-433, 439-441, 446-450, 455-459, 464, 469, 474, 482, 489-494, 497, 501-515, 519-522, 528, 531, 535, 583-596, 599-655, 661-681, 687-689, 695-697, 703-705, 711-713, 719 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/tensor_array_grad.py 93 49 47% 70-80, 102-111, 129-137, 159-168, 184-192, 214-224, 240-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/tensor_array_ops.py 537 380 29% 101-167, 171, 175, 179, 183, 195-199, 214-220, 224-225, 233-250, 254-262, 266-279, 283-290, 294-307, 311-319, 324-326, 332-347, 352-370, 374-377, 383, 432-473, 477, 481, 485, 491, 503-507, 511-512, 516, 520-527, 531-543, 547-558, 562-568, 572-582, 587-595, 600-610, 615-633, 637-640, 644, 688-717, 722, 726, 731, 735, 739, 742, 749-778, 793-828, 832-834, 837-841, 845-852, 857-860, 864-878, 882-888, 892-897, 902-929, 933-934, 937-938, 1058-1084, 1089, 1094, 1099, 1104, 1109, 1116, 1126, 1129, 1141, 1159, 1173, 1190, 1204, 1224, 1243, 1262, 1266, 1271, 1278-1303, 1309-1310, 1336-1339, 1343-1347, 1354-1357, 1363, 1368, 1371-1383, 1388-1394, 1398-1402, 1409, 1413, 1417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/unconnected_gradients.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variable_scope.py 822 585 29% 79-105, 109, 113, 130-135, 153-184, 253, 266-267, 283-285, 396-553, 669-795, 844-967, 984-1000, 1012-1013, 1016-1020, 1026, 1037, 1047, 1079, 1141-1145, 1149, 1153, 1157, 1161, 1165, 1173, 1177, 1181, 1185, 1189, 1193, 1197, 1201, 1206, 1212, 1216-1219, 1223, 1227, 1231-1232, 1236, 1240, 1244, 1265-1300, 1335-1375, 1407-1410, 1413-1415, 1418, 1441-1446, 1451-1457, 1481-1489, 1492, 1495, 1499, 1505, 1520-1534, 1557, 1701-1705, 1820-1828, 1890-1939, 1951-2001, 2004-2012, 2017-2031, 2036-2044, 2265-2296, 2301-2322, 2334-2442, 2445-2455, 2475-2490, 2507-2528, 2536-2543, 2548-2555, 2578, 2580, 2600, 2619-2634, 2656, 2734-2735 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py 802 471 41% 53-54, 58-59, 117, 120-125, 137, 160-161, 169-170, 204, 242, 436, 439, 458, 469, 477, 481, 485, 489, 521, 545-546, 562, 572, 590, 608, 626, 642, 658, 675, 692, 708, 724, 740, 786, 833, 880, 927, 943, 958, 981, 1018-1026, 1032-1040, 1072, 1078-1082, 1087-1091, 1096-1100, 1114, 1128, 1140, 1145, 1150, 1155, 1160, 1165, 1174, 1178, 1194, 1199, 1207, 1210, 1214, 1255, 1293-1304, 1309-1312, 1324-1336, 1645-1654, 1669-1674, 1745-1859, 1869-1904, 1908, 1927, 1938, 1954, 1962-1963, 1967, 1971, 1975, 2007, 2021, 2031, 2049-2053, 2071-2075, 2093-2097, 2114-2116, 2138-2140, 2163-2165, 2188-2190, 2212-2214, 2236-2238, 2260-2262, 2314, 2367, 2416, 2465, 2471, 2505, 2511-2519, 2533, 2538, 2543, 2548, 2553, 2558, 2563, 2572, 2584-2604, 2607-2611, 2614-2618, 2621-2626, 2629-2634, 2637-2642, 2645-2650, 2653-2658, 2688-2694, 2702-2713, 2731-2736, 2754-2787, 2802-2813, 2848-2879, 2883, 2886-2890, 2893-2896, 2908-2924, 2936-2937, 2942-2951, 2955, 2959, 2963, 2969, 2972, 2975, 2978, 2981-3004, 3007-3012, 3015-3020, 3023-3028, 3061, 3068, 3085, 3114, 3131, 3153, 3175, 3199-3201, 3209, 3221-3223, 3231, 3243-3245, 3253, 3268, 3293-3311, 3332-3359 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/weights_broadcast_ops.py 63 48 24% 34-43, 48-53, 81-133, 154-168 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/analytics.py 6 1 83% 28 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/app.py 14 3 79% 31, 38-40 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/benchmark.py 216 164 24% 58-76, 100-158, 167, 175-200, 222-240, 268-269, 281-284, 294-295, 343-408, 419-420, 434-456, 466-478 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/build_info.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/device_context.py 6 1 83% 22 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/flags.py 59 18 69% 52-54, 56, 78, 81-86, 89, 92, 95, 98, 101, 104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/gfile.py 29 1 97% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/googletest.py 103 67 35% 56, 61-66, 72-94, 107, 112, 134-135, 143, 147, 150, 154-155, 184-218, 229-232, 251-258, 271-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/remote_utils.py 6 1 83% 22 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/resource_loader.py 47 22 53% 47-48, 60, 72-100, 121-125, 136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/self_check.py 22 12 45% 26-27, 44-53 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/sysconfig.py 35 17 51% 42-43, 53-54, 64-67, 77-86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/test.py 43 9 79% 41, 57-58, 70, 84, 90, 96, 102, 108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/tf_logging.py 158 67 58% 49-58, 65-90, 106, 129-130, 148, 158, 163, 168, 173, 178, 201, 206, 226-227, 243-244, 259-260, 266-267, 272-275, 285-309, 315, 321, 327-329 /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/internal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/internal/flops_registry.py 179 93 48% 48-49, 54-57, 66-68, 74, 80, 87, 93, 99, 105, 111, 117-121, 133, 142-144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234, 243-249, 256, 263, 270, 277, 285, 296-297, 303-321, 327, 333, 339-349, 356-378, 396-407, 420-430, 443-447 /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/model_analyzer.py 157 119 24% 53-56, 67-103, 114-122, 175-183, 187, 199-203, 219-226, 236-243, 253-260, 270-277, 287-291, 302, 306, 339-381, 401-420 /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/option_builder.py 87 53 39% 68-71, 97, 126, 171, 199, 213-214, 239-243, 261-264, 278-279, 296-297, 313-314, 335-336, 362-370, 384-385, 389-390, 394-395, 399-400, 404-405, 418-419, 434-435, 450-451, 464-465 /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/profiler.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/profiler_client.py 15 3 80% 89, 120, 125 /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/profiler_v2.py 51 28 45% 71-86, 102-113, 124-125, 140, 158, 161, 164 /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/tfprof_logger.py 115 93 19% 41-65, 70-74, 91-142, 160-189, 214-219 /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/traceme.py 27 16 41% 35-41, 44-45, 48-49, 53-60 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_mlir.py 14 4 71% 27, 34, 41, 47 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow.py 30 6 80% 38, 51, 61, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py 65 39 40% 19-21, 31, 35-36, 40-55, 59, 63-71, 74, 78-82, 87-90 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tfe.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder_impl.py 231 176 24% 93-112, 122-131, 145-153, 168-182, 201-214, 220-228, 267-300, 345-393, 412-428, 437, 442-446, 456-465, 478-492, 507-511, 525-555, 570-615, 638-667, 688-706, 710-716, 731-740, 752-754, 759-774, 786-792, 796-797 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/constants.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_deserialization.py 202 39 81% 74, 80-86, 94-95, 104-105, 110, 112, 114, 116, 118, 121, 132, 190, 243-255, 364-365, 376, 380, 387-392, 398-403, 441, 461 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_serialization.py 71 56 21% 31-49, 54-75, 81-83, 92-103, 122-161 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py 270 68 75% 55-64, 93, 98, 128-130, 205, 212-219, 231, 234-241, 256, 263, 273-279, 308, 326-333, 342-346, 369, 391-394, 409, 435-442, 445, 453-454, 457, 460, 464, 468-470, 476-482, 586, 595, 612-614 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load_v1_in_v2.py 137 105 23% 55-57, 60, 64, 72-83, 89-92, 95-102, 109-126, 133-184, 188-257, 262-263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader_impl.py 157 93 41% 63-67, 100-110, 135-161, 182-190, 194, 199-204, 208, 215-219, 244-246, 265, 298-299, 312-314, 319, 324, 329, 343-358, 379-381, 401-409, 425-433, 450-455 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/main_op.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/main_op_impl.py 23 7 70% 43-46, 70-72 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/method_name_updater.py 42 26 38% 71-72, 92-113, 130-148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/export_output.py 155 93 40% 53, 57-63, 86-99, 134-148, 152, 156, 159-166, 182-185, 189, 192-199, 223, 228, 231, 269-278, 294-298, 301-303, 325-361, 365, 369, 373, 378, 381-382, 395, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/export_utils.py 115 84 27% 93-140, 154-182, 211-226, 243-247, 277-290, 309-325, 342-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/mode_keys.py 43 5 88% 87, 100, 103, 106, 109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/nested_structure_coder.py 250 95 62% 70, 81, 96, 107-111, 132, 135-139, 152, 164-166, 175, 178-182, 198, 201-205, 225, 228-236, 256, 259-262, 268-269, 279, 282-285, 291-292, 306, 309-312, 329, 332-335, 352, 355-358, 375, 378-382, 399, 402-405, 422, 425-431, 482, 486-494, 497, 501-511 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/revived_types.py 70 21 70% 78, 92, 94, 96, 98-99, 122, 128, 133, 142-147, 171, 175-181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/save.py 392 316 19% 88-99, 103, 108-133, 136, 140-145, 171-210, 214, 218-234, 254-335, 339, 363-374, 412-443, 448-456, 487-502, 507-523, 542-569, 589-650, 655-668, 683-698, 703-739, 752-761, 947-972, 977-987, 993-1049 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/save_options.py 25 13 48% 81-84, 89-100 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/saved_model.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/signature_constants.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/signature_def_utils.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py 147 112 24% 52-61, 88-109, 138-168, 195-209, 214, 221, 249-265, 277-279, 286-292, 297-314, 319-347, 365, 383-390 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/signature_serialization.py 132 94 29% 40-45, 50-60, 64-67, 75-93, 98-150, 154-161, 166-188, 213, 216, 219, 222, 225, 247-262, 267-280 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/simple_save.py 18 4 78% 79-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/tag_constants.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/utils.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/utils_impl.py 92 51 45% 65-67, 72-85, 90-98, 130, 162-183, 202-203, 212-215, 234-239, 244, 251-256 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/plugin_asset.py 41 26 37% 62-81, 99-109, 143 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/summary.py 105 64 39% 78-84, 133-140, 173-181, 220-230, 260-271, 306-327, 363-373, 398-406, 431-436 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/summary_iterator.py 11 2 82% 68-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer.py 128 95 26% 69-82, 90-96, 100, 110-112, 120-121, 132-137, 145-153, 160-163, 166-168, 193-204, 207-232, 244-254, 264-269, 285-294, 298-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer_v2.py 44 27 39% 74-96, 100, 110-112, 120-122, 131, 138-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer.py 133 90 32% 79-99, 119-142, 155-156, 159-161, 179-214, 217-227, 244-249, 263-273, 276-279, 360-372, 376, 380, 384, 387-388, 393-394, 402-403, 413-414, 421-422, 432-433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer_cache.py 24 8 67% 43-48, 60-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tf2.py 14 1 93% 40 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/module_util.py 31 12 61% 24, 47, 50-56, 60-61, 63 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/api.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/bfloat16.py 26 13 50% 49-68, 78-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/client.py 162 118 27% 34-35, 46, 50-54, 59-68, 75-81, 85-87, 106-141, 157-176, 181, 186-191, 196-200, 203, 206, 213-216, 220, 224, 228, 232, 236, 240, 244, 248-258, 270-281, 286-315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/device_assignment.py 180 148 18% 36-56, 81-102, 108, 113, 118, 129, 133, 148-151, 157-158, 162-163, 167-168, 175, 198-213, 257-413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/feature_column.py 220 158 28% 106-155, 219-285, 295-304, 310, 314, 318, 322, 330, 334, 338, 341, 344, 347, 351, 373, 397-402, 405, 409, 413-415, 419-421, 425, 428, 432-438, 441-459, 463-484, 505, 532-537, 540, 544, 548-550, 554-556, 560, 563, 567-573, 576-595, 599-618, 626-651, 657, 669, 693-704 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/feature_column_v2.py 322 248 23% 142-189, 305-401, 415, 428, 432, 443-448, 451, 455, 459-461, 465-467, 471, 474, 478-484, 487-505, 508-515, 519-534, 538-559, 563-584, 601, 610, 616, 629-635, 638, 642, 647-649, 653-655, 659, 662, 666-672, 676-697, 701-722, 747-759, 785-806, 831-845, 853-859, 863-869, 872-885, 892-910, 918-925, 929-935, 939-967 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/functional.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/ops/tpu_ops.py 80 44 45% 35-42, 67-69, 83, 107-110, 136, 144-145, 152, 168-185, 211-217, 238-243, 274-276, 306-308, 360-362, 431-433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/preempted_hook.py 46 27 41% 40, 43-45, 48, 61-72, 75-77, 80-93 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/session_support.py 205 148 28% 45, 50, 71-74, 79-91, 95, 104-106, 111-124, 128-140, 144, 150-160, 165-175, 211-222, 226-247, 253-259, 262-265, 268, 271, 277-284, 293-298, 304-306, 325-333, 338-360, 364-381, 384-406, 417, 420-424, 435, 438, 449, 452 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer.py 795 657 17% 113-144, 158-179, 197-200, 217-231, 237-249, 274, 280-281, 293-295, 300, 314, 333, 340-346, 350-354, 361-373, 378-380, 386, 393-399, 402, 418-443, 448-455, 460-462, 479-509, 514-520, 524-531, 534, 538-552, 556, 571-585, 596-599, 612-745, 762-857, 871-909, 926-983, 989-1022, 1043-1055, 1060-1080, 1093-1122, 1125, 1140-1194, 1209-1213, 1219-1233, 1237-1252, 1256-1266, 1279-1283, 1294-1399, 1402, 1406, 1435-1600, 1630-1659, 1680-1701 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_flags.py 250 163 35% 103-152, 155, 160-170, 174-183, 186-194, 199-211, 214, 219-229, 245-259, 263-290, 299-315, 319, 335-340, 353-363, 376-385, 401-418, 423-431, 436-444, 449-454, 468 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_pb2.py 28 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_report.py 242 183 24% 68-119, 126-130, 137-139, 143-160, 166-183, 193-199, 202, 205-206, 213-214, 217, 220, 223, 243-277, 281-284, 289-296, 300-304, 310-314, 319-335, 340-343, 348-361, 366-382, 387-396, 402-408 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/topology.py 90 60 33% 29-32, 37-40, 75-94, 98-129, 133-140, 145, 150, 164, 169, 182, 195, 199, 204, 211, 216, 220-228 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu.py 696 574 18% 90-93, 116-119, 145-147, 160-162, 175, 180-190, 196-198, 202-207, 216-221, 232-233, 235, 271-286, 303-350, 353-359, 363-399, 402-414, 420-463, 466-471, 474-481, 484, 488-508, 512-587, 591-604, 607-609, 617, 622-624, 627, 638-639, 642-645, 650-653, 658, 726-780, 886, 898-903, 926-1028, 1093-1354, 1373-1415, 1430-1461, 1532-1611, 1683, 1743, 1797, 1819-1831, 1843-1845, 1848, 1852-1858, 1861-1864, 1867, 1871, 1895-1896, 1936-1963, 1984-2001 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_embedding.py 574 454 21% 106-126, 158-162, 197, 202, 223-229, 263-268, 316-322, 381-399, 458-482, 522, 691-781, 790, 801, 810, 822, 833, 837, 841, 845, 849, 853-908, 945-1012, 1033-1034, 1045-1112, 1116-1118, 1135-1163, 1175-1197, 1215-1237, 1246-1248, 1255-1267, 1273-1274, 1280-1281, 1291, 1294, 1297, 1301, 1308-1309, 1312, 1315, 1319-1377, 1384-1386, 1389-1397, 1401, 1406-1477, 1484-1486, 1489-1497, 1503, 1508-1580, 1587, 1591, 1595-1643, 1648-1657, 1662, 1667-1671, 1676-1697, 1703-1721, 1732-1758 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_embedding_gradient.py 53 37 30% 45-50, 71-97, 118-126, 142-179 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_feed.py 285 232 19% 52-83, 98-104, 120-121, 165-198, 206-209, 214, 219, 237-251, 258, 276-295, 300, 312, 331-337, 347, 360-362, 378-382, 404-432, 445-458, 482-496, 529-546, 596-604, 618, 621, 679-722, 757-765, 782-794, 834-882, 897-920, 934-935 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_function.py 28 11 61% 35, 38, 48-54, 58, 66-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_optimizer.py 64 43 33% 56-70, 86-107, 144-161, 184-192, 206, 220, 224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_sharding.py 91 68 25% 36-38, 41-44, 48-51, 60-62, 67, 82-91, 98, 114-120, 132-135, 161-188, 204-216, 239-252 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_strategy_util.py 95 73 23% 53-127, 146-206 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_system_metadata.py 101 75 26% 56-149, 154-166, 174-176, 196-212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/training_loop.py 82 70 15% 56-177, 201-222 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adadelta.py 46 28 39% 59-67, 70-72, 75-81, 84-86, 97-99, 110-112, 124-126 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad.py 48 26 46% 63-70, 73-80, 84-90, 93-94, 98-99, 107-108, 116-117, 126-127 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad_da.py 59 39 34% 75-88, 91-100, 104-109, 112-116, 128-132, 144-148, 161-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adam.py 93 67 28% 101-111, 114-119, 127-136, 139-147, 150-153, 167-170, 184-207, 210, 221-223, 226, 231-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_loops.py 21 13 38% 51-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_session_run_hooks.py 491 361 26% 61, 65, 69, 83, 87, 99-108, 111-112, 125-139, 142-152, 155, 162-163, 166-167, 170, 212-231, 234-237, 243-247, 250-263, 266-270, 273-275, 306-315, 352-360, 363-366, 369-371, 374-377, 382-386, 412-417, 420-422, 425-427, 430, 433-442, 500, 503, 506, 509, 546-557, 560, 563-569, 572-588, 591, 594-602, 605-609, 613-634, 637-656, 669-678, 681, 684-690, 693, 696-702, 705-736, 743, 760-761, 764, 767-775, 809-817, 822-827, 831-839, 842-861, 864-865, 873-884, 903, 906-909, 913-933, 949-951, 955, 958-977, 991, 994, 1033-1037, 1041-1044, 1047-1055, 1058-1072, 1075-1078, 1085-1104 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_management.py 266 201 24% 45-47, 61-63, 96-128, 167, 213-247, 270-305, 322-324, 351-364, 381-388, 410, 438-456, 476-484, 489-490, 506-508, 614-664, 668, 672, 686, 698, 702-717, 721-722, 743, 748, 771-824, 844-852 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_ops.py 76 60 21% 123-203, 332-416, 467-473 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_state_pb2.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_utils.py 161 125 22% 63-67, 82-85, 98-104, 125-135, 181-201, 286-291, 297-378, 384-386, 406-427, 447-459, 463, 469-476 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/coordinator.py 141 104 26% 142-159, 178-185, 201-244, 251-255, 263, 296-299, 311, 319-320, 353-395, 401, 405-407, 444-457, 478-481, 484-496, 500, 504, 508-509 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/device_setter.py 64 45 30% 53-54, 66-68, 93-98, 111-133, 202-231 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/evaluation.py 106 74 30% 47-61, 74-78, 92-94, 97, 100, 105-109, 112, 116-130, 145-149, 153, 156, 160-169, 225-277 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scale.py 158 90 43% 83, 88, 125, 141-162, 167-176, 180-188, 193, 198, 202, 228-239, 242, 245-246, 249, 252, 257-260, 275-277, 282, 324-335, 340, 344, 348, 351, 355-400, 403-409, 414, 424, 426, 428, 432 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scale_optimizer.py 79 52 34% 61-74, 78, 114-126, 129-136, 139-141, 147-150, 174-184, 211-221, 226-229, 233, 237, 241, 245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scaling_gradient_tape.py 73 53 27% 43, 115-125, 163-181, 190, 200, 238-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/mixed_precision.py 51 30 41% 33-74, 219, 332, 339-362, 382-386, 413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/mixed_precision_global_state.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/ftrl.py 72 53 26% 95-130, 134-138, 141-150, 154-170, 186-202, 218-235, 252-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/gen_training_ops.py 1619 1436 11% 57-77, 83, 115-135, 141, 167-191, 197, 226-248, 254, 282-306, 312, 353-378, 384, 414-434, 440, 490-510, 516, 552-572, 578, 617-637, 643, 662-681, 687, 720-744, 750, 780-800, 806, 835-854, 860, 886-906, 912, 952-972, 978, 1011-1038, 1043-1056, 1086-1113, 1118-1131, 1156-1185, 1190-1206, 1236-1266, 1271-1286, 1313-1343, 1348-1365, 1404-1437, 1442-1459, 1498-1527, 1532-1547, 1575-1601, 1606-1618, 1665-1692, 1697-1712, 1747-1773, 1778-1791, 1829-1856, 1861-1874, 1893-1916, 1921-1933, 1965-1995, 2000-2017, 2049-2078, 2083-2099, 2127-2153, 2158-2171, 2199-2225, 2230-2243, 2269-2296, 2301-2313, 2351-2377, 2382-2395, 2423-2451, 2456-2472, 2500-2530, 2535-2553, 2585-2616, 2621-2638, 2668-2699, 2704-2722, 2769-2800, 2805-2822, 2860-2887, 2892-2908, 2949-2977, 2982-2998, 3034-3067, 3072-3090, 3126-3156, 3161-3179, 3211-3239, 3244-3259, 3288-3316, 3321-3335, 3375-3402, 3407-3423, 3452-3474, 3480, 3509-3534, 3540, 3571-3595, 3601, 3632-3658, 3664, 3714-3737, 3743, 3782-3804, 3810, 3852-3874, 3880, 3917-3943, 3949, 3982-4003, 4009, 4038-4061, 4067, 4109-4131, 4137 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/gradient_descent.py 28 10 64% 51-53, 56, 63, 69, 73-77, 80-81 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/input.py 387 284 27% 74-75, 103-114, 174-202, 256-267, 315-317, 363-376, 383, 399-401, 404-415, 418, 421, 425-430, 434, 438, 442, 446-449, 453-463, 467-475, 509-577, 582-597, 602-627, 631-634, 638-642, 647-656, 660-667, 671-678, 698-709, 714-718, 723-735, 740-752, 756, 765-791, 804-832, 840-876, 885-919, 1009, 1066, 1177, 1234, 1335, 1399, 1498, 1562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/learning_rate_decay.py 87 58 33% 97-103, 150-179, 268-280, 356-368, 444-451, 507-514, 579-591, 664-676, 757-771 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/momentum.py 40 22 45% 80-83, 86-87, 90-98, 101-102, 111-112, 121-122, 131-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/monitored_session.py 481 348 28% 153-188, 192-247, 251, 255, 259, 263, 267, 271, 275, 279, 283, 288-300, 315, 337-427, 512-601, 614, 639-644, 648-657, 660-661, 690-694, 698-707, 710-711, 734-751, 756-758, 774, 819-834, 852-853, 857, 861, 871, 874, 877, 880, 883-887, 893-897, 902-910, 915-929, 939, 950, 1034, 1118-1124, 1132, 1154-1155, 1159, 1163, 1173-1177, 1185, 1188-1197, 1200, 1206-1207, 1230-1231, 1235-1238, 1249-1272, 1276-1295, 1299-1316, 1342-1344, 1349-1351, 1354-1365, 1368-1384, 1412-1414, 1418, 1422-1453, 1458-1481, 1484-1486, 1501-1514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/moving_averages.py 131 103 21% 87-114, 152-178, 220-266, 378-382, 387, 421-473, 485, 509-511, 545-561 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/optimizer.py 403 311 23% 58-61, 76-80, 85-87, 97, 102, 109, 112, 115, 118-132, 139, 142, 146-151, 158, 161, 165-176, 188, 191, 194, 199-213, 327-343, 353, 399-412, 458-519, 523-529, 561-640, 667-735, 755-773, 783, 794-811, 816-843, 848-857, 862-866, 869-875, 883, 894-898, 914, 924, 932, 944, 957, 980-982, 1002, 1032-1038, 1057, 1074, 1090-1094, 1109-1116, 1134-1142, 1156-1163, 1171-1179, 1202-1239, 1245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_adagrad.py 44 25 43% 63-74, 77-82, 85-90, 95-96, 103-104, 111-112, 120-121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_gradient_descent.py 30 13 57% 57-62, 65, 74, 83, 93, 103-107 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/py_checkpoint_reader.py 40 17 58% 31-48, 61, 73-74, 98-99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/quantize_training.py 15 4 73% 45-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner_impl.py 176 128 27% 97-118, 138-165, 175-192, 196, 200, 204, 208, 212, 231, 236, 248-283, 293-298, 327-356, 368-385, 390, 411, 451-480 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/rmsprop.py 70 50 29% 107-118, 121-131, 134-142, 145-161, 173-189, 201-218, 231-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saver.py 480 383 20% 84, 104-124, 144-154, 173-179, 194, 206-207, 254-285, 298-311, 335-361, 379-390, 409-417, 462, 484-557, 574-583, 598-610, 800-843, 846-848, 851, 856-902, 906-914, 926-927, 931-942, 957-973, 981, 992-1008, 1021, 1032, 1045-1049, 1061-1062, 1075-1080, 1139-1217, 1253, 1283-1334, 1346, 1460, 1471-1490, 1496-1514, 1580-1599, 1603-1607, 1626-1634, 1679-1728 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/functional_saver.py 119 48 60% 51, 64-73, 118, 148, 153-154, 165-169, 179-182, 188-191, 202-263, 284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_hook.py 16 4 75% 44, 51, 55, 59 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object.py 34 8 76% 41, 47-51, 55, 78, 101 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object_util.py 161 98 39% 54-56, 63-64, 67-70, 84-85, 90-95, 101, 111, 120, 139, 143, 145-169, 175-183, 190, 196-209, 230-303, 319, 342 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/server_lib.py 200 151 24% 57-95, 146-150, 153-164, 173, 184, 194, 213, 235, 285-313, 318, 324, 327, 330-334, 348-361, 365, 374, 388-392, 407-411, 427-434, 457-464, 473-491, 527-530, 534-538, 542-546, 555-573 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_manager.py 149 120 19% 42-47, 144-155, 189-227, 288-319, 353-383, 411-442, 453-459, 473, 487, 502-512, 529-551, 557-558, 561-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_run_hook.py 41 13 68% 110, 127, 150, 169, 186, 211, 226-228, 240, 245, 255, 263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/slot_creator.py 62 48 23% 55-101, 124-135, 161-173, 190-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/summary_io.py 12 1 92% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/supervisor.py 339 239 29% 308-357, 360-369, 380-390, 405-416, 427-433, 444-455, 464-469, 478-484, 493-500, 509, 518, 530, 539, 548, 557, 561, 570, 579, 588, 597, 606, 615, 624, 628-636, 661-688, 720-745, 772-780, 801-808, 831-847, 859, 869, 879, 883, 898-902, 910-918, 928-931, 999-1023, 1038-1040, 1043-1051, 1066-1073, 1076-1077, 1081-1098, 1112-1114, 1117-1122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/sync_replicas_optimizer.py 150 114 24% 181-205, 223, 248-358, 375-378, 392, 403, 417, 439-458, 462, 476-478, 481-497, 501-513 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/base.py 342 122 64% 71-76, 79-82, 86, 93-95, 98, 112, 126, 143-155, 161, 165, 170-173, 181-183, 187, 211, 233, 241-253, 266, 277-278, 291-307, 320, 323-324, 338-348, 352-354, 364-367, 369, 373, 376, 411, 414, 489-494, 521-526, 544-546, 550, 558, 562, 566, 570, 601, 624, 629, 633, 637-640, 726, 736-737, 754, 780-790, 822, 825, 829-839, 876, 883, 923-925, 933, 1004-1005, 1022-1023 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/data_structures.py 536 236 56% 28-30, 70, 72, 74, 92, 94, 97, 144, 167, 171, 184, 187, 193, 205, 214, 221, 228, 232, 236, 240, 248-252, 257-261, 266, 271, 314, 318, 321, 324, 328, 336, 349-350, 353, 356-366, 369, 372, 375, 381, 387, 390, 444-445, 454-455, 459-462, 465-468, 472, 482, 484-485, 494, 501, 510, 522-523, 532-545, 550, 568-574, 577, 580, 583, 586, 589, 592, 597, 600-601, 604-605, 608, 611-612, 626, 629, 646-648, 654, 657, 660, 666-669, 672-676, 679-683, 689-690, 693, 696, 699, 702, 717, 720, 732, 747, 751-754, 757-760, 770, 777, 785, 806, 808-809, 815, 823-824, 828-832, 845-859, 862-864, 867, 870, 875, 878-879, 882, 892-938, 943, 947-954, 957, 962, 967, 970, 973, 976, 983, 987, 991-998, 1001-1009, 1013, 1048-1055 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/graph_view.py 201 126 37% 78-86, 97-135, 177-179, 192, 211-312, 319-330, 335-356, 379-380, 385-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/layer_utils.py 110 13 88% 33-34, 190, 231, 242, 268, 293-296, 301-304 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/python_state.py 17 1 94% 87 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/tracking.py 131 52 60% 82, 92-94, 98, 102-113, 119-125, 140, 177-178, 185-186, 190-191, 221, 226, 231-234, 237-252, 274-276, 323-324, 330 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py 602 378 37% 69-72, 90-115, 129, 133, 136, 143-145, 147-149, 151, 229, 244, 248-249, 252-254, 272-277, 285, 291-294, 302-308, 324-348, 355-379, 392-418, 433, 465-476, 493, 511-512, 537-607, 616, 621, 626, 631, 636, 640, 651-664, 710-740, 763, 780, 788-806, 810-814, 831-845, 849-850, 864-867, 871, 876, 881, 893, 911-921, 941-945, 959, 963-982, 990, 998, 1002-1014, 1018-1024, 1029, 1036-1037, 1040-1045, 1094-1107, 1125-1140, 1164-1198, 1259, 1263, 1268-1281, 1286-1290, 1333-1335, 1343, 1468-1481, 1485-1490, 1520-1529, 1540-1541, 1568-1601, 1705-1712, 1808-1821, 1825-1830, 1857-1866, 1877-1878, 1902-1933, 2009-2016 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training.py 105 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_ops.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_util.py 95 65 32% 66-68, 88-103, 120-137, 158-162, 172-184, 201-209, 222-239, 243-253 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/warm_starting_util.py 150 123 18% 122-126, 152-156, 173-189, 240-311, 340-372, 396-411, 465-549 /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/user_ops.py 10 1 90% 32 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/all_util.py 36 6 83% 78-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat.py 51 9 82% 60-61, 80, 86, 111, 116, 178-180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat_internal.py 9 3 67% 34-36 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/decorator_utils.py 52 11 79% 26-32, 51, 71, 100, 108, 119, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py 208 81 61% 95, 97, 102-110, 129-131, 188-239, 264, 314-317, 379, 381-382, 390, 439-442, 463-471, 485, 487, 489, 495, 497-500, 553, 565-568, 596-601, 635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py 58 21 64% 69, 81, 100-104, 120-125, 128-131, 156, 170, 181-188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/function_utils.py 59 38 36% 31-32, 36, 51-63, 78-86, 95-101, 106-119, 127-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/is_in_graph_mode.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lazy_loader.py 27 4 85% 50-52, 66-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lock_util.py 43 3 93% 68, 92, 112 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/memory.py 11 4 64% 40-45 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/module_wrapper.py 132 60 55% 37, 44-48, 52, 68-78, 104-105, 108, 133-140, 144-152, 161-162, 170-174, 193-206, 219-227, 230-233, 236, 239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py 303 121 60% 91-93, 100-101, 150, 157-162, 165, 167-171, 178-179, 181, 185, 220-221, 223-224, 231, 256, 332, 335, 379-382, 418-441, 485-486, 489, 496, 508-512, 596, 599, 605, 610, 653-657, 696, 777, 782, 789-804, 809-822, 827-828, 833, 837, 844, 1032-1037, 1189, 1249-1285, 1347-1351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/object_identity.py 114 27 76% 44, 47-48, 51-52, 56, 61, 70, 114, 141, 148, 155, 159, 162-168, 179-181, 190, 199, 202, 205, 232 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/compare.py 87 69 21% 94-118, 139-186, 190, 194-200, 216-255, 274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/serialization.py 29 19 34% 43-76 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_contextlib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_decorator.py 98 16 84% 172-173, 180-181, 188-193, 218, 222, 224, 257, 260, 272, 276 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_export.py 145 47 68% 114, 118, 132, 154, 174, 178, 180, 194-207, 220-228, 241-249, 274, 302, 307, 329-331, 343-344, 388-393 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_inspect.py 138 55 60% 34, 43, 79-90, 95, 118, 126, 131-147, 190-235, 282, 291-293, 298, 327, 342, 347, 362, 377, 382, 397, 402, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_should_use.py 98 67 32% 45-61, 64-68, 72-86, 95, 100-101, 105-107, 113-117, 122-129, 148-161, 179-202, 235 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_stack.py 70 6 91% 37-38, 61, 76, 88, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/all_renames_v2.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/renames_v2.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/doc_controls.py 49 30 39% 258-261, 276-322 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/baseline.py 124 78 37% 72-79, 83-90, 95-112, 128-154, 187-197, 220-227, 264-283, 381-398, 414-427, 497-506, 516-525, 607-621, 636-650 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees.py 715 607 15% 61-81, 95-99, 111-115, 130-147, 168, 201-275, 280-287, 317-388, 393-414, 443-471, 476-489, 497, 505-570, 597-621, 625-639, 643-654, 674-680, 686, 690, 703-706, 709, 717-724, 730-731, 761-776, 824, 830-836, 841-889, 898, 906-911, 916-920, 926, 938-982, 986-1010, 1017-1052, 1056-1089, 1093, 1147-1407, 1414, 1417, 1440-1479, 1489-1506, 1513-1521, 1531-1551, 1556, 1565-1578, 1601-1615, 1641-1686, 1691-1700, 1739-1751, 1770-1781, 1840-1889, 1894-1903, 2038-2063, 2190-2214, 2315-2343, 2357-2360 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees_utils.py 42 27 36% 33-37, 45-57, 61, 66, 73-82, 87-94 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn.py 224 162 28% 46-48, 73-102, 125-153, 157-158, 174-230, 233-254, 260, 272, 287-344, 347-359, 363-364, 371-379, 431-456, 489-504, 552-579, 737-759, 787-807, 946-962, 986-1003, 1151-1172, 1199-1221 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn_linear_combined.py 187 140 25% 43-44, 64-65, 69-71, 76-82, 141-226, 288-383, 554-585, 615-643, 657-682, 820-843, 864-888, 1048-1077, 1106-1136 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/head.py 497 410 18% 58, 78-89, 155, 166, 193, 230-240, 274, 299-354, 382-443, 448-472, 487-494, 510-520, 536-559, 563-567, 571-573, 579-584, 590-598, 615-617, 622-628, 632-637, 646-650, 658-666, 671-679, 738-748, 767-774, 778, 782, 787-808, 812-825, 829-852, 893-994, 1062-1077, 1096-1101, 1105, 1109, 1113-1195, 1199-1223, 1265-1373, 1435-1440, 1460-1467, 1471, 1475, 1479-1505, 1514-1532, 1571-1652, 1661-1664, 1668-1676, 1704-1715 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/kmeans.py 110 69 37% 46-48, 51-52, 55-62, 82-84, 87-99, 119-127, 137-145, 173-224, 404-415, 424-425, 436-438, 454, 470-475, 479 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py 283 217 23% 128-133, 138-174, 181-239, 243-244, 248-249, 262-270, 283-308, 326-375, 394-448, 470-539, 547-548, 555, 559, 583-619, 652-678, 719-748, 759-767, 920-945, 968-991, 1107-1119, 1158-1171, 1178-1186, 1325-1349, 1371-1396 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sdca_ops.py 291 245 16% 95-104, 114, 123, 132, 200-251, 254, 257, 261, 265, 270-271, 280-298, 305-307, 310-312, 316-318, 322-333, 337-348, 356-364, 368-371, 379-402, 420-433, 440-449, 464-629, 644-670, 679-699, 715-760, 775-781 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sharded_mutable_dense_hashtable.py 139 101 27% 73-99, 106-120, 124, 135-138, 158-165, 184-192, 204-210, 214, 223-229, 232-235, 262-280, 285, 289, 293, 296-298, 301-307, 310-311, 316-335, 339-352, 364-370 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/metric_keys.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/optimizers.py 46 27 41% 78-91, 95-97, 129-146 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/parsing_utils.py 44 25 43% 138-140, 254-257, 288-312, 321-324, 342-346 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/prediction_keys.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/early_stopping.py 179 131 27% 84-104, 153, 210, 268, 326, 346-356, 365-388, 401-430, 446-450, 454-458, 471-479, 482-484, 487-488, 491-498, 505, 508, 511-512, 515-518, 525-529, 540-549, 552-566, 570-571, 577-592 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/estimator.py 729 611 16% 176-203, 209, 213, 217, 228-231, 246-248, 259-261, 270-271, 321-351, 365-376, 390, 445-462, 478-511, 525-543, 596-639, 646, 717-722, 800, 817-889, 926-1010, 1014-1017, 1020-1021, 1027-1039, 1043, 1048-1054, 1058-1072, 1086, 1097-1100, 1123-1137, 1154-1176, 1179-1182, 1197-1213, 1231-1241, 1250-1354, 1361-1385, 1390-1523, 1527-1563, 1567-1575, 1581-1631, 1636-1658, 1661-1664, 1735-1738, 1756, 1760-1763, 1769-1786, 1793-1805, 1822-1849, 1854-1861, 1871-1941, 1945-1947, 1953-1957, 1962-1968, 1985-1998, 2003-2016, 2021-2026, 2031-2040, 2052, 2065-2110, 2123-2134, 2141-2148, 2338-2341, 2366-2385 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export.py 128 88 31% 65-72, 77-95, 99-103, 139-157, 206-215, 238, 271-277, 302-313, 327-343, 348, 370-376, 404-440, 470-474 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export_lib.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export_output.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/exporter.py 151 106 30% 41, 62, 97-100, 104, 108-117, 136-145, 150-160, 255-271, 277, 281-307, 319-337, 350-365, 400, 406, 410-416, 457-462, 467, 471-477, 489-507 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/extenders.py 38 25 34% 82-94, 104-107, 113-123 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/gc.py 63 46 27% 87-95, 110-127, 140-147, 161-166, 179-184, 207-217 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/base_head.py 290 222 23% 110, 122, 133, 163, 178, 192, 224, 227, 281-295, 347, 406-469, 498-582, 587-624, 639-648, 652-654, 660-661, 666-667, 673-677, 695-705, 726-759, 763-765, 771-773, 779-784, 790, 798-817, 821-824, 844-855, 896-918, 925-934 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py 205 164 20% 151-193, 198, 203, 208, 229-232, 245-250, 254-259, 263-275, 284-300, 318-362, 366-392, 421-427, 430-433, 443-487, 540-597 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/head_utils.py 30 17 43% 55-66, 70-77, 92-94, 101-102 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_class_head.py 151 113 25% 143-168, 173, 178, 183, 204-207, 220-225, 229-242, 246-262, 271-287, 305-345, 349-359, 368-385, 436-492 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_head.py 182 149 18% 34-35, 40-45, 178-200, 205, 210, 215-220, 250-282, 286-306, 315-342, 346-353, 357-367, 376-395, 454-512, 528-548 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_label_head.py 217 181 17% 161-243, 248, 253, 258, 277-280, 284-315, 322-339, 348-364, 381-399, 403-427, 436-480, 532-587 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/regression_head.py 124 85 31% 143-159, 164, 169, 174, 177-182, 186-203, 212-228, 242-254, 258-269, 279-297, 351-402, 483-484, 493, 568-570, 577 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/basic_session_run_hooks.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/hooks.py 92 67 27% 103-120, 124-141, 148-176, 179-201, 205-207, 211, 237-244, 247-249, 253, 256-266, 275-277 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/session_run_hook.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/numpy_io.py 70 56 20% 50-53, 70-86, 141-224 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/pandas_io.py 61 46 25% 32-36, 50-52, 91-158 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py 208 177 15% 34-38, 51-60, 78-96, 126-145, 158-169, 172-181, 197-212, 215-230, 243-255, 258-271, 285-297, 300-330, 376-504 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py 68 56 18% 57-73, 87-122, 150-174, 177, 182 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/mode_keys.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/model_fn.py 176 139 21% 152-164, 180-184, 213-221, 237-242, 283-295, 316-331, 353-377, 405-408, 424-430, 459-507, 512-516, 534-538, 542-543, 548-550, 577-598, 607-620 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/run_config.py 315 208 34% 95-113, 118-121, 126-130, 135-139, 145-149, 154-199, 211-226, 236-248, 254-328, 337-341, 536-586, 596-597, 609-627, 635-689, 697-724, 728, 738, 742, 746, 750, 754, 758, 762, 802, 806, 810, 814, 818, 822, 826, 830, 834, 838, 842, 846, 850, 855, 860, 865, 870, 907, 933-946, 957-975 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tools/analytics.py 8 2 75% 28, 37 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/_tpu_estimator_embedding.py 227 180 21% 58, 62, 66-68, 73-94, 103-122, 137-174, 270-315, 337-358, 362-363, 368, 372-412, 415-418, 424-429, 434-490, 495-505, 510-519, 524-541, 553-557 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/error_handling.py 65 44 32% 56-58, 73-105, 115-117, 122-125, 136-154 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/iteration_count_estimator.py 64 46 28% 68, 72-81, 84, 87, 90, 107-111, 122-123, 132-150, 169-201 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_config.py 99 60 39% 141-189, 227-265, 272, 276, 280, 284, 288, 291-297, 304-309 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_context.py 367 273 26% 55-58, 90-108, 121, 126, 131, 136-139, 144-147, 163, 173-177, 211-234, 237-240, 248-250, 254, 257-262, 266-284, 288-313, 318-337, 341, 345, 349, 354-355, 359-360, 365-369, 374-395, 399-400, 404, 408-409, 415, 420, 426-427, 434-436, 459-462, 466-478, 482-490, 495-505, 510-517, 531-552, 558-579, 584-596, 601-620, 624-718, 731-747, 756, 762-776, 793-799, 804-811, 814-816, 825-836 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py 1782 1486 17% 139-143, 149-153, 158-163, 204-215, 240-247, 260-262, 269-271, 277-278, 281-283, 346-363, 379-394, 411-416, 419, 422, 426-433, 436-438, 448-449, 452-454, 477-505, 508-522, 525-542, 545-557, 560, 563-570, 573-604, 608-616, 619-628, 641, 652, 694-710, 726-735, 745-749, 762-767, 771-772, 786-799, 812, 815, 818, 825, 828, 834, 837, 840-859, 866-903, 910-992, 999-1114, 1120-1210, 1218-1219, 1224-1267, 1270-1276, 1279, 1319-1338, 1342-1343, 1346, 1351-1372, 1377-1396, 1401-1410, 1428-1432, 1450-1457, 1464-1479, 1483-1593, 1608-1618, 1641-1680, 1694-1697, 1700, 1704-1714, 1739-1820, 1849-1881, 1894-1929, 1936-1944, 1965-1983, 1987-2048, 2052-2065, 2072-2080, 2086-2101, 2110-2126, 2131-2148, 2156-2172, 2194-2290, 2297, 2304-2309, 2312, 2315, 2318, 2330-2331, 2338-2351, 2362, 2365, 2368, 2407, 2735-2846, 2857-2889, 2900-2914, 2919-2922, 2942, 2945-2962, 2967-2976, 2998-3051, 3062, 3070-3083, 3091-3104, 3112-3129, 3134-3509, 3513, 3532-3539, 3559-3573, 3580-3612, 3618-3651, 3657-3694, 3701-3710, 3719-3732, 3747-3758, 3770-3771, 3774-3779, 3782-3786, 3791-3809, 3816-3817, 3822, 3826-3828, 3833-3835, 3839, 3849-3860, 3865-3870, 3874-3878, 3883, 3890-3892, 3896-3903, 3906, 3910, 3922-3962, 3965-3975, 3979-3985, 4005-4035, 4045-4047, 4051-4062, 4066, 4071-4079, 4088-4122, 4128-4164, 4168-4173, 4177-4183, 4187-4195, 4208-4216, 4244-4253, 4284-4315, 4321-4354, 4360-4412, 4449-4476 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/util.py 36 19 47% 37-41, 65-79, 89, 92-95 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/training.py 337 258 23% 49-50, 55-61, 66-105, 110-114, 155-165, 228-254, 449-472, 479-480, 483, 486-488, 496-499, 502-504, 507-526, 532-535, 538-544, 561-584, 589, 607-639, 644, 649, 653-676, 681, 685-687, 691-718, 722-762, 766-793, 802-827, 835-874, 880-888, 892, 904-943, 947-950, 954-969, 1017-1052, 1066, 1077-1078, 1083-1084 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/util.py 47 24 49% 57-62, 67-72, 79, 82, 85-86, 93, 96, 99-100, 107, 110-113 /usr/local/lib/python3.8/dist-packages/termcolor.py 59 46 22% 102-115, 124, 128-167 /usr/local/lib/python3.8/dist-packages/threadpoolctl.py 323 225 30% 57-58, 124, 168-171, 174, 177, 180-182, 189-218, 223-257, 265-281, 335-343, 347-353, 357, 360, 363, 366, 370-375, 387-409, 416-428, 437-487, 492-516, 523-526, 530-546, 551-558, 563-567, 573-580, 601-608, 611, 615, 643-649, 652-654, 657-659, 662, 666-671, 677-680, 683-688, 691-693, 696, 700-704, 710-717, 720-721, 724-726, 729, 736-740, 747, 750-751, 754-756, 759 /usr/local/lib/python3.8/dist-packages/tifffile/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/tifffile/tifffile.py 6306 5732 9% 558-559, 586-625, 663-688, 724-756, 773-782, 838-907, 1070-1943, 1947-2062, 2067-2095, 2099, 2103-2106, 2109, 2112, 2165-2237, 2241, 2245, 2250, 2255, 2260-2263, 2267-2269, 2317-2389, 2399-2428, 2436-2468, 2472-2488, 2492-2584, 2590-2674, 2681-2689, 2697-2715, 2722-2736, 2742-2957, 2961-2994, 2999-3017, 3025-3071, 3080-3100, 3104-3110, 3114, 3117, 3130-3182, 3187, 3197-3206, 3212-3227, 3233, 3244-3246, 3255-3258, 3263-3265, 3270-3303, 3308-3318, 3323-3338, 3343-3345, 3350-3359, 3364-3366, 3371-3383, 3388-3400, 3405, 3410, 3415-3417, 3422-3424, 3429-3431, 3436-3442, 3447-3462, 3467-3469, 3488-3532, 3537, 3542-3545, 3550, 3555, 3560, 3565-3589, 3594-3596, 3600-3607, 3611-3628, 3632-3669, 3673-3687, 3691-3764, 3773-3813, 3817-3845, 3849-3866, 3870-3878, 3882, 3886-3888, 3966-4276, 4309-4584, 4592-4639, 4686-4782, 4791-4837, 4841, 4846, 4851, 4856, 4861-4863, 4872, 4892-4897, 4902-4905, 4914-4944, 4953, 4963, 4973-5036, 5041, 5050, 5055, 5060-5071, 5078-5099, 5104-5114, 5119-5219, 5224, 5229, 5234, 5239, 5244, 5249-5252, 5261-5266, 5271-5278, 5283, 5288-5289, 5294, 5299, 5304, 5309, 5314, 5319, 5324-5327, 5332-5334, 5339-5342, 5347, 5352, 5357, 5362, 5370, 5375, 5380, 5385, 5390, 5401, 5406, 5411, 5416, 5444-5485, 5489-5532, 5536-5541, 5545-5547, 5551-5553, 5557-5559, 5564, 5569-5586, 5591-5595, 5600-5602, 5607-5609, 5613-5617, 5623-5635, 5666-5736, 5741, 5750-5757, 5761-5782, 5806-5807, 5811-5817, 5821, 5825-5826, 5830-5831, 5835-5852, 5856-5873, 5877-5884, 5888, 5892-5913, 5917-5924, 5928, 5932-5935, 5939-5955, 5979-6000, 6004-6009, 6014-6037, 6042, 6047, 6053, 6057-6063, 6067-6074, 6078-6084, 6088, 6092-6104, 6163-6243, 6247-6249, 6258, 6261, 6264, 6267-6269, 6297-6329, 6338, 6398-6408, 6412-6478, 6482-6484, 6488, 6492, 6496, 6500-6502, 6508-6537, 6576-6628, 6632-6642, 6646-6649, 6653-6657, 6661, 6665-6672, 6676-6678, 6681, 6684, 6688-6693, 6697, 6701, 6705, 6709, 6713, 6717, 6721, 6735, 6738, 6748-6751, 6755-6761, 6765-6779, 6783-6788, 6800-6803, 6807-6811, 6815-6819, 6823-6826, 6830-6839, 6842, 6845, 6856-6861, 6869-6883, 6886-6900, 6903-6917, 6920-6934, 6937-6951, 6955-7052, 7697, 7734, 7738, 7763, 7785-7792, 7795-7801, 7804-7860, 7863-7879, 7882-7887, 7890-7894, 7897-7907, 7910-7914, 7917-7924, 7927-7930, 7933-7941, 7944-7951, 7954-7962, 7965-7970, 7973-7981, 7984-8005, 8009, 8038, 8056, 8146-8166, 8171-8191, 8196-8236, 8241-8290, 8294, 8306-8315, 8323, 8331, 8338-8357, 8361-8377, 8381-8398, 8401, 8437, 8446, 8498-8505, 8508, 8575, 8604, 8620, 8632, 8641, 8649, 8661, 8674, 8874, 8925, 8939, 8949, 8965, 8981, 9027, 9087-9094, 9122, 9138-9139, 9212, 9256-9257, 9261-9262, 9272-9370, 9375-9390, 9395, 9400-9401, 9406-9413, 9418, 9423-9424, 9429-9431, 9436-9440, 9445-9453, 9458, 9467-9483, 9488-9491, 9502-9505, 9510-9519, 9529-9615, 9621-9630, 9635-9669, 9674-9675, 9680-9681, 9686-9691, 9696-9703, 9708-9730, 9735-9778, 9787-9827, 9832-9838, 9843-9866, 9871-9885, 9895-9931, 9936-9941, 9955-9969, 9980-10034, 10044, 10070-10124, 10141-10190, 10204-10225, 10245-10279, 10291-10303, 10315-10316, 10331-10338, 10352-10404, 10417-10448, 10460-10473, 10482-10508, 10513-10545, 10550, 10555-10558, 10570-10663, 10699-10722, 10726-10859, 10892-10896, 10907-10978, 10983-11014, 11029-11048, 11073-11081, 11102-11107, 11119-11125, 11137-11151, 11165-11199, 11208-11277, 11299-11316, 11340-11472, 11486-11487, 11502-11509, 11519-11524, 11540-11547, 11559-11565, 11575, 11584, 11598, 11613-11616, 11629-11633, 11645-11647, 11659-11680, 11691-11694, 11700-11708, 11720-11762, 11772-11837, 11849-11859, 11864-11881, 11886-11899, 11908-11948, 11958-12007, 12012-12015, 12027-12033, 12049-12060, 12072-12074, 12079-12080, 12094-12110, 12121-12184, 12228-12477, 12482-12483, 12488-12495, 12500-12664, 12669-12674, 12679, 12686 /usr/local/lib/python3.8/dist-packages/tornado/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/tornado/concurrent.py 80 50 38% 53, 60-65, 68, 117-135, 154-171, 184-185, 204-207, 229-231, 238, 245, 261-264 /usr/local/lib/python3.8/dist-packages/tornado/escape.py 144 91 37% 54, 61, 75, 83, 88, 102-103, 108, 115, 137-144, 159-165, 173, 178, 183, 192-196, 204, 209, 214, 225-227, 245-256, 309-375, 379-390 /usr/local/lib/python3.8/dist-packages/tornado/gen.py 298 227 24% 97, 127-138, 142-153, 189-241, 254, 279-282, 343-359, 363-367, 375-380, 383-386, 392-397, 400, 403-406, 457, 481-523, 539-544, 586-622, 639-643, 660, 663, 706-714, 720-768, 771-791, 796-802, 808-811, 831-842 /usr/local/lib/python3.8/dist-packages/tornado/ioloop.py 272 176 35% 59-61, 68, 71, 170-179, 201, 215, 231, 236, 241, 264-279, 298, 309-313, 320, 324, 333-341, 368, 374, 380, 399, 408, 417, 425, 438-445, 458, 490-532, 546, 580-587, 602, 620, 629, 644, 654, 663, 680-696, 713-726, 733, 742-763, 767, 787-789, 803-809, 821-825, 835, 838, 871-877, 884-887, 891-894, 901, 904-911, 914-916, 919-946 /usr/local/lib/python3.8/dist-packages/tornado/locks.py 158 107 32% 27, 43-44, 48-51, 115-116, 119-122, 130-142, 146-154, 158, 202-203, 206, 213, 220-225, 232, 240-258, 271, 274, 282, 382-386, 389-395, 399-412, 422-440, 443, 451, 454, 462, 475-476, 480-482, 523, 526, 536, 545-548, 551, 559, 562, 570 /usr/local/lib/python3.8/dist-packages/tornado/log.py 111 87 22% 39-40, 44-45, 56-71, 75-78, 138-161, 164-207, 216-255, 267-336 /usr/local/lib/python3.8/dist-packages/tornado/platform/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py 156 116 26% 36, 45-75, 78-90, 95-104, 107-123, 126-135, 138-139, 142-151, 154, 162, 169, 172-184, 189-194, 202, 205, 224, 229, 253-261, 264-266, 269-275, 278-280, 292, 308, 314, 338-346 /usr/local/lib/python3.8/dist-packages/tornado/queues.py 147 96 35% 40, 62-70, 75, 78, 154-166, 171, 175, 178, 181-184, 199-207, 214-223, 246-252, 260-270, 284-288, 296, 299, 303, 306, 309, 314-316, 320-324, 327, 330, 333-342, 371, 374, 377, 404, 407, 410 /usr/local/lib/python3.8/dist-packages/tornado/util.py 177 116 34% 37-40, 51-63, 81-84, 87, 101, 114, 120, 128, 149-157, 161-165, 177-185, 198-203, 210-213, 228, 270-287, 299, 305, 308, 328-334, 340-350, 355-356, 361-363, 375-380, 383-395, 404-407, 421-430, 436, 448-452, 458, 462-465, 470-472 /usr/local/lib/python3.8/dist-packages/traitlets/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/_version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/config/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/config/application.py 366 257 30% 54, 70, 74, 86-93, 113-117, 151-157, 177-181, 197-199, 209-229, 243-247, 273-282, 287-289, 297, 305-306, 310-332, 336-345, 348-359, 363-378, 385-406, 413, 419-421, 429-434, 438, 443-453, 469-498, 503-541, 550-589, 594, 599-609, 623-642, 646-650, 653-654, 662-664, 708-711 /usr/local/lib/python3.8/dist-packages/traitlets/config/configurable.py 190 142 25% 63-93, 102, 119-129, 134-168, 180-186, 196-200, 211-218, 227-250, 255, 260-286, 294-328, 342-343, 363-367, 373-379, 411-421 /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py 417 299 28% 53-55, 80, 83, 87, 91-93, 99-104, 108, 115-131, 138-147, 172-176, 180, 184-196, 206-216, 220-226, 232, 235, 238, 241-250, 260-266, 270-271, 277, 280-281, 285, 288-289, 292-297, 321-322, 339-344, 347, 356-357, 378-381, 385, 401-408, 411-412, 415-423, 426-427, 436-439, 452-458, 462-473, 477-489, 507-517, 521-527, 587-592, 596-597, 602-610, 636-686, 714-726, 738-748, 751-754, 757-758, 761, 766-768, 772-773, 782-807, 812-833, 846-857 /usr/local/lib/python3.8/dist-packages/traitlets/log.py 10 7 30% 18-27 /usr/local/lib/python3.8/dist-packages/traitlets/traitlets.py 1265 803 37% 52, 99-102, 110-118, 126-146, 153-156, 163-166, 173-178, 203-211, 217, 219, 221, 235-236, 245-252, 271-277, 281-285, 288-291, 294-297, 300-302, 325-332, 336-340, 343-346, 350-351, 406, 439-457, 473-475, 480-484, 499-514, 519-524, 527-543, 556, 559-574, 582-585, 588-594, 597-606, 609-612, 618-625, 632-637, 644-649, 660, 667, 681-686, 690-693, 697-706, 709-712, 729-732, 785, 788, 806-819, 848, 851, 894, 907, 914, 924, 933, 953-959, 965-977, 983-986, 992-1008, 1022-1028, 1031-1045, 1057-1065, 1076-1131, 1134, 1143-1176, 1179-1189, 1192-1198, 1228-1235, 1263-1265, 1284-1286, 1291-1297, 1319-1327, 1331-1334, 1338-1343, 1352, 1377-1387, 1395-1396, 1401, 1405, 1421-1437, 1441-1450, 1458-1459, 1476-1486, 1507, 1510-1524, 1561, 1566, 1574-1586, 1591, 1596, 1600-1601, 1604-1607, 1610-1614, 1655, 1660, 1664, 1666, 1674-1677, 1680-1688, 1691-1692, 1695-1696, 1699-1701, 1705, 1717-1718, 1746, 1752-1755, 1788-1790, 1793-1803, 1806-1809, 1812-1818, 1839-1853, 1869-1871, 1878-1882, 1886-1953, 1973-1977, 1984-1988, 1998-2002, 2009-2012, 2024-2026, 2033-2036, 2046-2054, 2061-2064, 2075-2082, 2087-2091, 2096-2101, 2111-2113, 2120-2123, 2136-2138, 2142-2145, 2155-2163, 2217, 2221, 2226, 2231-2233, 2236-2244, 2247-2257, 2265-2267, 2312-2314, 2317-2321, 2324-2326, 2416, 2421, 2429, 2433-2449, 2458-2461, 2501, 2504-2507, 2511-2515, 2517, 2524-2526, 2529-2533, 2536-2556, 2560, 2562-2563, 2567-2572, 2585-2591, 2602-2605, 2638-2645, 2649-2655, 2659-2663, 2666-2683, 2687-2690 /usr/local/lib/python3.8/dist-packages/traitlets/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/utils/bunch.py 12 8 33% 12-15, 18, 22-24 /usr/local/lib/python3.8/dist-packages/traitlets/utils/getargspec.py 64 58 9% 22-86 /usr/local/lib/python3.8/dist-packages/traitlets/utils/importstring.py 17 13 24% 27-42 /usr/local/lib/python3.8/dist-packages/traitlets/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/wcwidth/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/table_wide.py 1 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/table_zero.py 1 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/wcwidth.py 37 28 24% 116-129, 183-196, 212-220 /usr/local/lib/python3.8/dist-packages/wrapt/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/wrapt/decorators.py 186 91 51% 11-23, 40-41, 55-56, 60, 64, 68, 72, 76, 86, 91, 95, 99-102, 105-106, 112, 117-120, 123, 138, 142, 146, 149-150, 154, 158, 162-163, 165, 205, 208-212, 253-279, 292-294, 322, 343-390, 411, 444-445, 450-451, 454, 464-514 /usr/local/lib/python3.8/dist-packages/wrapt/importer.py 102 75 26% 12, 37-45, 52-98, 103-109, 112-119, 128-135, 145-148, 153, 156-159, 164, 172-221, 227-230 /usr/local/lib/python3.8/dist-packages/wrapt/wrappers.py 472 304 36% 11, 32, 36, 40, 44, 51, 60, 78-87, 91, 95, 99, 103, 107, 111, 114, 117, 121, 124, 130, 134, 138, 141, 144, 147, 150, 153, 156, 159, 162, 165, 168-190, 196-199, 202-216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300, 303-304, 307-308, 311-312, 315-316, 319-320, 323-324, 327-328, 331-332, 335-336, 339-340, 343-344, 347-348, 351-352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 406, 409, 412, 415, 418, 421, 424, 427, 431, 437, 442-453, 456-461, 471-477, 505-533, 542-566, 578-624, 704-719, 727-728, 733-771, 774, 777-780, 791-794, 797-798, 801, 804, 807-811, 819-828, 831, 834-836, 839-858, 870-880, 899-928, 936-947 /usr/local/lib/python3.8/dist-packages/zmq/__init__.py 41 16 61% 23-24, 32-37, 54-60, 64-67 /usr/local/lib/python3.8/dist-packages/zmq/_future.py 323 280 13% 28-112, 129-144, 149, 152-161, 165-168, 176, 187, 196-199, 208-212, 216-241, 249-278, 282-289, 299, 308-315, 319-350, 354-406, 410-445, 448-483, 488-493, 503-509, 513-515, 519-521, 528, 532-533, 540-543 /usr/local/lib/python3.8/dist-packages/zmq/asyncio/__init__.py 48 20 58% 18-19, 28, 34-37, 41-43, 53, 60, 75-76, 83-87, 93 /usr/local/lib/python3.8/dist-packages/zmq/backend/__init__.py 26 15 42% 14-17, 22, 28-40 /usr/local/lib/python3.8/dist-packages/zmq/backend/cython/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/zmq/backend/select.py 15 7 53% 29-35 /usr/local/lib/python3.8/dist-packages/zmq/error.py 79 44 44% 37-50, 58, 61, 90-91, 101-102, 107-108, 120, 123-124, 132-144, 157-162, 165, 168, 183-184 /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:720: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_origin_portfolio_ids = [int(item) for item in options.list_origin_portfolio_ids.split(",")] if options.list_origin_portfolio_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:721: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_photo_ids = [int(item) for item in options.list_photo_ids.split(",")] if options.list_photo_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:722: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval = [int(item) for item in options.interval_rotation.split(",")] if options.interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:723: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval = [float(item) for item in options.interval_resize.split(",")] if options.interval_resize is not "" else None /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:750: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi = [float(item) for item in options.mother_crop_portfolio_multi.split(",")] if options.mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1950: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1957: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2141: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2148: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py:84: SyntaxWarning: "is not" with a literal. Did you mean "!="? if (default_value.shape.ndims is not 0 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py:85: SyntaxWarning: "is not" with a literal. Did you mean "!="? and default_value.shape.ndims is not 1): /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py:285: SyntaxWarning: "is" with a literal. Did you mean "=="? minval_is_zero = minval is 0 # pylint: disable=literal-comparison /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py:286: SyntaxWarning: "is" with a literal. Did you mean "=="? maxval_is_one = maxval is 1 # pylint: disable=literal-comparison /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py:795: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(key) is 1: /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py:804: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(key) is 1: /usr/local/lib/python3.8/dist-packages/zmq/eventloop/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/zmq/eventloop/ioloop.py 64 35 45% 23-26, 42-45, 49-52, 55-60, 64-67, 75, 89-91, 103-106, 114-117, 129-130, 135 /usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py 246 181 26% 44, 49, 53-54, 60-61, 114-138, 142, 146, 150, 154, 182-189, 200-203, 241-243, 255-258, 265, 271-279, 285-287, 295-299, 306-307, 311, 344-393, 397, 401-419, 423, 427, 430, 435-444, 449-469, 473-486, 491-505, 508-509, 513-522, 526-527, 531-532, 536-542, 546 /usr/local/lib/python3.8/dist-packages/zmq/sugar/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/zmq/sugar/attrsettr.py 29 21 28% 15-32, 36, 40-48, 52 /usr/local/lib/python3.8/dist-packages/zmq/sugar/constants.py 53 5 91% 34, 47, 104, 107-108 /usr/local/lib/python3.8/dist-packages/zmq/sugar/context.py 125 78 38% 27, 43-49, 53-54, 57, 60, 64, 77-79, 89-94, 119-132, 139-145, 152-154, 157-163, 180-192, 196, 210-222, 229, 236, 240-243, 247-253, 257-266 /usr/local/lib/python3.8/dist-packages/zmq/sugar/frame.py 29 12 59% 12-14, 51, 61-62, 66-67, 77-78, 82-83 /usr/local/lib/python3.8/dist-packages/zmq/sugar/poll.py 62 48 23% 22-23, 26, 44-57, 61, 71-75, 95-99, 126-152 /usr/local/lib/python3.8/dist-packages/zmq/sugar/socket.py 229 160 30% 32, 39-40, 59-63, 66-67, 75, 78, 86, 99-101, 104-106, 114-117, 124-132, 142-155, 166, 175-177, 186-188, 207-209, 229-231, 261-283, 290-300, 313-328, 390-400, 431-447, 475-481, 499, 519-520, 548-549, 566-568, 592-593, 610-611, 631-632, 646-651, 673-674, 697-704, 725-746, 754-755 /usr/local/lib/python3.8/dist-packages/zmq/sugar/stopwatch.py 15 11 27% 12-21, 25, 29-30 /usr/local/lib/python3.8/dist-packages/zmq/sugar/tracker.py 56 37 34% 54-63, 68-74, 96-118 /usr/local/lib/python3.8/dist-packages/zmq/sugar/version.py 20 7 65% 17-18, 26-29, 36, 41 /usr/local/lib/python3.8/dist-packages/zmq/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/zmq/utils/constant_names.py 16 1 94% 549 /usr/local/lib/python3.8/dist-packages/zmq/utils/jsonapi.py 21 11 48% 26-27, 37-45, 53-56 /usr/local/lib/python3.8/dist-packages/zmq/utils/strtypes.py 23 13 43% 18-20, 24-29, 33-38 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ TOTAL 810185 560489 31% ret : 0 command : coverage3 html -i --omit=/usr/local/lib/python3.8/dist-packages/*,/home/admin/.local/lib/python3.8/site-packages/*,/usr/lib/python3/dist-packages/* -d htmlcov ret : 0 command : coverage3 report -i -m ret : 0 349.54user 154.22system 1:17:14elapsed 10%CPU (0avgtext+0avgdata 4916380maxresident)k 18039856inputs+993056outputs (588934major+10972486minor)pagefaults 0swaps