python /home/admin/mtr/script_for_cron.py -j python_test3 -m 12 -a ' --short_python3 -v ' -s python_test3 -M 0 -S 0 -U 100,100,120 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 list_job_run_as_list : ['mask_detection', 'datou', 'CacheModelData_queries', 'CachePhotoData_queries', 'test_fork', 'prepare_maskdata', 'portfolio_queries', 'sla_mensuel'] python version used : 3 liste_fichiers : [('tests/mask_test', True, 'Test mask-detection ', 'mask_detection'), ('tests/datou_test', True, 'Datou All Test', 'datou', 'all'), ('mtr/database_queries/CacheModelData_queries', True, 'Test Cache Model Data', 'CacheModelData_queries'), ('tests/cache_photo_data_test', True, 'Test local_cache_photo ', 'CachePhotoData_queries'), ('mtr/mask_rcnn/prepare_maskdata', True, 'test prepare mask data', 'prepare_maskdata', 'all'), ('mtr/database_queries/portfolio_queries', True, 'test portfolio queries', 'portfolio_queries'), ('prod/memo/memo', True, 'SLA Mensuel', 'sla_mensuel', 'all')] #&_# 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 : 5683 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 : 0.15319252014160156 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 22 09:35:28 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 mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 5709 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 2025-03-22 09:35:31.220067: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-22 09:35:31.247285: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-22 09:35:31.249425: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb02c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-22 09:35:31.249503: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-22 09:35:31.253417: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-22 09:35:31.466401: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2e964670 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-22 09:35:31.466469: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-22 09:35:31.467797: 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 2025-03-22 09:35:31.468226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:35:31.471305: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:35:31.474327: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-22 09:35:31.474949: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-22 09:35:31.477776: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-22 09:35:31.478959: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-22 09:35:31.483683: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-22 09:35:31.485017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-22 09:35:31.485113: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:35:31.485771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-22 09:35:31.485788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-22 09:35:31.485799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-22 09:35:31.487554: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5325 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 2025-03-22 09:35:32.223130: 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 2025-03-22 09:35:32.223209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:35:32.223231: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:35:32.223250: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-22 09:35:32.223268: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-22 09:35:32.223286: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-22 09:35:32.223315: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-22 09:35:32.223334: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-22 09:35:32.224538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-22 09:35:32.225638: 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 2025-03-22 09:35:32.225682: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:35:32.225704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:35:32.225725: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-22 09:35:32.225745: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-22 09:35:32.225765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-22 09:35:32.225785: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-22 09:35:32.225805: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-22 09:35:32.227027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-22 09:35:32.227062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-22 09:35:32.227073: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-22 09:35:32.227083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-22 09:35:32.228394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5325 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 : [] 2025-03-22 09:35:39.794358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:35:40.015219: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 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 395132 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4328 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 : 9617 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 : 0.0006422996520996094 nb_pixel_total : 15551 time to create 1 rle with old method : 0.01895284652709961 length of segment : 256 time for calcul the mask position with numpy : 0.0030312538146972656 nb_pixel_total : 145325 time to create 1 rle with old method : 0.16956830024719238 length of segment : 371 time for calcul the mask position with numpy : 0.00025463104248046875 nb_pixel_total : 14254 time to create 1 rle with old method : 0.016773700714111328 length of segment : 151 time for calcul the mask position with numpy : 0.00012421607971191406 nb_pixel_total : 5613 time to create 1 rle with old method : 0.007563114166259766 length of segment : 48 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 1824 time to create 1 rle with old method : 0.0024712085723876953 length of segment : 39 time spent for convertir_results : 0.9757754802703857 time spend for datou_step_exec : 18.451538562774658 time spend to save output : 7.271766662597656e-05 total time spend for step 1 : 18.451611280441284 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 3316 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 : 0.013692855834960938 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, 0.99548435, [(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, 52025-03-22 09:36:00.801680: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:36:00.997570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 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: (720, 1280, 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: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 396419 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4328 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 : 9617 list_Values should be empty [] ['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 : 0.00048041343688964844 nb_pixel_total : 16902 time to create 1 rle with old method : 0.018366575241088867 length of segment : 107 time for calcul the mask position with numpy : 0.009015321731567383 nb_pixel_total : 480755 time to create 1 rle with new method : 0.021271705627441406 length of segment : 632 time for calcul the mask position with numpy : 0.0008187294006347656 nb_pixel_total : 36642 time to create 1 rle with old method : 0.0392460823059082 length of segment : 133 time for calcul the mask position with numpy : 0.00012612342834472656 nb_pixel_total : 4793 time to create 1 rle with old method : 0.005912065505981445 length of segment : 51 time spent for convertir_results : 0.27176356315612793 time spend for datou_step_exec : 16.437876224517822 time spend to save output : 5.602836608886719e-05 total time spend for step 1 : 16.43793225288391 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 ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 404 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 : 0.01459646224975586 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917855882': [[(917855882, 492601069, 445, 1092, 1280, 0, 108, 0.9988368, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.99774855, [(711, 22, 21), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (488, 47, 590), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (413, 103, 677), (410, 104, 680), (405, 105, 685), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (368, 113, 722), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (348, 120, 742), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (286, 134, 803), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (233, 148, 856), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 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['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1742632550_394949_917855882_da0fa7b7e6b5b551fe26c0ba8713276d.jpg']} ############################### TEST 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 : 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 : 0.22730064392089844 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 22 09:36:08 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 : 9617 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-22 09:36:11.143488: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-22 09:36:11.167197: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-22 09:36:11.169191: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb030000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-22 09:36:11.169237: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-22 09:36:11.173038: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-22 09:36:11.420588: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2f7a7440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-22 09:36:11.420632: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-22 09:36:11.421906: 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 2025-03-22 09:36:11.422198: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:36:11.424423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:36:11.426411: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-22 09:36:11.426763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-22 09:36:11.429205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-22 09:36:11.430421: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-22 09:36:11.434555: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-22 09:36:11.435934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-22 09:36:11.436007: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:36:11.436746: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-22 09:36:11.436762: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-22 09:36:11.436770: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-22 09:36:11.438102: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8897 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. 2025-03-22 09:36:11.507857: 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 2025-03-22 09:36:11.507943: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:36:11.507966: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:36:11.507987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-22 09:36:11.508007: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-22 09:36:11.508028: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-22 09:36:11.508048: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-22 09:36:11.508068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-22 09:36:11.509565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-22 09:36:11.510756: 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 2025-03-22 09:36:11.510804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:36:11.510826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:36:11.510846: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-22 09:36:11.510866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-22 09:36:11.510886: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-22 09:36:11.510935: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-22 09:36:11.510957: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-22 09:36:11.512453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-22 09:36:11.512482: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-22 09:36:11.512492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-22 09:36:11.512502: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-22 09:36:11.514039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8897 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. 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 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 : [] 2025-03-22 09:36:19.501958: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:36:19.684193: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 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: (2448, 2448, 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: 2448.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 397400 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4328 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 : 9617 list_Values should be empty [] ['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 : 0.6524364948272705 nb_pixel_total : 3693265 time to create 1 rle with new method : 0.8474936485290527 length of segment : 2042 time spent for convertir_results : 2.503544807434082 time spend for datou_step_exec : 19.242994785308838 time spend to save output : 2.288818359375e-05 total time spend for step 1 : 19.24301767349243 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 ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 721 chid ids of type : 445 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++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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 : 0.012216329574584961 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 7, 2268, 118, 2241, 0.9850205, [(675, 120, 111), (520, 121, 480), (1053, 121, 378), (503, 122, 946), (486, 123, 981), (470, 124, 1014), (455, 125, 1046), (442, 126, 1091), (429, 127, 1135), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1222), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (364, 135, 1265), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (324, 159, 1388), (322, 160, 1393), (321, 161, 1397), (320, 162, 1401), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (281, 182, 1507), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (240, 196, 1623), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 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['1001,2150,936,2144,771,2092,694,2075,610,2037,288,1973,128,1971,97,1924,54,1825,39,1677,39,1453,29,1241,27,757,21,695,28,531,39,458,93,308,126,270,210,206,291,179,373,132,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2012,294,2094,411,2148,535,2168,613,2165,833,2128,914,2112,994,2081,1068,2031,1132,2009,1191,1950,1295,1931,1368,1876,1456,1846,1670,1766,1901,1718,1974,1662,2015,1581,2015,1496,2039,1420,2046,1339,2070,1177,2101,1097,2141'])], 'temp/1742632568_394949_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3691206 proportion of common points : 0.9997050614861489 [('test release memory', 'SUCCESS', True), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)] res_total : True #&_# TEST SUCCEEDED #&_# : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_6419193983607b8ec28e462733ffc29d3101ac80 SQL :INSERT INTO MTRAdmin.monitor_sys (name, type, server, version_code, result_str, result_bool, lien , test_group ,test_name) VALUES ('python_test3','1','marlene','refs/heads/master_6419193983607b8ec28e462733ffc29d3101ac80','{"mask_detection": "success"}','1','http://marlene.fotonower-preprod.com/job/2025/March/22032025/python_test3//data_2/data_log/job/2025/March/22032025/python_test3/log-python3----short_python3--v--marlene-09:35:01.txt','mask_detection','unknown'); #&_# 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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4573 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4573 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4573 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4573 # 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 ! no param json to modify List Step Type Loaded in datou : sam list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1189321094) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 1189321094 download finish for photo 1189321094 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.20295977592468262 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:sam Sat Mar 22 09:36:38 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632597_394949_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1742632597_394949_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png', 'extension': 'png'}} map_subphoto_mainphoto : {} Beginning of datou step sam ! pht : 4677 Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0022192001342773438 nb_pixel_total : 3785 time to create 1 rle with old method : 0.004686117172241211 time for calcul the mask position with numpy : 0.001627206802368164 nb_pixel_total : 6628 time to create 1 rle with old method : 0.008459091186523438 time for calcul the mask position with numpy : 0.0014328956604003906 nb_pixel_total : 2353 time to create 1 rle with old method : 0.0028007030487060547 time for calcul the mask position with numpy : 0.0014166831970214844 nb_pixel_total : 5614 time to create 1 rle with old method : 0.006632089614868164 time for calcul the mask position with numpy : 0.0021224021911621094 nb_pixel_total : 83597 time to create 1 rle with old method : 0.08880400657653809 time for calcul the mask position with numpy : 0.0013451576232910156 nb_pixel_total : 2780 time to create 1 rle with old method : 0.0031266212463378906 time for calcul the mask position with numpy : 0.0013768672943115234 nb_pixel_total : 13915 time to create 1 rle with old method : 0.01517176628112793 time for calcul the mask position with numpy : 0.001482248306274414 nb_pixel_total : 29432 time to create 1 rle with old method : 0.03605318069458008 time for calcul the mask position with numpy : 0.0015766620635986328 nb_pixel_total : 2027 time to create 1 rle with old method : 0.003459453582763672 time for calcul the mask position with numpy : 0.0015745162963867188 nb_pixel_total : 2941 time to create 1 rle with old method : 0.0049517154693603516 time for calcul the mask position with numpy : 0.0016546249389648438 nb_pixel_total : 10828 time to create 1 rle with old method : 0.01756906509399414 time for calcul the mask position with numpy : 0.002058267593383789 nb_pixel_total : 1228 time to create 1 rle with old method : 0.00214385986328125 time for calcul the mask position with numpy : 0.001680612564086914 nb_pixel_total : 4274 time to create 1 rle with old method : 0.00706934928894043 time for calcul the mask position with numpy : 0.0019724369049072266 nb_pixel_total : 18178 time to create 1 rle with old method : 0.029204130172729492 time for calcul the mask position with numpy : 0.0014553070068359375 nb_pixel_total : 3954 time to create 1 rle with old method : 0.004366874694824219 time for calcul the mask position with numpy : 0.0015010833740234375 nb_pixel_total : 13111 time to create 1 rle with old method : 0.01467585563659668 time for calcul the mask position with numpy : 0.0014140605926513672 nb_pixel_total : 2077 time to create 1 rle with old method : 0.0024919509887695312 time for calcul the mask position with numpy : 0.0013730525970458984 nb_pixel_total : 1261 time to create 1 rle with old method : 0.0015223026275634766 time for calcul the mask position with numpy : 0.0016238689422607422 nb_pixel_total : 38114 time to create 1 rle with old method : 0.04651165008544922 time for calcul the mask position with numpy : 0.0014178752899169922 nb_pixel_total : 7637 time to create 1 rle with old method : 0.008716821670532227 time for calcul the mask position with numpy : 0.0013582706451416016 nb_pixel_total : 1335 time to create 1 rle with old method : 0.0016620159149169922 time for calcul the mask position with numpy : 0.0013206005096435547 nb_pixel_total : 4270 time to create 1 rle with old method : 0.005237579345703125 time for calcul the mask position with numpy : 0.0013699531555175781 nb_pixel_total : 1686 time to create 1 rle with old method : 0.0020532608032226562 time for calcul the mask position with numpy : 0.0014119148254394531 nb_pixel_total : 2453 time to create 1 rle with old method : 0.0028722286224365234 time for calcul the mask position with numpy : 0.0014028549194335938 nb_pixel_total : 5483 time to create 1 rle with old method : 0.006589412689208984 time for calcul the mask position with numpy : 0.0014269351959228516 nb_pixel_total : 8638 time to create 1 rle with old method : 0.010308504104614258 time for calcul the mask position with numpy : 0.0014400482177734375 nb_pixel_total : 3538 time to create 1 rle with old method : 0.0045239925384521484 time for calcul the mask position with numpy : 0.0014605522155761719 nb_pixel_total : 2381 time to create 1 rle with old method : 0.003081083297729492 time for calcul the mask position with numpy : 0.00144195556640625 nb_pixel_total : 2728 time to create 1 rle with old method : 0.003366231918334961 time for calcul the mask position with numpy : 0.0014650821685791016 nb_pixel_total : 9859 time to create 1 rle with old method : 0.01130986213684082 time for calcul the mask position with numpy : 0.0015227794647216797 nb_pixel_total : 16417 time to create 1 rle with old method : 0.018374204635620117 time for calcul the mask position with numpy : 0.0014965534210205078 nb_pixel_total : 11916 time to create 1 rle with old method : 0.013852596282958984 time for calcul the mask position with numpy : 0.001605987548828125 nb_pixel_total : 13017 time to create 1 rle with old method : 0.022165298461914062 time for calcul the mask position with numpy : 0.001592397689819336 nb_pixel_total : 5408 time to create 1 rle with old method : 0.009112834930419922 time for calcul the mask position with numpy : 0.0017731189727783203 nb_pixel_total : 861 time to create 1 rle with old method : 0.0016512870788574219 time for calcul the mask position with numpy : 0.0015711784362792969 nb_pixel_total : 1648 time to create 1 rle with old method : 0.002886533737182617 time for calcul the mask position with numpy : 0.0016019344329833984 nb_pixel_total : 1025 time to create 1 rle with old method : 0.0017840862274169922 time for calcul the mask position with numpy : 0.001735687255859375 nb_pixel_total : 14655 time to create 1 rle with old method : 0.028953075408935547 time for calcul the mask position with numpy : 0.00164031982421875 nb_pixel_total : 3332 time to create 1 rle with old method : 0.005665779113769531 time for calcul the mask position with numpy : 0.001645803451538086 nb_pixel_total : 10624 time to create 1 rle with old method : 0.012542724609375 time for calcul the mask position with numpy : 0.001482248306274414 nb_pixel_total : 348 time to create 1 rle with old method : 0.0004558563232421875 time for calcul the mask position with numpy : 0.0014486312866210938 nb_pixel_total : 4110 time to create 1 rle with old method : 0.005149364471435547 time for calcul the mask position with numpy : 0.0015001296997070312 nb_pixel_total : 3928 time to create 1 rle with old method : 0.004885673522949219 time for calcul the mask position with numpy : 0.0014503002166748047 nb_pixel_total : 4173 time to create 1 rle with old method : 0.005275726318359375 time for calcul the mask position with numpy : 0.0015859603881835938 nb_pixel_total : 27955 time to create 1 rle with old method : 0.03128504753112793 time for calcul the mask position with numpy : 0.0014445781707763672 nb_pixel_total : 577 time to create 1 rle with old method : 0.000743865966796875 time for calcul the mask position with numpy : 0.001390218734741211 nb_pixel_total : 875 time to create 1 rle with old method : 0.0011715888977050781 time for calcul the mask position with numpy : 0.0014286041259765625 nb_pixel_total : 594 time to create 1 rle with old method : 0.0007956027984619141 time for calcul the mask position with numpy : 0.0014331340789794922 nb_pixel_total : 2324 time to create 1 rle with old method : 0.0028765201568603516 time for calcul the mask position with numpy : 0.0014421939849853516 nb_pixel_total : 2405 time to create 1 rle with old method : 0.003060579299926758 time for calcul the mask position with numpy : 0.0014214515686035156 nb_pixel_total : 885 time to create 1 rle with old method : 0.0011572837829589844 time for calcul the mask position with numpy : 0.001402139663696289 nb_pixel_total : 874 time to create 1 rle with old method : 0.0010662078857421875 time for calcul the mask position with numpy : 0.0014276504516601562 nb_pixel_total : 1638 time to create 1 rle with old method : 0.0021224021911621094 time for calcul the mask position with numpy : 0.0013935565948486328 nb_pixel_total : 692 time to create 1 rle with old method : 0.0009319782257080078 time for calcul the mask position with numpy : 0.0014307498931884766 nb_pixel_total : 1060 time to create 1 rle with old method : 0.0014057159423828125 time for calcul the mask position with numpy : 0.0014295578002929688 nb_pixel_total : 2773 time to create 1 rle with old method : 0.0032227039337158203 time for calcul the mask position with numpy : 0.0014162063598632812 nb_pixel_total : 337 time to create 1 rle with old method : 0.0004904270172119141 time for calcul the mask position with numpy : 0.0014104843139648438 nb_pixel_total : 1206 time to create 1 rle with old method : 0.001547098159790039 time for calcul the mask position with numpy : 0.0014219284057617188 nb_pixel_total : 583 time to create 1 rle with old method : 0.0008029937744140625 time for calcul the mask position with numpy : 0.0014312267303466797 nb_pixel_total : 1084 time to create 1 rle with old method : 0.0014448165893554688 time for calcul the mask position with numpy : 0.001443624496459961 nb_pixel_total : 3166 time to create 1 rle with old method : 0.0038771629333496094 time for calcul the mask position with numpy : 0.0014674663543701172 nb_pixel_total : 8606 time to create 1 rle with old method : 0.01044011116027832 time for calcul the mask position with numpy : 0.001424551010131836 nb_pixel_total : 970 time to create 1 rle with old method : 0.0011794567108154297 time for calcul the mask position with numpy : 0.0014503002166748047 nb_pixel_total : 3091 time to create 1 rle with old method : 0.0038514137268066406 time for calcul the mask position with numpy : 0.0014371871948242188 nb_pixel_total : 16687 time to create 1 rle with old method : 0.019716978073120117 time for calcul the mask position with numpy : 0.0014314651489257812 nb_pixel_total : 595 time to create 1 rle with old method : 0.00079345703125 time for calcul the mask position with numpy : 0.0014221668243408203 nb_pixel_total : 1744 time to create 1 rle with old method : 0.0021882057189941406 time for calcul the mask position with numpy : 0.0014662742614746094 nb_pixel_total : 9510 time to create 1 rle with old method : 0.011410713195800781 time for calcul the mask position with numpy : 0.0014655590057373047 nb_pixel_total : 8448 time to create 1 rle with old method : 0.00963592529296875 time for calcul the mask position with numpy : 0.001451730728149414 nb_pixel_total : 1513 time to create 1 rle with old method : 0.00183868408203125 time for calcul the mask position with numpy : 0.0015666484832763672 nb_pixel_total : 9079 time to create 1 rle with old method : 0.011032819747924805 time for calcul the mask position with numpy : 0.0014412403106689453 nb_pixel_total : 717 time to create 1 rle with old method : 0.0010006427764892578 time for calcul the mask position with numpy : 0.0014164447784423828 nb_pixel_total : 267 time to create 1 rle with old method : 0.00037407875061035156 time for calcul the mask position with numpy : 0.0014772415161132812 nb_pixel_total : 18440 time to create 1 rle with old method : 0.02080535888671875 time for calcul the mask position with numpy : 0.0014119148254394531 nb_pixel_total : 249 time to create 1 rle with old method : 0.00036978721618652344 time for calcul the mask position with numpy : 0.0013780593872070312 nb_pixel_total : 618 time to create 1 rle with old method : 0.0007905960083007812 time for calcul the mask position with numpy : 0.0014367103576660156 nb_pixel_total : 900 time to create 1 rle with old method : 0.0011670589447021484 time for calcul the mask position with numpy : 0.0013337135314941406 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003223419189453125 time for calcul the mask position with numpy : 0.0013892650604248047 nb_pixel_total : 968 time to create 1 rle with old method : 0.0012445449829101562 time for calcul the mask position with numpy : 0.0013594627380371094 nb_pixel_total : 733 time to create 1 rle with old method : 0.0010454654693603516 time for calcul the mask position with numpy : 0.0014438629150390625 nb_pixel_total : 1526 time to create 1 rle with old method : 0.001996755599975586 time for calcul the mask position with numpy : 0.0014371871948242188 nb_pixel_total : 1632 time to create 1 rle with old method : 0.0020360946655273438 time for calcul the mask position with numpy : 0.0015308856964111328 nb_pixel_total : 5016 time to create 1 rle with old method : 0.0061452388763427734 time for calcul the mask position with numpy : 0.0014317035675048828 nb_pixel_total : 7497 time to create 1 rle with old method : 0.008762121200561523 time for calcul the mask position with numpy : 0.001432657241821289 nb_pixel_total : 1442 time to create 1 rle with old method : 0.001882791519165039 time for calcul the mask position with numpy : 0.0014183521270751953 nb_pixel_total : 298 time to create 1 rle with old method : 0.000484466552734375 time for calcul the mask position with numpy : 0.0014445781707763672 nb_pixel_total : 918 time to create 1 rle with old method : 0.0013949871063232422 time for calcul the mask position with numpy : 0.001436471939086914 nb_pixel_total : 2192 time to create 1 rle with old method : 0.0027642250061035156 time for calcul the mask position with numpy : 0.0014269351959228516 nb_pixel_total : 1132 time to create 1 rle with old method : 0.0014069080352783203 time for calcul the mask position with numpy : 0.0014390945434570312 nb_pixel_total : 1612 time to create 1 rle with old method : 0.002079486846923828 time for calcul the mask position with numpy : 0.0014307498931884766 nb_pixel_total : 889 time to create 1 rle with old method : 0.0011222362518310547 time for calcul the mask position with numpy : 0.0014505386352539062 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0017306804656982422 time for calcul the mask position with numpy : 0.001443624496459961 nb_pixel_total : 886 time to create 1 rle with old method : 0.0011835098266601562 time for calcul the mask position with numpy : 0.001439809799194336 nb_pixel_total : 947 time to create 1 rle with old method : 0.0012454986572265625 time for calcul the mask position with numpy : 0.001428365707397461 nb_pixel_total : 477 time to create 1 rle with old method : 0.0006723403930664062 time for calcul the mask position with numpy : 0.0013494491577148438 nb_pixel_total : 1389 time to create 1 rle with old method : 0.001836538314819336 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 96 chid ids of type : 4677 Number RLEs to save : 8786 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3724018743', '199', '535', '11') ... last line : ('3724018838', '587', '148', '18') INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] map_info['map_portfolio_photo'] : {} final : True mtd_id 4573 list_pids : [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 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 : [('4573', None, '1189321094', 'None', None, None, None, None, None)] time used for this insertion : 0.015196800231933594 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.006794691085815 time spend to save output : 0.015444278717041016 total time spend for step 1 : 9.022238969802856 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1742632597_394949_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 96 ############################### TEST frcnn ################################ test frcnn Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4184 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4184 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4184 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4184 # 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 ! no param json to modify List Step Type Loaded in datou : frcnn list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917754606) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917754606 download finish for photo 917754606 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1219935417175293 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:frcnn Sat Mar 22 09:36:47 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632607_394949_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1742632607_394949_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Faster rcnn ! classes : ['background', 'plaque'] pht : 4370 caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 3375, 'mtr_user_id': 31, 'name': 'detection_plaque_valcor_010622', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,plaque', 'svm_portfolios_learning': '0,0', 'photo_hashtag_type': 4370, 'photo_desc_type': 5676, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0'} 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/1742632607_394949_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 0.111s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.063836984 (374.127, 293.91992, 430.80988, 317.80908) proba : 0.052214842 (382.1786, 297.1881, 552.35974, 344.65845) proba : 0.01227079 (345.3563, 272.43, 468.8588, 320.72443) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 2.467766523361206 time spend to save output : 6.604194641113281e-05 total time spend for step 1 : 2.467832565307617 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True Inside saveFrcnn : final : True verbose : True threshold to save the result : 0.1 output flattener : [(0, 493029425, 4370, 374, 430, 293, 317, 0.063836984, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052214842, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.01227079, None)] 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 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 : [('4184', None, '917754606', '0', 0, '0', 493061979, '0', None)] time used for this insertion : 0.1444246768951416 [917754606] map_info['map_portfolio_photo'] : {} final : True mtd_id 4184 list_pids : [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 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 : [('4184', None, '917754606', None, None, None, None, None, None)] time used for this insertion : 0.011971473693847656 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, 0.063836984, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052214842, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.01227079, None)], 'temp/1742632607_394949_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=2 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 2 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=2 # 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 ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (916235064) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 916235064 download finish for photo 916235064 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.11983847618103027 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:thcl Sat Mar 22 09:36:49 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632609_394949_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1742632609_394949_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'355': 1} we are using the classfication for only one thcl 355 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (66, 66, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.006005764007568359 time to convert the images to numpy array : 0.0008282661437988281 total time to convert the images to numpy array : 0.007145881652832031 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 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (355) 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', 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'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 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (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 : 5520 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (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)) param : , param.caffemodel : car_360_1027 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/caffe_cuda8_python3/python/:/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 : 5520 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.012161970138549805 time used to do the prediction : 0.058939456939697266 save descriptor for thcl : 355 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.04941391944885254 Testing : ['916235064'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (916235064) Catched exception ! Connect or reconnect ! result : {916235064: {'photo_id': 916235064, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2017/10/14/6293d1bb790dc6902450e7c572b7d10b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': None}} list_photo_exists : [916235064] storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.9664952754974365 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True time used to find the portfolios of the photos select button_legend_list from MTRDatou.classification_theme where id = 355 SAVE THCL, output : {'916235064': [[('916235064', 'c_elysee_1027_gao__port_506302', 0.0018815849, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011635853, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.0008157693, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.001177302, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.002584991, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.0041701626, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.003480726, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.0073668663, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021888209, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.0005798443, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.004590961, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031587672, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.0022508367, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.00532045, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0010998852, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.0054026474, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.0039188405, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014787939, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019778125, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.0012442751, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015049729, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021692035, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.0027081647, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.004704038, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019418669, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.00095854705, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.00076999364, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019467506, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.00137253, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.0016201949, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013925161, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010045045, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010793159, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.0008447493, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.0012520859, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.0025844933, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.001243002, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.0028650414, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012948669, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010339454, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018765646, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011343628, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015758522, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.003980156, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.002873848, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023355403, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012105576, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.00114475, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.0022338766, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017464529, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.0046993634, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021913492, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.002263168, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018175197, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.00090566394, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.0020402954, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025336612, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.004488961, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.003425329, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031352732, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010255846, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.001535515, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.0005914995, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.004029594, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011586093, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012158313, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014967471, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.0011320522, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010545942, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011985237, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.0041854717, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.0023635612, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016323685, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015567545, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013958507, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.0021979413, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.008827565, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.0014615526, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.0008825144, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.002471315, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010093102, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.001596322, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.0015330747, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.0014917812, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014585975, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.005645798, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.0011165634, 332, '355'), 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'viano_1027_gao__port_506211', 0.0026943109, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.00083202665, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037378243, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.0007919804, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030524854, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.003723088, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017740733, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015305674, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004086814, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011862413, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.0026954277, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011407441, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012528892, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.0015149594, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.003165294, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017347479, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.016726412, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018255492, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.002145868, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014152399, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.0020553505, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.002296556, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013943978, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.011968763, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010548779, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020715005, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017870486, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.005721971, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.002766615, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017144994, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.0048254225, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018686518, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012580353, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012625228, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.00092443137, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.007467018, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.00072905637, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.0010385977, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.000691143, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.0021418373, 332, '355')]]} begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 6.4373016357421875e-06 save missing photos in datou_result : time spend for datou_step_exec : 5.862262487411499 time spend to save output : 1.9849369525909424 total time spend for step 1 : 7.847199440002441 step2:argmax Sat Mar 22 09:36:57 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632609_394949_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1742632609_394949_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 355 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.017712008, 332, '355'), 'temp/1742632609_394949_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('916235064', '2049863950', '332') ... last line : ('916235064', '2049863950', '332') time used for this insertion : 0.015828609466552734 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.01844620704650879 len list_finale : 1, len picture : 1 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 : [('2', None, '916235064', 'c15_1027_gao__port_506055', None, None, '2049863950', '0.017712008', None)] time used for this insertion : 0.014056682586669922 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 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 : [] time used for this insertion : 3.5762786865234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.000225067138671875 time spend to save output : 0.048624515533447266 total time spend for step 2 : 0.04884958267211914 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', 0.017712008, 332, '355'), 'temp/1742632609_394949_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4567 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4567 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4567 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4567 # 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 ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171252784,1171252764,1171252487) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171252487 begin to download photo : 1171252764 begin to download photo : 1171252784 download finish for photo 1171252764 download finish for photo 1171252487 download finish for photo 1171252784 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.2160048484802246 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:tfhub_classification2 Sat Mar 22 09:36:58 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632617_394949_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1742632617_394949_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1742632617_394949_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784} map_photo_id_path_extension : {1171252764: {'path': 'temp/1742632617_394949_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1742632617_394949_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252784: {'path': 'temp/1742632617_394949_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3609': 1} we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory 2025-03-22 09:37:00.659199: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-22 09:37:00.659871: 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 2025-03-22 09:37:00.659947: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:37:00.659995: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:37:00.661793: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-22 09:37:00.661894: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-22 09:37:00.664380: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-22 09:37:00.666231: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-22 09:37:00.670852: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-22 09:37:00.672054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-22 09:37:00.672390: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-22 09:37:00.703131: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-22 09:37:00.705111: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fad98000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-22 09:37:00.705156: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-22 09:37:00.708789: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x38b9c780 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-22 09:37:00.708832: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-22 09:37:00.710054: 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 2025-03-22 09:37:00.710173: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:37:00.710205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-22 09:37:00.710298: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-22 09:37:00.710338: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-22 09:37:00.710385: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-22 09:37:00.710436: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-22 09:37:00.710488: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-22 09:37:00.712149: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-22 09:37:00.712235: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-22 09:37:00.712305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-22 09:37:00.712322: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-22 09:37:00.712336: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-22 09:37:00.714027: 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 : 5520 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3609) 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 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (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 : [] /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 : 8.217599391937256 time used to load_weights : 0.12184643745422363 0it [00:00, ?it/s] 3it [00:00, 1053.40it/s]2025-03-22 09:37:11.087404: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1742632617_394949_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1742632617_394949_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1742632617_394949_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 2.809218168258667 ['temp/image000000000_1742632617_394949_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'temp/image000000001_1742632617_394949_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'temp/image000000002_1742632617_394949_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 (3, 1280) Got the blobs of the net to insert : [0, 6, 0, 1, 0, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000100'| Got the blobs of the net to insert : [0, 9, 0, 0, 0, 0, 1, 0, 0, 0] code_as_byte_string:b'0009000000'| Got the blobs of the net to insert : [0, 6, 0, 0, 1, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000001'| time to traite the descriptors : 0.024945497512817383 Testing : ['1171252764', '1171252487', '1171252784'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171252764,1171252487,1171252784) result : {1171252487: {'photo_id': 1171252487, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5ebdd6b0a6bb39942a3808ed114806de.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_35_005998m0.jpg 0.4259977941513062 for time 6.000020980834961, id_amount 3 this amount prod time diff : 0.006000020980834961'}, 1171252764: {'photo_id': 1171252764, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/29d5179a892cc50aadc9d67245534b59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_41_005998m0.jpg 0.4319977941513062 for time 6.0, id_amount 3 this amount prod time diff : 0.006'}, 1171252784: {'photo_id': 1171252784, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_47_006033m0.jpg 0.4379978291988373 for time 6.000035047531128, id_amount 4 this amount prod time diff : 0.006000035047531128'}} list_photo_exists : [1171252487, 1171252764, 1171252784] storage_type for insertDescriptorsMulti : 3 To insert : 1171252764 To insert : 1171252487 To insert : 1171252784 time to insert the descriptors : 1.096487283706665 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252764, 1171252487, 1171252784] map_info['map_portfolio_photo'] : {} final : False mtd_id 4567 list_pids : [1171252764, 1171252487, 1171252784] Looping around the photos to save general results len do output : 3 /1171252764Didn't retrieve data . /1171252487Didn't retrieve data . /1171252784Didn'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, '1171252764', 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, '1171252784', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 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 : [('4567', None, '1171252764', 'None', None, None, None, None, None), ('4567', None, '1171252487', 'None', None, None, None, None, None), ('4567', None, '1171252784', 'None', None, None, None, None, None)] time used for this insertion : 0.012403488159179688 save_final save missing photos in datou_result : time spend for datou_step_exec : 16.92656373977661 time spend to save output : 0.012710094451904297 total time spend for step 1 : 16.939273834228516 step2:argmax Sat Mar 22 09:37:15 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632617_394949_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1742632617_394949_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1742632617_394949_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784} map_photo_id_path_extension : {1171252764: {'path': 'temp/1742632617_394949_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1742632617_394949_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252784: {'path': 'temp/1742632617_394949_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.9853498, 4674, '3609'), 'temp/1742632617_394949_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.9262227, 4674, '3609'), 'temp/1742632617_394949_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.9677619, 4674, '3609'), 'temp/1742632617_394949_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('1171252764', '495916461', '4674') ... last line : ('1171252784', '495916461', '4674') time used for this insertion : 0.012723922729492188 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.016527175903320312 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 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 : [('4567', None, '1171252764', 'jrm', None, None, '495916461', '0.9853498', None), ('4567', None, '1171252487', 'jrm', None, None, '495916461', '0.9262227', None), ('4567', None, '1171252784', 'jrm', None, None, '495916461', '0.9677619', None)] time used for this insertion : 0.013092041015625 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 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 : [] time used for this insertion : 3.337860107421875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0001552104949951172 time spend to save output : 0.04700160026550293 total time spend for step 2 : 0.04715681076049805 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252764': [(1171252764, 'jrm', 0.9853498, 4674, '3609'), 'temp/1742632617_394949_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262227, 4674, '3609'), 'temp/1742632617_394949_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'], '1171252784': [(1171252784, 'jrm', 0.9677619, 4674, '3609'), 'temp/1742632617_394949_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4621 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4621 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4621 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4621 # 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 ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171291875,1171275372,1171275314) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171275314 begin to download photo : 1171275372 begin to download photo : 1171291875 download finish for photo 1171291875 download finish for photo 1171275314 download finish for photo 1171275372 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.28423166275024414 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:tfhub_classification2 Sat Mar 22 09:37:15 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632635_394949_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1742632635_394949_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1742632635_394949_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1742632635_394949_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1742632635_394949_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1742632635_394949_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3655': 1} 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 : 1968 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1968 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1968 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1968 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1968 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1968 wait 20 seconds l 3637 free memory gpu now : 1968 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3655) 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 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (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 : 8.034995079040527 time used to load_weights : 0.1303081512451172 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.077469825744629 ['temp/1742632635_394949_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1742632635_394949_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'temp/1742632635_394949_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 (3, 1280) Got the blobs of the net to insert : [0, 1, 0, 0, 11, 0, 2, 2, 0, 0] code_as_byte_string:b'000100000b'| Got the blobs of the net to insert : [0, 0, 0, 0, 8, 0, 0, 0, 3, 0] code_as_byte_string:b'0000000008'| Got the blobs of the net to insert : [0, 0, 0, 0, 14, 0, 1, 4, 0, 0] code_as_byte_string:b'000000000e'| time to traite the descriptors : 0.04176449775695801 Testing : ['1171291875', '1171275314', '1171275372'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171291875,1171275314,1171275372) result : {1171275314: {'photo_id': 1171275314, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/6e0a72c8fa00d5e4b018bd689b547133.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_54_22_6187.jpg'}, 1171275372: {'photo_id': 1171275372, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/76d81364ff7df843bff095f45c07ba35.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_56_46_6098.jpg'}, 1171291875: {'photo_id': 1171291875, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/b62cd9e0d976b143f86fe82d072798c0.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_59_04_5803.jpg'}} list_photo_exists : [1171275314, 1171275372, 1171291875] storage_type for insertDescriptorsMulti : 3 To insert : 1171291875 To insert : 1171275314 To insert : 1171275372 time to insert the descriptors : 0.9871499538421631 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171291875, 1171275314, 1171275372] map_info['map_portfolio_photo'] : {} final : False mtd_id 4621 list_pids : [1171291875, 1171275314, 1171275372] Looping around the photos to save general results len do output : 3 /1171291875Didn't retrieve data . /1171275314Didn't retrieve data . /1171275372Didn'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, '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) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 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 : [('4621', None, '1171291875', 'None', None, None, None, None, None), ('4621', None, '1171275314', 'None', None, None, None, None, None), ('4621', None, '1171275372', 'None', None, None, None, None, None)] time used for this insertion : 0.012732982635498047 save_final save missing photos in datou_result : time spend for datou_step_exec : 139.54292249679565 time spend to save output : 0.013030529022216797 total time spend for step 1 : 139.55595302581787 step2:argmax Sat Mar 22 09:39:34 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632635_394949_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1742632635_394949_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1742632635_394949_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1742632635_394949_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1742632635_394949_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1742632635_394949_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.97065896, 4723, '3655'), 'temp/1742632635_394949_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96513057, 4723, '3655'), 'temp/1742632635_394949_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.9674588, 4723, '3655'), 'temp/1742632635_394949_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('1171291875', '2107748999', '4723') ... last line : ('1171275372', '2107748999', '4723') time used for this insertion : 0.013112068176269531 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.01593327522277832 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 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 : [('4621', None, '1171291875', 'tapis_vide', None, None, '2107748999', '0.97065896', None), ('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.96513057', None), ('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.9674588', None)] time used for this insertion : 0.012762784957885742 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 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 : [] time used for this insertion : 4.76837158203125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00017762184143066406 time spend to save output : 0.046915531158447266 total time spend for step 2 : 0.04709315299987793 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171291875': [(1171291875, 'tapis_vide', 0.97065896, 4723, '3655'), 'temp/1742632635_394949_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96513057, 4723, '3655'), 'temp/1742632635_394949_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9674588, 4723, '3655'), 'temp/1742632635_394949_1171275372_76d81364ff7df843bff095f45c07ba35.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (358) 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 SELECT hashtag_id,hashtag FROM MTRBack.hashtags where hashtag in ('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 dict_keys(['cartegrise_orientation__port_505064', 'car_exterieur_angle_arriere_droit_axe_arriere__port_504217', 'car_exterieur_angle_arriere_droit_axe_droit__port_504215', 'car_exterieur_angle_arriere_droit__port_504160', 'car_exterieur_angle_arriere_gauche_axe_arriere__port_504201', 'car_exterieur_angle_arriere_gauche_axe_gauche__port_504199', 'car_exterieur_angle_arriere_gauche__port_504170', 'car_exterieur_angle_avant_droit_axe_arriere__port_504226', 'car_exterieur_angle_avant_droit_axe_droit__port_504225', 'car_exterieur_angle_avant_droit__port_504087', 'car_exterieur_angle_avant_gauche_axe_avant__port_504235', 'car_exterieur_angle_avant_gauche_axe_gauche__port_504234', 'car_exterieur_angle_avant_gauche__port_504098', 'car_exterieur_arriere__port_504184', 'car_exterieur_avant__port_504146', 'car_exterieur_cote_droite__port_504108', 'car_exterieur_cote_droit_axe_arriere__port_504214', 'car_exterieur_cote_droit_axe_avant__port_504465', 'car_exterieur_cote_gauche_axe_arriere__port_504198', 'car_exterieur_cote_gauche_axe_avant__port_504233', 'car_exterieur_cote_gauche__port_504017', 'car_exterieur_face_arriere_axe_droit__port_504218', 'car_exterieur_face_arriere_axe_gauche__port_504202', 'car_exterieur_face_avant_axe_droit__port_504451', 'car_exterieur_face_avant_axe_gauche__port_504236', 'car_exterieur_moteur__port_503704', 'car_exterieur_roue_de_secour__port_503763', 'car_exterieur_roue__port_503398', 'car_interieur_avant_volant_class_1__port_506503', 'car_interieur_avant_volant_class_2__port_506504', 'car_interieur_avant_volant_class_6_boutonrond__port_506562', 'car_interieur_avant_volant_class_6_class_2__port_506563', 'car_interieur_avant_volant_class_6_ecrangrosplan__port_506564', 'car_interieur_avant_volant_class_6_levierdevitesse__port_506565', 'car_interieur_avant_vue-arriere_class_1__port_506531', 'car_interieur_avant_vue-arriere_class_2__port_506532', 'car_interieur_avant_vue_droite_habitacle_class_1__port_506540', 'car_interieur_avant_vue_gauche_habitacle_class_1__port_506494', 'car_interieur_coffre__port_503412', 'car_interieur_siege_arriere_class_1__port_506551', 'firstuploadexperveo_carrosseriegrosplan_carrosserie__port_506483', 'firstuploadexperveo_carrosseriegrosplan_class_6__port_506487', 'firstuploadexperveo_carrosseriegrosplan_morceauderoue__port_506484', 'firstuploadexperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'firstuploadexperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'firstuploadexperveo_carrosseriegrosplan_vindanslamoquette__port_506486', 'firstuploadexperveo_rouetranche__port_505677', 'firstuploadexperveo_vignette__port_505674', 'firstuploadexperveo_vin__port_505675', 'interieur_compteur_kilometrique__port_503644', 'uploadphotoimmatbest_class_1__port_505051']) select photo_hashtag_type from MTRDatou.classification_theme where id = 358 thcl : 358 photo_hashtag_type : 337 SELECT phi.hashtag_id , phi.photo_id FROM MTRBack.photo_hashtag_ids phi, MTRUser.mtr_portfolio_photos mp where phi.type = 337 and phi.photo_id = mp.mtr_photo_id and mp.mtr_portfolio_id =510365; {510365: [(917973295, 1), (917973297, 1), (917973302, 1), (917973293, 7), (917973296, 11), (917973300, 11), (917973286, 13), (917973289, 13), (917973301, 24), (917973285, 29), (917973290, 29), (917973299, 29), (917973304, 35), (917973287, 36), (917973298, 36), (917973305, 36), (917973292, 37), (917973291, 41), (917973303, 41), (917973294, 42), (917973288, 46)]} ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=230 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=230 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 230 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=230 # 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 ! no param json to modify List Step Type Loaded in datou : rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.13582587242126465 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:rotate Sat Mar 22 09:39:44 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 90 degree temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 90 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : False image_rotate : image_path : temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 180 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : False image_rotate : image_path : temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 270 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : False image_rotate : image_path : temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg image_rotate.mode : RGB 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/1742632784_394949 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.1127214431762695 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1346373416, 'temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1346373417, 'temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1346373418} Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] After datou_step_exec type output : time spend for datou_step_exec : 1.3631532192230225 time spend to save output : 0.0001399517059326172 total time spend for step 1 : 1.363293170928955 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 230 list_pids : [917849322] Looping around the photos to save general results len do output : 3 /1346373416Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373417Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373418Didn'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 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 : [('230', None, '1346373416', 'None', None, None, None, None, None), ('230', None, '1346373417', 'None', None, None, None, None, None), ('230', None, '1346373418', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.01273798942565918 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1346373416: ['917849322', 'temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1346373417: ['917849322', 'temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1346373418: ['917849322', 'temp/1742632783_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=233 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=233 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 233 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=233 # 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 ! no param json to modify List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.17029023170471191 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 3 step1:thcl Sat Mar 22 09:39:45 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'500': 1} we are using the classfication for only one thcl 500 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (2448, 3264, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.00021767616271972656 time to convert the images to numpy array : 0.9459187984466553 total time to convert the images to numpy array : 0.9466736316680908 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 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (500) 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 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (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 : 35 wait 20 seconds l 3637 free memory gpu now : 35 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (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)) param : , param.caffemodel : orientation_carte_grise_all_2 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/caffe_cuda8_python3/python/:/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 havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 1968 wait 20 seconds l 3637 free memory gpu now : 1968 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 2.488075017929077 time used to do the prediction : 0.21039032936096191 save descriptor for thcl : 500 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.0501856803894043 Testing : ['917849322'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (917849322) result : {917849322: {'photo_id': 917849322, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/9/13/2bd260e91e91df8378dde8bb8b8c4548.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_13092022_12_32_19_5566.jpg'}} list_photo_exists : [917849322] storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.5662918090820312 After datou_step_exec type output : time spend for datou_step_exec : 49.32090997695923 time spend to save output : 5.698204040527344e-05 total time spend for step 1 : 49.320966958999634 step2:argmax Sat Mar 22 09:40:35 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 : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976496, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005033048, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003661863, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.001480859, 507, '500')]]} input_args_next_step : {'917849322': ()} output_args : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976496, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005033048, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003661863, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.001480859, 507, '500')]]} args : 917849322 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ([('917849322', 'carteGrisesVerticales__port_549774', 0.9976496, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005033048, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.0003661863, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.001480859, 507, '500')],) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 500 After datou_step_exec type output : time spend for datou_step_exec : 0.00020623207092285156 time spend to save output : 2.0742416381835938e-05 total time spend for step 2 : 0.0002269744873046875 step3:rotate Sat Mar 22 09:40:35 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 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976496, 507, '500'), 'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ()} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976496, 507, '500'), 'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 1 complete output_args for input 1 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976496, 507, '500'), 'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ('temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg',)} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976496, 507, '500'), 'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', ('917849322', 'carteGrisesVerticales__port_549774', 0.9976496, 507, '500')) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} 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 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 0 degree temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 0 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : False image_rotate : image_path : temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg image_rotate.mode : RGB 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/1742632835_394949 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.6523303985595703 map_filename_photo_id : 1 map_filename_photo_id : {'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg': 1346373450} Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] 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 : [] After datou_step_exec type output : time spend for datou_step_exec : 0.7506272792816162 time spend to save output : 5.412101745605469e-05 total time spend for step 3 : 0.7506814002990723 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 233 list_pids : [917849322] Looping around the photos to save general results len do output : 1 /1346373450Didn'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 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 : [('233', None, '1346373450', 'None', None, None, None, None, None), ('233', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.017577171325683594 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1346373450: ['917849322', 'temp/1742632785_394949_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=937852786 AND `type`=520 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3723841074,3723841075,3723841078,3723841081) # 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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=243 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=243 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 243 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=243 # 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 ! no param json to modify List Step Type Loaded in datou : crop, tile, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (937852786) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 937852786 download finish for photo 937852786 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.13810300827026367 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 3 step1:crop Sat Mar 22 09:40:36 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786} map_photo_id_path_extension : {937852786: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} 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 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786) and `type` in (407) Loaded 4 chid ids of type : 407 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) +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 SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos where photo_id in ( 937852786) WARNING : margin is only used for type bib ! type of cropped photo chosen : we resize croppped photo by 1 on x axis and by 1 on y axis new_file_path_bib_crop : temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg new_file_path_bib_crop : temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg new_file_path_bib_crop : temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg new_file_path_bib_crop : temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg map_result returned by crop_photo_return_map_crop : length : 4 map_result after crop : {8165075: {'crop': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (426, 467, 312, 347), 'sub_photo_infos': (418, 475, 304, 355, 1, 1), 'same_chi': False}, 8165076: {'crop': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (411, 445, 443, 480), 'sub_photo_infos': (403, 453, 435, 480, 1, 1), 'same_chi': False}, 8165077: {'crop': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (103, 138, 358, 396), 'sub_photo_infos': (95, 146, 350, 404, 1, 1), 'same_chi': False}, 8165078: {'crop': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (104, 131, 256, 292), 'sub_photo_infos': (96, 139, 248, 300, 1, 1), 'same_chi': False}} 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 : 21607071 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1742632838_394949 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (21607071, 1346373456, 0, NOW()),(21607071, 1346373457, 0, NOW()),(21607071, 1346373458, 0, NOW()),(21607071, 1346373459, 0, NOW()) 4 we have uploaded 4 photos in the portfolio 21607071 time of upload the photos Elapsed time : 4.2584309577941895 {'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1346373456, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1346373457, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1346373458, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1346373459} list_errors : [] map_result_insert : {'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1346373456, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1346373457, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1346373458, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1346373459} Now we prepare data that will be used for ellipse search ! chi_id found to be used 8165075 path of cropped varroa found to be used to match on an ellipse temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg sub_photo_id found to be used 1346373456 chi_id found to be used 8165076 path of cropped varroa found to be used to match on an ellipse temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg sub_photo_id found to be used 1346373457 chi_id found to be used 8165077 path of cropped varroa found to be used to match on an ellipse temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg sub_photo_id found to be used 1346373458 chi_id found to be used 8165078 path of cropped varroa found to be used to match on an ellipse temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg sub_photo_id found to be used 1346373459 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165075, '1346373456', 31), (8165076, '1346373457', 31), (8165077, '1346373458', 31), (8165078, '1346373459', 31)] map of cropped photos with some data : {'1346373456': ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', (426, 467, 312, 347)], '1346373457': ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', (411, 445, 443, 480)], '1346373458': ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', (103, 138, 358, 396)], '1346373459': ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', (104, 131, 256, 292)]} About to compute ellipse and record with type : 520 (54, 57) (51, 57) [54, 57] (54, 57) score : 5120 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [14.25, 42.75, 1.78125]), ('yc', [12.75, 38.25, 1.59375]), ('radius', [14.25, 42.75, 1.78125])] {0.5: 18351, 0.55: 16004, 0.6000000000000001: 14127, 0.65: 12575, 0.7000000000000001: 11300, 0.75: 10158, 0.8: 9208, 0.8500000000000001: 8418, 0.9: 7642, 0.9500000000000001: 6925, 1.0: 6364, 1.05: 5843, 1.1: 5353, 1.1500000000000001: 4868, 1.2000000000000002: 4553, 1.25: 4250, 1.3: 3947, 1.35: 3698, 1.4000000000000001: 3438, 1.4500000000000002: 3230, 1.5: 3012, 1.55: 2829, 1.6: 2636, 1.6500000000000001: 2535, 1.7000000000000002: 2460, 1.75: 2388, 1.8: 2372, 1.85: 2354, 1.9000000000000001: 2332, 1.9500000000000002: 2311} [(1.9500000000000002, 2311), (1.9000000000000001, 2332), (1.85, 2354), (1.8, 2372), (1.75, 2388), (1.7000000000000002, 2460), (1.6500000000000001, 2535), (1.6, 2636), (1.55, 2829), (1.5, 3012), (1.4500000000000002, 3230), (1.4000000000000001, 3438), (1.35, 3698), (1.3, 3947), (1.25, 4250), (1.2000000000000002, 4553), (1.1500000000000001, 4868), (1.1, 5353), (1.05, 5843), (1.0, 6364), (0.9500000000000001, 6925), (0.9, 7642), (0.8500000000000001, 8418), (0.8, 9208), (0.75, 10158), (0.7000000000000001, 11300), (0.65, 12575), (0.6000000000000001, 14127), (0.55, 16004), (0.5, 18351)] arg_min reach at : 1.9500000000000002 with value = 2311 | arg_min : 1.9500000000000002 min_score : 2311{-90.0: 3359, -85.0: 3372, -80.0: 3395, -75.0: 3380, -70.0: 3304, -65.0: 3180, -60.0: 2991, -55.0: 2793, -50.0: 2601, -45.0: 2377, -40.0: 2183, -35.0: 2078, -30.0: 1968, -25.0: 1968, -20.0: 2021, -15.0: 2036, -10.0: 2102, -5.0: 2188, 0.0: 2208, 5.0: 2265, 10.0: 2311, 15.0: 2333, 20.0: 2362, 25.0: 2375, 30.0: 2375, 35.0: 2397, 40.0: 2370, 45.0: 2421, 50.0: 2491, 55.0: 2661, 60.0: 2826, 65.0: 2993, 70.0: 3106, 75.0: 3182, 80.0: 3263, 85.0: 3306} [(-30.0, 1968), (-25.0, 1968), (-20.0, 2021), (-15.0, 2036), (-35.0, 2078), (-10.0, 2102), (-40.0, 2183), (-5.0, 2188), (0.0, 2208), (5.0, 2265), (10.0, 2311), (15.0, 2333), (20.0, 2362), (40.0, 2370), (25.0, 2375), (30.0, 2375), (-45.0, 2377), (35.0, 2397), (45.0, 2421), (50.0, 2491), (-50.0, 2601), (55.0, 2661), (-55.0, 2793), (60.0, 2826), (-60.0, 2991), (65.0, 2993), (70.0, 3106), (-65.0, 3180), (75.0, 3182), (80.0, 3263), (-70.0, 3304), (85.0, 3306), (-90.0, 3359), (-85.0, 3372), (-75.0, 3380), (-80.0, 3395)] arg_min reach at : -30.0 with value = 1968 | arg_min : -30.0 min_score : 1968{14.25: 1703, 16.03125: 1654, 17.8125: 1614, 19.59375: 1679, 21.375: 1702, 23.15625: 1712, 24.9375: 1730, 26.71875: 1847, 28.5: 1968, 30.28125: 2177, 32.0625: 2456, 33.84375: 2827, 35.625: 3294, 37.40625: 3723, 39.1875: 4161, 40.96875: 4643} [(17.8125, 1614), (16.03125, 1654), (19.59375, 1679), (21.375, 1702), (14.25, 1703), (23.15625, 1712), (24.9375, 1730), (26.71875, 1847), (28.5, 1968), (30.28125, 2177), (32.0625, 2456), (33.84375, 2827), (35.625, 3294), (37.40625, 3723), (39.1875, 4161), (40.96875, 4643)] arg_min reach at : 17.8125 with value = 1614 | arg_min : 17.8125 min_score : 1614{12.75: 5534, 14.34375: 5352, 15.9375: 5116, 17.53125: 4723, 19.125: 4111, 20.71875: 3506, 22.3125: 2799, 23.90625: 2194, 25.5: 1614, 27.09375: 1339, 28.6875: 1206, 30.28125: 1137, 31.875: 1105, 33.46875: 1339, 35.0625: 1659, 36.65625: 2022} [(31.875, 1105), (30.28125, 1137), (28.6875, 1206), (27.09375, 1339), (33.46875, 1339), (25.5, 1614), (35.0625, 1659), (36.65625, 2022), (23.90625, 2194), (22.3125, 2799), (20.71875, 3506), (19.125, 4111), (17.53125, 4723), (15.9375, 5116), (14.34375, 5352), (12.75, 5534)] arg_min reach at : 31.875 with value = 1105 | arg_min : 31.875 min_score : 1105{14.25: 1942, 16.03125: 1853, 17.8125: 1756, 19.59375: 1651, 21.375: 1533, 23.15625: 1424, 24.9375: 1309, 26.71875: 1199, 28.5: 1105, 30.28125: 1219, 32.0625: 1332, 33.84375: 1547, 35.625: 1881, 37.40625: 2378, 39.1875: 3042, 40.96875: 3824} [(28.5, 1105), (26.71875, 1199), (30.28125, 1219), (24.9375, 1309), (32.0625, 1332), (23.15625, 1424), (21.375, 1533), (33.84375, 1547), (19.59375, 1651), (17.8125, 1756), (16.03125, 1853), (35.625, 1881), (14.25, 1942), (37.40625, 2378), (39.1875, 3042), (40.96875, 3824)] arg_min reach at : 28.5 with value = 1105 | arg_min : 28.5 min_score : 1105{0.5: 17397, 0.55: 15138, 0.6000000000000001: 13352, 0.65: 11859, 0.7000000000000001: 10523, 0.75: 9463, 0.8: 8469, 0.8500000000000001: 7565, 0.9: 6551, 0.9500000000000001: 5592, 1.0: 4856, 1.05: 4291, 1.1: 3654, 1.1500000000000001: 3099, 1.2000000000000002: 2647, 1.25: 2309, 1.3: 1983, 1.35: 1755, 1.4000000000000001: 1594, 1.4500000000000002: 1447, 1.5: 1358, 1.55: 1323, 1.6: 1219, 1.6500000000000001: 1215, 1.7000000000000002: 1201, 1.75: 1131, 1.8: 1134, 1.85: 1142, 1.9000000000000001: 1137, 1.9500000000000002: 1105} [(1.9500000000000002, 1105), (1.75, 1131), (1.8, 1134), (1.9000000000000001, 1137), (1.85, 1142), (1.7000000000000002, 1201), (1.6500000000000001, 1215), (1.6, 1219), (1.55, 1323), (1.5, 1358), (1.4500000000000002, 1447), (1.4000000000000001, 1594), (1.35, 1755), (1.3, 1983), (1.25, 2309), (1.2000000000000002, 2647), (1.1500000000000001, 3099), (1.1, 3654), (1.05, 4291), (1.0, 4856), (0.9500000000000001, 5592), (0.9, 6551), (0.8500000000000001, 7565), (0.8, 8469), (0.75, 9463), (0.7000000000000001, 10523), (0.65, 11859), (0.6000000000000001, 13352), (0.55, 15138), (0.5, 17397)] arg_min reach at : 1.9500000000000002 with value = 1105 arg_min : 1.9500000000000002 min_score : 1105{-90.0: 3514, -85.0: 3396, -80.0: 3238, -75.0: 3017, -70.0: 2764, -65.0: 2549, -60.0: 2339, -55.0: 2105, -50.0: 1912, -45.0: 1701, -40.0: 1467, -35.0: 1266, -30.0: 1105, -25.0: 1110, -20.0: 1114, -15.0: 1111, -10.0: 1110, -5.0: 1121, 0.0: 1120, 5.0: 1118, 10.0: 1109, 15.0: 1109, 20.0: 1099, 25.0: 1088, 30.0: 1108, 35.0: 1355, 40.0: 1649, 45.0: 1925, 50.0: 2257, 55.0: 2542, 60.0: 2845, 65.0: 3137, 70.0: 3370, 75.0: 3500, 80.0: 3578, 85.0: 3540} [(25.0, 1088), (20.0, 1099), (-30.0, 1105), (30.0, 1108), (10.0, 1109), (15.0, 1109), (-25.0, 1110), (-10.0, 1110), (-15.0, 1111), (-20.0, 1114), (5.0, 1118), (0.0, 1120), (-5.0, 1121), (-35.0, 1266), (35.0, 1355), (-40.0, 1467), (40.0, 1649), (-45.0, 1701), (-50.0, 1912), (45.0, 1925), (-55.0, 2105), (50.0, 2257), (-60.0, 2339), (55.0, 2542), (-65.0, 2549), (-70.0, 2764), (60.0, 2845), (-75.0, 3017), (65.0, 3137), (-80.0, 3238), (70.0, 3370), (-85.0, 3396), (75.0, 3500), (-90.0, 3514), (85.0, 3540), (80.0, 3578)] arg_min reach at : 25.0 with value = 1088 arg_min : 25.0 min_score : 1088{14.25: 1174, 16.03125: 1133, 17.8125: 1088, 19.59375: 1051, 21.375: 1026, 23.15625: 997, 24.9375: 979, 26.71875: 1139, 28.5: 1344, 30.28125: 1564, 32.0625: 1949, 33.84375: 2422, 35.625: 2930, 37.40625: 3453, 39.1875: 4006, 40.96875: 4592} [(24.9375, 979), (23.15625, 997), (21.375, 1026), (19.59375, 1051), (17.8125, 1088), (16.03125, 1133), (26.71875, 1139), (14.25, 1174), (28.5, 1344), (30.28125, 1564), (32.0625, 1949), (33.84375, 2422), (35.625, 2930), (37.40625, 3453), (39.1875, 4006), (40.96875, 4592)] arg_min reach at : 24.9375 with value = 979 arg_min : 24.9375 min_score : 979{12.75: 5539, 14.34375: 5239, 15.9375: 4911, 17.53125: 4425, 19.125: 3749, 20.71875: 3181, 22.3125: 2575, 23.90625: 2124, 25.5: 1808, 27.09375: 1478, 28.6875: 1290, 30.28125: 1104, 31.875: 979, 33.46875: 1070, 35.0625: 1368, 36.65625: 1872} [(31.875, 979), (33.46875, 1070), (30.28125, 1104), (28.6875, 1290), (35.0625, 1368), (27.09375, 1478), (25.5, 1808), (36.65625, 1872), (23.90625, 2124), (22.3125, 2575), (20.71875, 3181), (19.125, 3749), (17.53125, 4425), (15.9375, 4911), (14.34375, 5239), (12.75, 5539)] arg_min reach at : 31.875 with value = 979 arg_min : 31.875 min_score : 979{14.25: 1939, 16.03125: 1852, 17.8125: 1755, 19.59375: 1643, 21.375: 1531, 23.15625: 1397, 24.9375: 1264, 26.71875: 1119, 28.5: 979, 30.28125: 1023, 32.0625: 1319, 33.84375: 1771, 35.625: 2432, 37.40625: 3324, 39.1875: 4299, 40.96875: 5405} [(28.5, 979), (30.28125, 1023), (26.71875, 1119), (24.9375, 1264), (32.0625, 1319), (23.15625, 1397), (21.375, 1531), (19.59375, 1643), (17.8125, 1755), (33.84375, 1771), (16.03125, 1852), (14.25, 1939), (35.625, 2432), (37.40625, 3324), (39.1875, 4299), (40.96875, 5405)] arg_min reach at : 28.5 with value = 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 x0 : 426 y1 : 347 width : 41, height : 35, area : 1435, score : 1.0 x0 : 432 y1 : 355 width : 35, height : 52, area : 1820, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! (47, 50) (45, 50) [47, 50] (47, 50) score : 5362 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [12.5, 37.5, 1.5625]), ('yc', [11.25, 33.75, 1.40625]), ('radius', [12.5, 37.5, 1.5625])] {0.5: 15776, 0.55: 13889, 0.6000000000000001: 12446, 0.65: 11147, 0.7000000000000001: 10181, 0.75: 9228, 0.8: 8441, 0.8500000000000001: 7765, 0.9: 7258, 0.9500000000000001: 6684, 1.0: 6195, 1.05: 5920, 1.1: 5487, 1.1500000000000001: 5099, 1.2000000000000002: 4795, 1.25: 4511, 1.3: 4245, 1.35: 4042, 1.4000000000000001: 3828, 1.4500000000000002: 3629, 1.5: 3467, 1.55: 3235, 1.6: 3126, 1.6500000000000001: 2946, 1.7000000000000002: 2826, 1.75: 2700, 1.8: 2549, 1.85: 2472, 1.9000000000000001: 2381, 1.9500000000000002: 2281} [(1.9500000000000002, 2281), (1.9000000000000001, 2381), (1.85, 2472), (1.8, 2549), (1.75, 2700), (1.7000000000000002, 2826), (1.6500000000000001, 2946), (1.6, 3126), (1.55, 3235), (1.5, 3467), (1.4500000000000002, 3629), (1.4000000000000001, 3828), (1.35, 4042), (1.3, 4245), (1.25, 4511), (1.2000000000000002, 4795), (1.1500000000000001, 5099), (1.1, 5487), (1.05, 5920), (1.0, 6195), (0.9500000000000001, 6684), (0.9, 7258), (0.8500000000000001, 7765), (0.8, 8441), (0.75, 9228), (0.7000000000000001, 10181), (0.65, 11147), (0.6000000000000001, 12446), (0.55, 13889), (0.5, 15776)] arg_min reach at : 1.9500000000000002 with value = 2281 | arg_min : 1.9500000000000002 min_score : 2281{-90.0: 3295, -85.0: 3240, -80.0: 3290, -75.0: 3269, -70.0: 3287, -65.0: 3223, -60.0: 3095, -55.0: 3043, -50.0: 2985, -45.0: 2906, -40.0: 2796, -35.0: 2724, -30.0: 2625, -25.0: 2489, -20.0: 2330, -15.0: 2220, -10.0: 2127, -5.0: 2127, 0.0: 2143, 5.0: 2182, 10.0: 2281, 15.0: 2385, 20.0: 2506, 25.0: 2665, 30.0: 2768, 35.0: 2900, 40.0: 2939, 45.0: 2994, 50.0: 3007, 55.0: 3076, 60.0: 3095, 65.0: 3201, 70.0: 3243, 75.0: 3247, 80.0: 3279, 85.0: 3240} [(-10.0, 2127), (-5.0, 2127), (0.0, 2143), (5.0, 2182), (-15.0, 2220), (10.0, 2281), (-20.0, 2330), (15.0, 2385), (-25.0, 2489), (20.0, 2506), (-30.0, 2625), (25.0, 2665), (-35.0, 2724), (30.0, 2768), (-40.0, 2796), (35.0, 2900), (-45.0, 2906), (40.0, 2939), (-50.0, 2985), (45.0, 2994), (50.0, 3007), (-55.0, 3043), (55.0, 3076), (-60.0, 3095), (60.0, 3095), (65.0, 3201), (-65.0, 3223), (-85.0, 3240), (85.0, 3240), (70.0, 3243), (75.0, 3247), (-75.0, 3269), (80.0, 3279), (-70.0, 3287), (-80.0, 3290), (-90.0, 3295)] arg_min reach at : -10.0 with value = 2127 | arg_min : -10.0 min_score : 2127{12.5: 2171, 14.0625: 2288, 15.625: 2314, 17.1875: 2360, 18.75: 2347, 20.3125: 2300, 21.875: 2249, 23.4375: 2188, 25.0: 2127, 26.5625: 2163, 28.125: 2315, 29.6875: 2476, 31.25: 2756, 32.8125: 2992, 34.375: 3319, 35.9375: 3672} [(25.0, 2127), (26.5625, 2163), (12.5, 2171), (23.4375, 2188), (21.875, 2249), (14.0625, 2288), (20.3125, 2300), (15.625, 2314), (28.125, 2315), (18.75, 2347), (17.1875, 2360), (29.6875, 2476), (31.25, 2756), (32.8125, 2992), (34.375, 3319), (35.9375, 3672)] arg_min reach at : 25.0 with value = 2127 | arg_min : 25.0 min_score : 2127{11.25: 6815, 12.65625: 6505, 14.0625: 5928, 15.46875: 5326, 16.875: 4772, 18.28125: 4095, 19.6875: 3413, 21.09375: 2728, 22.5: 2127, 23.90625: 1694, 25.3125: 1345, 26.71875: 1048, 28.125: 823, 29.53125: 728, 30.9375: 714, 32.34375: 1027} [(30.9375, 714), (29.53125, 728), (28.125, 823), (32.34375, 1027), (26.71875, 1048), (25.3125, 1345), (23.90625, 1694), (22.5, 2127), (21.09375, 2728), (19.6875, 3413), (18.28125, 4095), (16.875, 4772), (15.46875, 5326), (14.0625, 5928), (12.65625, 6505), (11.25, 6815)] arg_min reach at : 30.9375 with value = 714 | arg_min : 30.9375 min_score : 714{12.5: 1275, 14.0625: 1207, 15.625: 1132, 17.1875: 1049, 18.75: 966, 20.3125: 866, 21.875: 784, 23.4375: 729, 25.0: 714, 26.5625: 976, 28.125: 1546, 29.6875: 2147, 31.25: 2981, 32.8125: 3767, 34.375: 4761, 35.9375: 5732} [(25.0, 714), (23.4375, 729), (21.875, 784), (20.3125, 866), (18.75, 966), (26.5625, 976), (17.1875, 1049), (15.625, 1132), (14.0625, 1207), (12.5, 1275), (28.125, 1546), (29.6875, 2147), (31.25, 2981), (32.8125, 3767), (34.375, 4761), (35.9375, 5732)] arg_min reach at : 25.0 with value = 714 | arg_min : 25.0 min_score : 714{0.5: 20107, 0.55: 18155, 0.6000000000000001: 16503, 0.65: 15058, 0.7000000000000001: 13673, 0.75: 12474, 0.8: 11100, 0.8500000000000001: 9667, 0.9: 8374, 0.9500000000000001: 7344, 1.0: 6274, 1.05: 5570, 1.1: 4846, 1.1500000000000001: 4170, 1.2000000000000002: 3623, 1.25: 3211, 1.3: 2832, 1.35: 2475, 1.4000000000000001: 2182, 1.4500000000000002: 1949, 1.5: 1655, 1.55: 1513, 1.6: 1333, 1.6500000000000001: 1182, 1.7000000000000002: 1038, 1.75: 921, 1.8: 841, 1.85: 732, 1.9000000000000001: 733, 1.9500000000000002: 714} [(1.9500000000000002, 714), (1.85, 732), (1.9000000000000001, 733), (1.8, 841), (1.75, 921), (1.7000000000000002, 1038), (1.6500000000000001, 1182), (1.6, 1333), (1.55, 1513), (1.5, 1655), (1.4500000000000002, 1949), (1.4000000000000001, 2182), (1.35, 2475), (1.3, 2832), (1.25, 3211), (1.2000000000000002, 3623), (1.1500000000000001, 4170), (1.1, 4846), (1.05, 5570), (1.0, 6274), (0.9500000000000001, 7344), (0.9, 8374), (0.8500000000000001, 9667), (0.8, 11100), (0.75, 12474), (0.7000000000000001, 13673), (0.65, 15058), (0.6000000000000001, 16503), (0.55, 18155), (0.5, 20107)] arg_min reach at : 1.9500000000000002 with value = 714 arg_min : 1.9500000000000002 min_score : 714{-90.0: 2866, -85.0: 3006, -80.0: 3063, -75.0: 3168, -70.0: 3193, -65.0: 3193, -60.0: 3149, -55.0: 3022, -50.0: 2885, -45.0: 2656, -40.0: 2413, -35.0: 2119, -30.0: 1819, -25.0: 1500, -20.0: 1171, -15.0: 865, -10.0: 714, -5.0: 668, 0.0: 689, 5.0: 734, 10.0: 824, 15.0: 898, 20.0: 1116, 25.0: 1368, 30.0: 1610, 35.0: 1866, 40.0: 2105, 45.0: 2304, 50.0: 2467, 55.0: 2604, 60.0: 2731, 65.0: 2753, 70.0: 2753, 75.0: 2761, 80.0: 2722, 85.0: 2786} [(-5.0, 668), (0.0, 689), (-10.0, 714), (5.0, 734), (10.0, 824), (-15.0, 865), (15.0, 898), (20.0, 1116), (-20.0, 1171), (25.0, 1368), (-25.0, 1500), (30.0, 1610), (-30.0, 1819), (35.0, 1866), (40.0, 2105), (-35.0, 2119), (45.0, 2304), (-40.0, 2413), (50.0, 2467), (55.0, 2604), (-45.0, 2656), (80.0, 2722), (60.0, 2731), (65.0, 2753), (70.0, 2753), (75.0, 2761), (85.0, 2786), (-90.0, 2866), (-50.0, 2885), (-85.0, 3006), (-55.0, 3022), (-80.0, 3063), (-60.0, 3149), (-75.0, 3168), (-70.0, 3193), (-65.0, 3193)] arg_min reach at : -5.0 with value = 668 arg_min : -5.0 min_score : 668{12.5: 1164, 14.0625: 1082, 15.625: 1019, 17.1875: 933, 18.75: 866, 20.3125: 765, 21.875: 713, 23.4375: 655, 25.0: 668, 26.5625: 845, 28.125: 1107, 29.6875: 1374, 31.25: 1691, 32.8125: 2036, 34.375: 2375, 35.9375: 2731} [(23.4375, 655), (25.0, 668), (21.875, 713), (20.3125, 765), (26.5625, 845), (18.75, 866), (17.1875, 933), (15.625, 1019), (14.0625, 1082), (28.125, 1107), (12.5, 1164), (29.6875, 1374), (31.25, 1691), (32.8125, 2036), (34.375, 2375), (35.9375, 2731)] arg_min reach at : 23.4375 with value = 655 arg_min : 23.4375 min_score : 655{11.25: 7209, 12.65625: 6826, 14.0625: 6195, 15.46875: 5638, 16.875: 4941, 18.28125: 4177, 19.6875: 3484, 21.09375: 2750, 22.5: 2183, 23.90625: 1709, 25.3125: 1327, 26.71875: 1008, 28.125: 779, 29.53125: 631, 30.9375: 655, 32.34375: 920} [(29.53125, 631), (30.9375, 655), (28.125, 779), (32.34375, 920), (26.71875, 1008), (25.3125, 1327), (23.90625, 1709), (22.5, 2183), (21.09375, 2750), (19.6875, 3484), (18.28125, 4177), (16.875, 4941), (15.46875, 5638), (14.0625, 6195), (12.65625, 6826), (11.25, 7209)] arg_min reach at : 29.53125 with value = 631 arg_min : 29.53125 min_score : 631{12.5: 1282, 14.0625: 1211, 15.625: 1130, 17.1875: 1050, 18.75: 960, 20.3125: 871, 21.875: 772, 23.4375: 672, 25.0: 631, 26.5625: 666, 28.125: 947, 29.6875: 1472, 31.25: 2184, 32.8125: 3032, 34.375: 3932, 35.9375: 5005} [(25.0, 631), (26.5625, 666), (23.4375, 672), (21.875, 772), (20.3125, 871), (28.125, 947), (18.75, 960), (17.1875, 1050), (15.625, 1130), (14.0625, 1211), (12.5, 1282), (29.6875, 1472), (31.25, 2184), (32.8125, 3032), (34.375, 3932), (35.9375, 5005)] arg_min reach at : 25.0 with value = 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 x0 : 411 y1 : 480 width : 34, height : 37, area : 1258, score : 1.0 x0 : 419 y1 : 483 width : 26, height : 49, area : 1274, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! (55, 54) (54, 51) [55, 54] (55, 54) score : 4603 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [12.75, 38.25, 1.59375]), ('yc', [13.5, 40.5, 1.6875]), ('radius', [13.5, 40.5, 1.6875])] {0.5: 14831, 0.55: 12756, 0.6000000000000001: 10989, 0.65: 9469, 0.7000000000000001: 8158, 0.75: 7251, 0.8: 6254, 0.8500000000000001: 5559, 0.9: 5119, 0.9500000000000001: 4564, 1.0: 4072, 1.05: 3783, 1.1: 3496, 1.1500000000000001: 3342, 1.2000000000000002: 3228, 1.25: 3161, 1.3: 3127, 1.35: 3087, 1.4000000000000001: 3047, 1.4500000000000002: 3050, 1.5: 2990, 1.55: 2992, 1.6: 2992, 1.6500000000000001: 2985, 1.7000000000000002: 2988, 1.75: 2999, 1.8: 2994, 1.85: 2981, 1.9000000000000001: 3002, 1.9500000000000002: 2995} [(1.85, 2981), (1.6500000000000001, 2985), (1.7000000000000002, 2988), (1.5, 2990), (1.55, 2992), (1.6, 2992), (1.8, 2994), (1.9500000000000002, 2995), (1.75, 2999), (1.9000000000000001, 3002), (1.4000000000000001, 3047), (1.4500000000000002, 3050), (1.35, 3087), (1.3, 3127), (1.25, 3161), (1.2000000000000002, 3228), (1.1500000000000001, 3342), (1.1, 3496), (1.05, 3783), (1.0, 4072), (0.9500000000000001, 4564), (0.9, 5119), (0.8500000000000001, 5559), (0.8, 6254), (0.75, 7251), (0.7000000000000001, 8158), (0.65, 9469), (0.6000000000000001, 10989), (0.55, 12756), (0.5, 14831)] arg_min reach at : 1.85 with value = 2981 | arg_min : 1.85 min_score : 2981{-90.0: 1442, -85.0: 1411, -80.0: 1411, -75.0: 1423, -70.0: 1409, -65.0: 1428, -60.0: 1417, -55.0: 1385, -50.0: 1356, -45.0: 1374, -40.0: 1401, -35.0: 1501, -30.0: 1635, -25.0: 1772, -20.0: 1988, -15.0: 2183, -10.0: 2376, -5.0: 2552, 0.0: 2691, 5.0: 2860, 10.0: 2981, 15.0: 3063, 20.0: 3044, 25.0: 2938, 30.0: 2889, 35.0: 2865, 40.0: 2787, 45.0: 2694, 50.0: 2566, 55.0: 2452, 60.0: 2275, 65.0: 2055, 70.0: 1849, 75.0: 1731, 80.0: 1532, 85.0: 1466} [(-50.0, 1356), (-45.0, 1374), (-55.0, 1385), (-40.0, 1401), (-70.0, 1409), (-85.0, 1411), (-80.0, 1411), (-60.0, 1417), (-75.0, 1423), (-65.0, 1428), (-90.0, 1442), (85.0, 1466), (-35.0, 1501), (80.0, 1532), (-30.0, 1635), (75.0, 1731), (-25.0, 1772), (70.0, 1849), (-20.0, 1988), (65.0, 2055), (-15.0, 2183), (60.0, 2275), (-10.0, 2376), (55.0, 2452), (-5.0, 2552), (50.0, 2566), (0.0, 2691), (45.0, 2694), (40.0, 2787), (5.0, 2860), (35.0, 2865), (30.0, 2889), (25.0, 2938), (10.0, 2981), (20.0, 3044), (15.0, 3063)] arg_min reach at : -50.0 with value = 1356 | arg_min : -50.0 min_score : 1356{12.75: 3483, 14.34375: 3154, 15.9375: 2818, 17.53125: 2499, 19.125: 2212, 20.71875: 1992, 22.3125: 1750, 23.90625: 1554, 25.5: 1356, 27.09375: 1213, 28.6875: 1123, 30.28125: 1079, 31.875: 1312, 33.46875: 1586, 35.0625: 1971, 36.65625: 2464} [(30.28125, 1079), (28.6875, 1123), (27.09375, 1213), (31.875, 1312), (25.5, 1356), (23.90625, 1554), (33.46875, 1586), (22.3125, 1750), (35.0625, 1971), (20.71875, 1992), (19.125, 2212), (36.65625, 2464), (17.53125, 2499), (15.9375, 2818), (14.34375, 3154), (12.75, 3483)] arg_min reach at : 30.28125 with value = 1079 | arg_min : 30.28125 min_score : 1079{13.5: 1299, 15.1875: 1190, 16.875: 1129, 18.5625: 1073, 20.25: 1041, 21.9375: 1025, 23.625: 995, 25.3125: 1002, 27.0: 1079, 28.6875: 1280, 30.375: 1506, 32.0625: 1818, 33.75: 2218, 35.4375: 2606, 37.125: 3013, 38.8125: 3545} [(23.625, 995), (25.3125, 1002), (21.9375, 1025), (20.25, 1041), (18.5625, 1073), (27.0, 1079), (16.875, 1129), (15.1875, 1190), (28.6875, 1280), (13.5, 1299), (30.375, 1506), (32.0625, 1818), (33.75, 2218), (35.4375, 2606), (37.125, 3013), (38.8125, 3545)] arg_min reach at : 23.625 with value = 995 | arg_min : 23.625 min_score : 995{13.5: 1907, 15.1875: 1827, 16.875: 1734, 18.5625: 1635, 20.25: 1520, 21.9375: 1400, 23.625: 1268, 25.3125: 1139, 27.0: 995, 28.6875: 1081, 30.375: 1376, 32.0625: 1837, 33.75: 2365, 35.4375: 3034, 37.125: 3644, 38.8125: 4482} [(27.0, 995), (28.6875, 1081), (25.3125, 1139), (23.625, 1268), (30.375, 1376), (21.9375, 1400), (20.25, 1520), (18.5625, 1635), (16.875, 1734), (15.1875, 1827), (32.0625, 1837), (13.5, 1907), (33.75, 2365), (35.4375, 3034), (37.125, 3644), (38.8125, 4482)] arg_min reach at : 27.0 with value = 995 | arg_min : 27.0 min_score : 995{0.5: 16358, 0.55: 14291, 0.6000000000000001: 12544, 0.65: 11117, 0.7000000000000001: 9800, 0.75: 8586, 0.8: 7533, 0.8500000000000001: 6488, 0.9: 5599, 0.9500000000000001: 4779, 1.0: 4075, 1.05: 3520, 1.1: 3021, 1.1500000000000001: 2582, 1.2000000000000002: 2150, 1.25: 1820, 1.3: 1534, 1.35: 1347, 1.4000000000000001: 1219, 1.4500000000000002: 1130, 1.5: 1073, 1.55: 1037, 1.6: 998, 1.6500000000000001: 961, 1.7000000000000002: 981, 1.75: 984, 1.8: 995, 1.85: 995, 1.9000000000000001: 1023, 1.9500000000000002: 1058} [(1.6500000000000001, 961), (1.7000000000000002, 981), (1.75, 984), (1.8, 995), (1.85, 995), (1.6, 998), (1.9000000000000001, 1023), (1.55, 1037), (1.9500000000000002, 1058), (1.5, 1073), (1.4500000000000002, 1130), (1.4000000000000001, 1219), (1.35, 1347), (1.3, 1534), (1.25, 1820), (1.2000000000000002, 2150), (1.1500000000000001, 2582), (1.1, 3021), (1.05, 3520), (1.0, 4075), (0.9500000000000001, 4779), (0.9, 5599), (0.8500000000000001, 6488), (0.8, 7533), (0.75, 8586), (0.7000000000000001, 9800), (0.65, 11117), (0.6000000000000001, 12544), (0.55, 14291), (0.5, 16358)] arg_min reach at : 1.6500000000000001 with value = 961 arg_min : 1.6500000000000001 min_score : 961{-90.0: 906, -85.0: 871, -80.0: 858, -75.0: 866, -70.0: 852, -65.0: 864, -60.0: 855, -55.0: 853, -50.0: 961, -45.0: 1180, -40.0: 1412, -35.0: 1690, -30.0: 1959, -25.0: 2243, -20.0: 2484, -15.0: 2677, -10.0: 2820, -5.0: 2976, 0.0: 3039, 5.0: 3049, 10.0: 3098, 15.0: 3122, 20.0: 3061, 25.0: 2954, 30.0: 2845, 35.0: 2631, 40.0: 2417, 45.0: 2130, 50.0: 1863, 55.0: 1564, 60.0: 1439, 65.0: 1326, 70.0: 1220, 75.0: 1142, 80.0: 1056, 85.0: 986} [(-70.0, 852), (-55.0, 853), (-60.0, 855), (-80.0, 858), (-65.0, 864), (-75.0, 866), (-85.0, 871), (-90.0, 906), (-50.0, 961), (85.0, 986), (80.0, 1056), (75.0, 1142), (-45.0, 1180), (70.0, 1220), (65.0, 1326), (-40.0, 1412), (60.0, 1439), (55.0, 1564), (-35.0, 1690), (50.0, 1863), (-30.0, 1959), (45.0, 2130), (-25.0, 2243), (40.0, 2417), (-20.0, 2484), (35.0, 2631), (-15.0, 2677), (-10.0, 2820), (30.0, 2845), (25.0, 2954), (-5.0, 2976), (0.0, 3039), (5.0, 3049), (20.0, 3061), (10.0, 3098), (15.0, 3122)] arg_min reach at : -70.0 with value = 852 arg_min : -70.0 min_score : 852{12.75: 4966, 14.34375: 4695, 15.9375: 4364, 17.53125: 3945, 19.125: 3465, 20.71875: 2958, 22.3125: 2415, 23.90625: 1841, 25.5: 1295, 27.09375: 936, 28.6875: 847, 30.28125: 852, 31.875: 883, 33.46875: 997, 35.0625: 1322, 36.65625: 1859} [(28.6875, 847), (30.28125, 852), (31.875, 883), (27.09375, 936), (33.46875, 997), (25.5, 1295), (35.0625, 1322), (23.90625, 1841), (36.65625, 1859), (22.3125, 2415), (20.71875, 2958), (19.125, 3465), (17.53125, 3945), (15.9375, 4364), (14.34375, 4695), (12.75, 4966)] arg_min reach at : 28.6875 with value = 847 arg_min : 28.6875 min_score : 847{13.5: 1489, 15.1875: 1374, 16.875: 1244, 18.5625: 1126, 20.25: 996, 21.9375: 893, 23.625: 847, 25.3125: 933, 27.0: 1153, 28.6875: 1446, 30.375: 1827, 32.0625: 2217, 33.75: 2658, 35.4375: 3137, 37.125: 3653, 38.8125: 4142} [(23.625, 847), (21.9375, 893), (25.3125, 933), (20.25, 996), (18.5625, 1126), (27.0, 1153), (16.875, 1244), (15.1875, 1374), (28.6875, 1446), (13.5, 1489), (30.375, 1827), (32.0625, 2217), (33.75, 2658), (35.4375, 3137), (37.125, 3653), (38.8125, 4142)] arg_min reach at : 23.625 with value = 847 arg_min : 23.625 min_score : 847{13.5: 1870, 15.1875: 1783, 16.875: 1676, 18.5625: 1564, 20.25: 1442, 21.9375: 1302, 23.625: 1153, 25.3125: 997, 27.0: 847, 28.6875: 864, 30.375: 1163, 32.0625: 1613, 33.75: 2300, 35.4375: 3191, 37.125: 4251, 38.8125: 5356} [(27.0, 847), (28.6875, 864), (25.3125, 997), (23.625, 1153), (30.375, 1163), (21.9375, 1302), (20.25, 1442), (18.5625, 1564), (32.0625, 1613), (16.875, 1676), (15.1875, 1783), (13.5, 1870), (33.75, 2300), (35.4375, 3191), (37.125, 4251), (38.8125, 5356)] arg_min reach at : 27.0 with value = 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 x0 : 103 y1 : 396 width : 35, height : 38, area : 1330, score : 1.0 x0 : 93 y1 : 396 width : 51, height : 36, area : 1836, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! (57, 52) (52, 43) [57, 52] (57, 52) score : 7970 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [10.75, 32.25, 1.34375]), ('yc', [13.0, 39.0, 1.625]), ('radius', [13.0, 39.0, 1.625])] {0.5: 16167, 0.55: 14430, 0.6000000000000001: 12864, 0.65: 11529, 0.7000000000000001: 10346, 0.75: 9256, 0.8: 8418, 0.8500000000000001: 7609, 0.9: 6862, 0.9500000000000001: 6277, 1.0: 5482, 1.05: 4840, 1.1: 4264, 1.1500000000000001: 3773, 1.2000000000000002: 3275, 1.25: 2948, 1.3: 2727, 1.35: 2514, 1.4000000000000001: 2254, 1.4500000000000002: 2155, 1.5: 1986, 1.55: 1892, 1.6: 1846, 1.6500000000000001: 1782, 1.7000000000000002: 1717, 1.75: 1656, 1.8: 1641, 1.85: 1602, 1.9000000000000001: 1587, 1.9500000000000002: 1576} [(1.9500000000000002, 1576), (1.9000000000000001, 1587), (1.85, 1602), (1.8, 1641), (1.75, 1656), (1.7000000000000002, 1717), (1.6500000000000001, 1782), (1.6, 1846), (1.55, 1892), (1.5, 1986), (1.4500000000000002, 2155), (1.4000000000000001, 2254), (1.35, 2514), (1.3, 2727), (1.25, 2948), (1.2000000000000002, 3275), (1.1500000000000001, 3773), (1.1, 4264), (1.05, 4840), (1.0, 5482), (0.9500000000000001, 6277), (0.9, 6862), (0.8500000000000001, 7609), (0.8, 8418), (0.75, 9256), (0.7000000000000001, 10346), (0.65, 11529), (0.6000000000000001, 12864), (0.55, 14430), (0.5, 16167)] arg_min reach at : 1.9500000000000002 with value = 1576 | arg_min : 1.9500000000000002 min_score : 1576{-90.0: 2291, -85.0: 2369, -80.0: 2390, -75.0: 2401, -70.0: 2341, -65.0: 2390, -60.0: 2418, -55.0: 2445, -50.0: 2599, -45.0: 2701, -40.0: 2755, -35.0: 2787, -30.0: 2753, -25.0: 2678, -20.0: 2611, -15.0: 2478, -10.0: 2313, -5.0: 2098, 0.0: 1870, 5.0: 1702, 10.0: 1576, 15.0: 1422, 20.0: 1258, 25.0: 1061, 30.0: 872, 35.0: 752, 40.0: 632, 45.0: 677, 50.0: 872, 55.0: 1070, 60.0: 1274, 65.0: 1477, 70.0: 1637, 75.0: 1895, 80.0: 2049, 85.0: 2193} [(40.0, 632), (45.0, 677), (35.0, 752), (30.0, 872), (50.0, 872), (25.0, 1061), (55.0, 1070), (20.0, 1258), (60.0, 1274), (15.0, 1422), (65.0, 1477), (10.0, 1576), (70.0, 1637), (5.0, 1702), (0.0, 1870), (75.0, 1895), (80.0, 2049), (-5.0, 2098), (85.0, 2193), (-90.0, 2291), (-10.0, 2313), (-70.0, 2341), (-85.0, 2369), (-80.0, 2390), (-65.0, 2390), (-75.0, 2401), (-60.0, 2418), (-55.0, 2445), (-15.0, 2478), (-50.0, 2599), (-20.0, 2611), (-25.0, 2678), (-45.0, 2701), (-30.0, 2753), (-40.0, 2755), (-35.0, 2787)] arg_min reach at : 40.0 with value = 632 | arg_min : 40.0 min_score : 632{10.75: 982, 12.09375: 841, 13.4375: 751, 14.78125: 672, 16.125: 624, 17.46875: 599, 18.8125: 575, 20.15625: 561, 21.5: 632, 22.84375: 895, 24.1875: 1257, 25.53125: 1708, 26.875: 2192, 28.21875: 2686, 29.5625: 3202, 30.90625: 3701} [(20.15625, 561), (18.8125, 575), (17.46875, 599), (16.125, 624), (21.5, 632), (14.78125, 672), (13.4375, 751), (12.09375, 841), (22.84375, 895), (10.75, 982), (24.1875, 1257), (25.53125, 1708), (26.875, 2192), (28.21875, 2686), (29.5625, 3202), (30.90625, 3701)] arg_min reach at : 20.15625 with value = 561 | arg_min : 20.15625 min_score : 561{13.0: 3254, 14.625: 2871, 16.25: 2436, 17.875: 1977, 19.5: 1474, 21.125: 1071, 22.75: 835, 24.375: 629, 26.0: 561, 27.625: 716, 29.25: 937, 30.875: 1286, 32.5: 1738, 34.125: 2226, 35.75: 2782, 37.375: 3291} [(26.0, 561), (24.375, 629), (27.625, 716), (22.75, 835), (29.25, 937), (21.125, 1071), (30.875, 1286), (19.5, 1474), (32.5, 1738), (17.875, 1977), (34.125, 2226), (16.25, 2436), (35.75, 2782), (14.625, 2871), (13.0, 3254), (37.375, 3291)] arg_min reach at : 26.0 with value = 561 | arg_min : 26.0 min_score : 561{13.0: 1371, 14.625: 1301, 16.25: 1217, 17.875: 1127, 19.5: 1028, 21.125: 926, 22.75: 811, 24.375: 683, 26.0: 561, 27.625: 624, 29.25: 1026, 30.875: 1508, 32.5: 2147, 34.125: 2981, 35.75: 3949, 37.375: 5069} [(26.0, 561), (27.625, 624), (24.375, 683), (22.75, 811), (21.125, 926), (29.25, 1026), (19.5, 1028), (17.875, 1127), (16.25, 1217), (14.625, 1301), (13.0, 1371), (30.875, 1508), (32.5, 2147), (34.125, 2981), (35.75, 3949), (37.375, 5069)] arg_min reach at : 26.0 with value = 561 | arg_min : 26.0 min_score : 561{0.5: 13327, 0.55: 12420, 0.6000000000000001: 11540, 0.65: 10560, 0.7000000000000001: 9652, 0.75: 8782, 0.8: 7834, 0.8500000000000001: 7061, 0.9: 6294, 0.9500000000000001: 5633, 1.0: 4916, 1.05: 4424, 1.1: 3865, 1.1500000000000001: 3323, 1.2000000000000002: 2817, 1.25: 2401, 1.3: 1935, 1.35: 1600, 1.4000000000000001: 1218, 1.4500000000000002: 1019, 1.5: 834, 1.55: 693, 1.6: 614, 1.6500000000000001: 568, 1.7000000000000002: 549, 1.75: 529, 1.8: 520, 1.85: 521, 1.9000000000000001: 534, 1.9500000000000002: 561} [(1.8, 520), (1.85, 521), (1.75, 529), (1.9000000000000001, 534), (1.7000000000000002, 549), (1.9500000000000002, 561), (1.6500000000000001, 568), (1.6, 614), (1.55, 693), (1.5, 834), (1.4500000000000002, 1019), (1.4000000000000001, 1218), (1.35, 1600), (1.3, 1935), (1.25, 2401), (1.2000000000000002, 2817), (1.1500000000000001, 3323), (1.1, 3865), (1.05, 4424), (1.0, 4916), (0.9500000000000001, 5633), (0.9, 6294), (0.8500000000000001, 7061), (0.8, 7834), (0.75, 8782), (0.7000000000000001, 9652), (0.65, 10560), (0.6000000000000001, 11540), (0.55, 12420), (0.5, 13327)] arg_min reach at : 1.8 with value = 520 arg_min : 1.8 min_score : 520{-90.0: 2114, -85.0: 2268, -80.0: 2343, -75.0: 2405, -70.0: 2409, -65.0: 2435, -60.0: 2467, -55.0: 2514, -50.0: 2590, -45.0: 2654, -40.0: 2632, -35.0: 2610, -30.0: 2565, -25.0: 2526, -20.0: 2435, -15.0: 2330, -10.0: 2181, -5.0: 2013, 0.0: 1792, 5.0: 1584, 10.0: 1389, 15.0: 1208, 20.0: 1038, 25.0: 843, 30.0: 673, 35.0: 564, 40.0: 520, 45.0: 597, 50.0: 764, 55.0: 963, 60.0: 1158, 65.0: 1390, 70.0: 1573, 75.0: 1800, 80.0: 1969, 85.0: 2070} [(40.0, 520), (35.0, 564), (45.0, 597), (30.0, 673), (50.0, 764), (25.0, 843), (55.0, 963), (20.0, 1038), (60.0, 1158), (15.0, 1208), (10.0, 1389), (65.0, 1390), (70.0, 1573), (5.0, 1584), (0.0, 1792), (75.0, 1800), (80.0, 1969), (-5.0, 2013), (85.0, 2070), (-90.0, 2114), (-10.0, 2181), (-85.0, 2268), (-15.0, 2330), (-80.0, 2343), (-75.0, 2405), (-70.0, 2409), (-65.0, 2435), (-20.0, 2435), (-60.0, 2467), (-55.0, 2514), (-25.0, 2526), (-30.0, 2565), (-50.0, 2590), (-35.0, 2610), (-40.0, 2632), (-45.0, 2654)] arg_min reach at : 40.0 with value = 520 arg_min : 40.0 min_score : 520{10.75: 1004, 12.09375: 902, 13.4375: 775, 14.78125: 655, 16.125: 580, 17.46875: 515, 18.8125: 494, 20.15625: 520, 21.5: 692, 22.84375: 1028, 24.1875: 1466, 25.53125: 2001, 26.875: 2490, 28.21875: 3027, 29.5625: 3629, 30.90625: 4163} [(18.8125, 494), (17.46875, 515), (20.15625, 520), (16.125, 580), (14.78125, 655), (21.5, 692), (13.4375, 775), (12.09375, 902), (10.75, 1004), (22.84375, 1028), (24.1875, 1466), (25.53125, 2001), (26.875, 2490), (28.21875, 3027), (29.5625, 3629), (30.90625, 4163)] arg_min reach at : 18.8125 with value = 494 arg_min : 18.8125 min_score : 494{13.0: 3121, 14.625: 2763, 16.25: 2397, 17.875: 1962, 19.5: 1409, 21.125: 1056, 22.75: 809, 24.375: 589, 26.0: 494, 27.625: 635, 29.25: 987, 30.875: 1406, 32.5: 1937, 34.125: 2442, 35.75: 2987, 37.375: 3537} [(26.0, 494), (24.375, 589), (27.625, 635), (22.75, 809), (29.25, 987), (21.125, 1056), (30.875, 1406), (19.5, 1409), (32.5, 1937), (17.875, 1962), (16.25, 2397), (34.125, 2442), (14.625, 2763), (35.75, 2987), (13.0, 3121), (37.375, 3537)] arg_min reach at : 26.0 with value = 494 arg_min : 26.0 min_score : 494{13.0: 1347, 14.625: 1266, 16.25: 1182, 17.875: 1086, 19.5: 979, 21.125: 865, 22.75: 737, 24.375: 616, 26.0: 494, 27.625: 559, 29.25: 954, 30.875: 1524, 32.5: 2347, 34.125: 3350, 35.75: 4468, 37.375: 5635} [(26.0, 494), (27.625, 559), (24.375, 616), (22.75, 737), (21.125, 865), (29.25, 954), (19.5, 979), (17.875, 1086), (16.25, 1182), (14.625, 1266), (13.0, 1347), (30.875, 1524), (32.5, 2347), (34.125, 3350), (35.75, 4468), (37.375, 5635)] arg_min reach at : 26.0 with value = 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 x0 : 104 y1 : 292 width : 27, height : 36, area : 972, score : 1.0 x0 : 102 y1 : 288 width : 39, height : 43, area : 1677, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1742632835_394949_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 : 21607072 Result OK ! uploaded one batch 0 Elapsed time : 18.633061170578003 After datou_step_exec type output : time spend for datou_step_exec : 24.383371591567993 time spend to save output : 2.3126602172851562e-05 total time spend for step 1 : 24.383394718170166 step2:tile Sat Mar 22 09:41:00 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 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 : ['temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'] 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 input_args_next_step, len :1, first value : [('temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg',)] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1346373456, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1346373457, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1346373458, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1346373459} map_photo_id_path_extension : {937852786: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1346373456: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1346373457: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1346373458: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1346373459: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}} map_subphoto_mainphoto : {1346373456: 937852786, 1346373457: 937852786, 1346373458: 937852786, 1346373459: 937852786} verbose : True 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/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786,937852786) and `type` in (520) Loaded 4 chid ids of type : 520 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3724020957,3724020958,3724020965,3724020966) ++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3724020957,3724020958,3724020965,3724020966) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3724020957,3724020958,3724020965,3724020966) https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 21607074 with name tile_taggage_varroa feed_id_new_photos : 21607074 filename : temp/1742632835_394949_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)] calcul des nouveaux crops pour le tile x0:0,x1:480,y0:0,y1:480 calcul avec la methode originale calcul avec la methode originale calcul avec la methode originale calcul avec la methode originale chi selectionnes : [, , , ] new_crops_tiles : 1 crop_transformed : 4 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [(937852786, 2090988864, 17, 0, 480, 0, 480, 1.0)] list_photo_ids_cropped : [937852786] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786) and `type` in (17) Loaded 1 chid ids of type : 17 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165084) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165084) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165084) treat the image : temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.009839057922363281 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/1742632867_394949 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (21607074, 1346373495, 0, NOW()) 1 we have uploaded 1 photos in the portfolio 21607074 Importing ! upload mediasElapsed time : 0.47601819038391113 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165084, 1346373495, 0)] Saving 4 CHIs. list_chi_tile : [": {'photo_id': 1346373495, 'hashtag_id': 2087736828, 'type': 521, 'x0': 432, 'x1': 467, 'y0': 303, 'y1': 355, 'score': 1.0, 'id': 0, 'points': ['463,352,452,353,439,350,426,342,418,333,417,325,422,319,433,318,446,321,459,328,467,338,469,346', '463,352,452,353,439,350,426,342,418,333,417,325,422,319,433,318,446,321,459,328,467,338,469,346'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1346373495, 'hashtag_id': 2087736828, 'type': 521, 'x0': 419, 'x1': 445, 'y0': 434, 'y1': 480, 'score': 1.0, 'id': 0, 'points': ['451,461,449,467,441,473,429,477,416,477,406,473,402,467,403,461,411,455,423,451,435,452,446,455', '451,461,449,467,441,473,429,477,416,477,406,473,402,467,403,461,411,455,423,451,435,452,446,455'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1346373495, 'hashtag_id': 2087736828, 'type': 521, 'x0': 93, 'x1': 144, 'y0': 360, 'y1': 396, 'score': 1.0, 'id': 0, 'points': ['112,359,120,350,130,348,137,351,141,361,140,374,134,387,126,396,117,399,109,395,105,385,106,372', '112,359,120,350,130,348,137,351,141,361,140,374,134,387,126,396,117,399,109,395,105,385,106,372'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1346373495, 'hashtag_id': 2087736828, 'type': 521, 'x0': 102, 'x1': 141, 'y0': 245, 'y1': 288, 'score': 1.0, 'id': 0, 'points': ['124,293,112,290,102,281,95,271,93,262,96,255,105,254,116,257,127,266,134,276,136,285,132,292', '124,293,112,290,102,281,95,271,93,262,96,255,105,254,116,257,127,266,134,276,136,285,132,292'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}"] insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 4 chid ids of type : 521 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 0 INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! end of tileElapsed time : 0.5472900867462158 map_pid_results : {'1346373495': ['temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} After datou_step_exec type output : time spend for datou_step_exec : 7.156207084655762 time spend to save output : 5.245208740234375e-05 total time spend for step 2 : 7.156259536743164 step3:rotate Sat Mar 22 09:41:07 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 complete output_args for input 0 : {'1346373495': ['temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} input_args_next_step : {'1346373495': ()} output_args : {'1346373495': ['temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} args : 1346373495 depend.output_id : 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 input_args_next_step, len :1, first value : ('temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1346373456, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1346373457, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1346373458, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1346373459, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg': 1346373495} map_photo_id_path_extension : {937852786: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1346373456: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1346373457: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1346373458: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1346373459: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}, 1346373495: {'path': 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg'}} map_subphoto_mainphoto : {1346373456: 937852786, 1346373457: 937852786, 1346373458: 937852786, 1346373459: 937852786, 1346373495: 937852786} 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 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1346373495) and `type` in (521) Loaded 4 chid ids of type : 521 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3724021023,3724021024,3724021022,3724021021) ++WARNING : duplicated polygon, we should remove this data for chi_id : 3724021021. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3724021022. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3724021023. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3724021024. Ignored now SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3724021023,3724021024,3724021022,3724021021) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3724021023,3724021024,3724021022,3724021021) map_chi : {1346373495: [, , , ]} https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 21607075 photo_id in download_rotate_and_save : 1346373495 list_chi_loc : 4 Use all angle ! Rotation of photo 1346373495 of 0 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 0 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00048661231994628906 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0026090145111083984 .time for calcul the mask position with numpy : 0.00047016143798828125 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002132892608642578 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -1, 'hashtag_id': 2087736828, 'type': 529, 'x0': 25, 'x1': 61, 'y0': 268, 'y1': 319, 'score': 1.0, 'id': None, 'points': ['32,279,40,270,50,268,57,271,61,281,60,294,54,307,46,316,37,319,29,315,25,305,26,292'], 'sub_photo_id': 0, 'rles': [(-1, 48, 268, 4), (-1, 43, 269, 11), (-1, 40, 270, 16), (-1, 39, 271, 19), (-1, 38, 272, 20), (-1, 37, 273, 22), (-1, 36, 274, 23), (-1, 36, 275, 24), (-1, 35, 276, 25), (-1, 34, 277, 26), (-1, 33, 278, 28), (-1, 32, 279, 29), (-1, 32, 280, 30), (-1, 31, 281, 31), (-1, 31, 282, 31), (-1, 30, 283, 32), (-1, 30, 284, 32), (-1, 29, 285, 33), (-1, 29, 286, 33), (-1, 28, 287, 34), (-1, 28, 288, 33), (-1, 27, 289, 34), (-1, 27, 290, 34), (-1, 26, 291, 35), (-1, 26, 292, 35), (-1, 26, 293, 35), (-1, 26, 294, 35), (-1, 26, 295, 35), (-1, 26, 296, 34), (-1, 26, 297, 34), (-1, 26, 298, 33), (-1, 25, 299, 34), (-1, 25, 300, 33), (-1, 25, 301, 33), (-1, 25, 302, 32), (-1, 25, 303, 32), (-1, 25, 304, 31), (-1, 25, 305, 31), (-1, 25, 306, 30), (-1, 26, 307, 29), (-1, 26, 308, 28), (-1, 27, 309, 26), (-1, 27, 310, 25), (-1, 27, 311, 24), (-1, 28, 312, 23), (-1, 28, 313, 22), (-1, 29, 314, 20), (-1, 29, 315, 19), (-1, 31, 316, 16), (-1, 33, 317, 12), (-1, 35, 318, 7), (-1, 37, 319, 2)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -1, 'hashtag_id': 2087736828, 'type': 529, 'x0': 13, 'x1': 56, 'y0': 174, 'y1': 213, 'score': 1.0, 'id': None, 'points': ['44,213,32,210,22,201,15,191,13,182,16,175,25,174,36,177,47,186,54,196,56,205,52,212'], 'sub_photo_id': 0, 'rles': [(-1, 21, 174, 6), (-1, 16, 175, 15), (-1, 16, 176, 19), (-1, 15, 177, 22), (-1, 15, 178, 23), (-1, 14, 179, 26), (-1, 14, 180, 27), (-1, 13, 181, 29), (-1, 13, 182, 30), (-1, 13, 183, 31), (-1, 13, 184, 33), (-1, 14, 185, 33), (-1, 14, 186, 34), (-1, 14, 187, 35), (-1, 14, 188, 35), (-1, 15, 189, 35), (-1, 15, 190, 36), (-1, 15, 191, 36), (-1, 16, 192, 36), (-1, 16, 193, 37), (-1, 17, 194, 37), (-1, 18, 195, 36), (-1, 18, 196, 37), (-1, 19, 197, 36), (-1, 20, 198, 35), (-1, 21, 199, 35), (-1, 21, 200, 35), (-1, 22, 201, 34), (-1, 23, 202, 33), (-1, 24, 203, 33), (-1, 25, 204, 32), (-1, 26, 205, 31), (-1, 28, 206, 28), (-1, 29, 207, 27), (-1, 30, 208, 25), (-1, 31, 209, 24), (-1, 32, 210, 22), (-1, 35, 211, 19), (-1, 39, 212, 14), (-1, 43, 213, 6)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 15 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 15 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.96592583 0.25881905] [-0.25881905 0.96592583]] 15 [[ 0.96592583 0.25881905] [-0.25881905 0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.000396728515625 nb_pixel_total : 694 time to create 1 rle with old method : 0.001314401626586914 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004088878631591797 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002200603485107422 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -2, 'hashtag_id': 2087736828, 'type': 529, 'x0': 24, 'x1': 72, 'y0': 209, 'y1': 242, 'score': 1.0, 'id': None, 'points': ['62,241,50,242,38,236,28,228,24,220,25,212,34,209,45,209,58,215,67,222,72,231,70,238'], 'sub_photo_id': 0, 'rles': [(-1, 33, 209, 3), (-1, 37, 209, 9), (-1, 30, 210, 18), (-1, 49, 210, 1), (-1, 30, 211, 21), (-1, 26, 212, 27), (-1, 26, 213, 29), (-1, 26, 214, 32), (-1, 25, 215, 35), (-1, 25, 216, 36), (-1, 25, 217, 37), (-1, 25, 218, 38), (-1, 24, 219, 40), (-1, 25, 220, 40), (-1, 25, 221, 42), (-1, 25, 222, 43), (-1, 26, 223, 43), (-1, 27, 224, 42), (-1, 27, 225, 43), (-1, 28, 226, 43), (-1, 29, 227, 42), (-1, 29, 228, 42), (-1, 30, 229, 43), (-1, 30, 230, 43), (-1, 32, 231, 41), (-1, 33, 232, 39), (-1, 34, 233, 39), (-1, 36, 234, 36), (-1, 37, 235, 35), (-1, 38, 236, 34), (-1, 40, 237, 32), (-1, 42, 238, 30), (-1, 43, 239, 28), (-1, 46, 240, 22), (-1, 48, 241, 19), (-1, 50, 242, 15)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 30 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 30 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.8660254 0.5 ] [-0.5 0.8660254]] 30 [[ 0.8660254 0.5 ] [-0.5 0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004177093505859375 nb_pixel_total : 221 time to create 1 rle with old method : 0.0004343986511230469 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004107952117919922 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0021948814392089844 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -3, 'hashtag_id': 2087736828, 'type': 529, 'x0': 44, 'x1': 93, 'y0': 238, 'y1': 268, 'score': 1.0, 'id': None, 'points': ['86,264,74,268,61,265,50,260,44,253,43,245,50,240,61,237,75,239,87,245,93,251,93,259'], 'sub_photo_id': 0, 'rles': [(-1, 59, 238, 9), (-1, 55, 239, 17), (-1, 73, 239, 1), (-1, 51, 240, 27), (-1, 49, 241, 30), (-1, 48, 242, 34), (-1, 48, 243, 36), (-1, 46, 244, 40), (-1, 45, 245, 43), (-1, 44, 246, 45), (-1, 44, 247, 46), (-1, 44, 248, 47), (-1, 44, 249, 48), (-1, 44, 250, 49), (-1, 44, 251, 50), (-1, 44, 252, 50), (-1, 44, 253, 50), (-1, 45, 254, 49), (-1, 45, 255, 49), (-1, 47, 256, 47), (-1, 48, 257, 46), (-1, 48, 258, 46), (-1, 50, 259, 44), (-1, 51, 260, 43), (-1, 52, 261, 41), (-1, 54, 262, 37), (-1, 56, 263, 35), (-1, 59, 264, 31), (-1, 60, 265, 28), (-1, 63, 266, 21), (-1, 67, 267, 2), (-1, 70, 267, 11), (-1, 74, 268, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 45 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 45 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.70710678 0.70710678] [-0.70710678 0.70710678]] 45 [[ 0.70710678 0.70710678] [-0.70710678 0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00038933753967285156 nb_pixel_total : 143 time to create 1 rle with old method : 0.00029540061950683594 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00040721893310546875 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0022077560424804688 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -4, 'hashtag_id': 2087736828, 'type': 529, 'x0': 69, 'x1': 121, 'y0': 258, 'y1': 286, 'score': 1.0, 'id': None, 'points': ['115,279,105,285,91,286,79,284,72,279,69,272,74,265,84,259,98,258,110,260,118,265,120,273'], 'sub_photo_id': 0, 'rles': [(-1, 96, 258, 5), (-1, 88, 259, 17), (-1, 84, 260, 26), (-1, 111, 260, 1), (-1, 82, 261, 31), (-1, 81, 262, 34), (-1, 78, 263, 38), (-1, 77, 264, 42), (-1, 75, 265, 45), (-1, 74, 266, 46), (-1, 73, 267, 47), (-1, 72, 268, 48), (-1, 72, 269, 49), (-1, 71, 270, 50), (-1, 70, 271, 51), (-1, 69, 272, 53), (-1, 70, 273, 52), (-1, 70, 274, 51), (-1, 70, 275, 50), (-1, 71, 276, 48), (-1, 71, 277, 49), (-1, 72, 278, 47), (-1, 71, 279, 47), (-1, 72, 280, 45), (-1, 73, 281, 41), (-1, 76, 282, 37), (-1, 77, 283, 34), (-1, 79, 284, 31), (-1, 82, 285, 25), (-1, 85, 286, 21)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 60 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 60 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.5 0.8660254] [-0.8660254 0.5 ]] 60 [[ 0.5 0.8660254] [-0.8660254 0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00039887428283691406 nb_pixel_total : 414 time to create 1 rle with old method : 0.0007855892181396484 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00040149688720703125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0021810531616210938 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -5, 'hashtag_id': 2087736828, 'type': 529, 'x0': 102, 'x1': 152, 'y0': 269, 'y1': 300, 'score': 1.0, 'id': None, 'points': ['148,286,140,295,127,299,115,300,106,297,101,291,105,283,113,275,126,270,139,268,147,271,151,278'], 'sub_photo_id': 0, 'rles': [(-1, 132, 269, 1), (-1, 135, 269, 6), (-1, 126, 270, 18), (-1, 124, 271, 24), (-1, 121, 272, 28), (-1, 118, 273, 32), (-1, 116, 274, 34), (-1, 113, 275, 38), (-1, 112, 276, 39), (-1, 111, 277, 41), (-1, 111, 278, 42), (-1, 109, 279, 44), (-1, 108, 280, 44), (-1, 108, 281, 44), (-1, 106, 282, 45), (-1, 105, 283, 47), (-1, 105, 284, 46), (-1, 104, 285, 46), (-1, 104, 286, 46), (-1, 104, 287, 45), (-1, 104, 288, 44), (-1, 103, 289, 44), (-1, 103, 290, 43), (-1, 102, 291, 43), (-1, 102, 292, 42), (-1, 103, 293, 41), (-1, 104, 294, 38), (-1, 104, 295, 37), (-1, 105, 296, 33), (-1, 106, 297, 28), (-1, 107, 298, 27), (-1, 111, 299, 19), (-1, 112, 300, 1), (-1, 114, 300, 9)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 75 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 75 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.25881905 0.96592583] [-0.96592583 0.25881905]] 75 [[ 0.25881905 0.96592583] [-0.96592583 0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004105567932128906 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0022230148315429688 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004429817199707031 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002209186553955078 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003981590270996094 nb_pixel_total : 264 time to create 1 rle with old method : 0.0005390644073486328 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -6, 'hashtag_id': 2087736828, 'type': 529, 'x0': 138, 'x1': 183, 'y0': 271, 'y1': 308, 'score': 1.0, 'id': None, 'points': ['182,285,176,295,164,303,153,307,144,306,138,302,139,293,145,283,156,275,168,270,177,271,183,277'], 'sub_photo_id': 0, 'rles': [(-1, 168, 271, 10), (-1, 165, 272, 14), (-1, 162, 273, 18), (-1, 160, 274, 22), (-1, 157, 275, 25), (-1, 155, 276, 28), (-1, 154, 277, 30), (-1, 153, 278, 31), (-1, 152, 279, 32), (-1, 150, 280, 33), (-1, 148, 281, 36), (-1, 148, 282, 36), (-1, 146, 283, 38), (-1, 145, 284, 38), (-1, 144, 285, 39), (-1, 144, 286, 39), (-1, 143, 287, 39), (-1, 143, 288, 38), (-1, 142, 289, 39), (-1, 141, 290, 40), (-1, 141, 291, 38), (-1, 140, 292, 39), (-1, 140, 293, 39), (-1, 139, 294, 39), (-1, 139, 295, 38), (-1, 139, 296, 38), (-1, 139, 297, 36), (-1, 139, 298, 35), (-1, 139, 299, 33), (-1, 139, 300, 32), (-1, 138, 301, 32), (-1, 138, 302, 30), (-1, 139, 303, 27), (-1, 140, 304, 25), (-1, 142, 305, 19), (-1, 143, 306, 17), (-1, 143, 307, 14), (-1, 150, 308, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 90 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 90 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00042057037353515625 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0027446746826171875 .time for calcul the mask position with numpy : 0.00041103363037109375 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002232789993286133 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -7, 'hashtag_id': 2087736828, 'type': 529, 'x0': 268, 'x1': 319, 'y0': 258, 'y1': 294, 'score': 1.0, 'id': None, 'points': ['279,287,270,279,268,269,271,262,281,258,294,259,307,265,316,273,319,282,315,290,305,294,292,293'], 'sub_photo_id': 0, 'rles': [(-1, 280, 258, 8), (-1, 278, 259, 18), (-1, 275, 260, 23), (-1, 273, 261, 27), (-1, 271, 262, 31), (-1, 271, 263, 33), (-1, 270, 264, 36), (-1, 270, 265, 38), (-1, 269, 266, 40), (-1, 269, 267, 41), (-1, 268, 268, 43), (-1, 268, 269, 45), (-1, 268, 270, 46), (-1, 268, 271, 47), (-1, 269, 272, 47), (-1, 269, 273, 48), (-1, 269, 274, 48), (-1, 269, 275, 49), (-1, 269, 276, 49), (-1, 270, 277, 48), (-1, 270, 278, 49), (-1, 270, 279, 49), (-1, 271, 280, 48), (-1, 272, 281, 48), (-1, 273, 282, 47), (-1, 274, 283, 45), (-1, 276, 284, 43), (-1, 277, 285, 41), (-1, 278, 286, 40), (-1, 279, 287, 38), (-1, 281, 288, 36), (-1, 283, 289, 33), (-1, 285, 290, 31), (-1, 287, 291, 27), (-1, 289, 292, 23), (-1, 291, 293, 18), (-1, 299, 294, 8)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -7, 'hashtag_id': 2087736828, 'type': 529, 'x0': 174, 'x1': 213, 'y0': 263, 'y1': 306, 'score': 1.0, 'id': None, 'points': ['213,275,210,287,201,297,191,304,182,306,175,303,174,294,177,283,186,272,196,265,205,263,212,267'], 'sub_photo_id': 0, 'rles': [(-1, 203, 263, 3), (-1, 199, 264, 9), (-1, 196, 265, 14), (-1, 194, 266, 18), (-1, 193, 267, 20), (-1, 192, 268, 21), (-1, 190, 269, 23), (-1, 189, 270, 24), (-1, 187, 271, 27), (-1, 186, 272, 28), (-1, 185, 273, 29), (-1, 184, 274, 30), (-1, 184, 275, 30), (-1, 183, 276, 31), (-1, 182, 277, 31), (-1, 181, 278, 32), (-1, 180, 279, 33), (-1, 179, 280, 34), (-1, 179, 281, 33), (-1, 178, 282, 34), (-1, 177, 283, 35), (-1, 177, 284, 35), (-1, 176, 285, 35), (-1, 176, 286, 35), (-1, 176, 287, 35), (-1, 176, 288, 34), (-1, 175, 289, 34), (-1, 175, 290, 33), (-1, 175, 291, 32), (-1, 175, 292, 31), (-1, 174, 293, 32), (-1, 174, 294, 31), (-1, 174, 295, 30), (-1, 174, 296, 29), (-1, 174, 297, 28), (-1, 174, 298, 27), (-1, 175, 299, 24), (-1, 175, 300, 23), (-1, 175, 301, 22), (-1, 175, 302, 20), (-1, 175, 303, 19), (-1, 177, 304, 15), (-1, 179, 305, 10), (-1, 181, 306, 4)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 105 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 105 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.25881905 0.96592583] [-0.96592583 -0.25881905]] 105 [[-0.25881905 0.96592583] [-0.96592583 -0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00042510032653808594 nb_pixel_total : 694 time to create 1 rle with old method : 0.0013401508331298828 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004222393035888672 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002190113067626953 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -8, 'hashtag_id': 2087736828, 'type': 529, 'x0': 209, 'x1': 242, 'y0': 248, 'y1': 296, 'score': 1.0, 'id': None, 'points': ['241,257,242,269,236,281,228,291,220,295,212,294,209,285,209,274,215,261,222,252,231,247,238,249'], 'sub_photo_id': 0, 'rles': [(-1, 229, 248, 3), (-1, 233, 248, 1), (-1, 229, 249, 10), (-1, 226, 250, 14), (-1, 225, 251, 15), (-1, 223, 252, 17), (-1, 222, 253, 19), (-1, 221, 254, 21), (-1, 221, 255, 21), (-1, 220, 256, 23), (-1, 219, 257, 24), (-1, 218, 258, 25), (-1, 217, 259, 26), (-1, 216, 260, 27), (-1, 215, 261, 28), (-1, 215, 262, 28), (-1, 214, 263, 29), (-1, 214, 264, 29), (-1, 214, 265, 29), (-1, 213, 266, 30), (-1, 213, 267, 30), (-1, 212, 268, 31), (-1, 212, 269, 31), (-1, 211, 270, 32), (-1, 210, 271, 32), (-1, 211, 272, 31), (-1, 210, 273, 31), (-1, 210, 274, 31), (-1, 209, 275, 31), (-1, 209, 276, 31), (-1, 209, 277, 31), (-1, 209, 278, 30), (-1, 209, 279, 29), (-1, 209, 280, 29), (-1, 209, 281, 28), (-1, 209, 282, 28), (-1, 209, 283, 27), (-1, 210, 284, 25), (-1, 209, 285, 25), (-1, 209, 286, 25), (-1, 209, 287, 24), (-1, 210, 288, 22), (-1, 210, 289, 21), (-1, 210, 290, 21), (-1, 212, 291, 17), (-1, 212, 292, 15), (-1, 212, 293, 14), (-1, 212, 294, 12), (-1, 215, 295, 8), (-1, 219, 296, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 120 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 120 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.5 0.8660254] [-0.8660254 -0.5 ]] 120 [[-0.5 0.8660254] [-0.8660254 -0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00038743019104003906 nb_pixel_total : 221 time to create 1 rle with old method : 0.0004324913024902344 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00040841102600097656 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0021877288818359375 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -9, 'hashtag_id': 2087736828, 'type': 529, 'x0': 238, 'x1': 268, 'y0': 227, 'y1': 276, 'score': 1.0, 'id': None, 'points': ['264,233,268,245,265,258,260,269,253,275,245,276,240,269,237,258,239,244,245,232,251,226,259,226'], 'sub_photo_id': 0, 'rles': [(-1, 251, 227, 10), (-1, 250, 228, 12), (-1, 249, 229, 13), (-1, 248, 230, 16), (-1, 247, 231, 18), (-1, 246, 232, 19), (-1, 245, 233, 21), (-1, 245, 234, 21), (-1, 244, 235, 22), (-1, 244, 236, 22), (-1, 243, 237, 24), (-1, 243, 238, 24), (-1, 242, 239, 25), (-1, 242, 240, 26), (-1, 242, 241, 26), (-1, 241, 242, 27), (-1, 240, 243, 28), (-1, 240, 244, 29), (-1, 240, 245, 29), (-1, 240, 246, 29), (-1, 239, 247, 29), (-1, 240, 248, 28), (-1, 239, 249, 29), (-1, 239, 250, 29), (-1, 239, 251, 28), (-1, 239, 252, 29), (-1, 238, 253, 30), (-1, 238, 254, 29), (-1, 238, 255, 29), (-1, 238, 256, 29), (-1, 238, 257, 29), (-1, 238, 258, 28), (-1, 238, 259, 28), (-1, 238, 260, 28), (-1, 238, 261, 27), (-1, 239, 262, 25), (-1, 239, 263, 25), (-1, 239, 264, 25), (-1, 239, 265, 24), (-1, 240, 266, 23), (-1, 240, 267, 22), (-1, 240, 268, 22), (-1, 240, 269, 21), (-1, 241, 270, 19), (-1, 241, 271, 18), (-1, 242, 272, 17), (-1, 244, 273, 13), (-1, 244, 274, 12), (-1, 245, 275, 11), (-1, 246, 276, 8)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 135 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 135 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.70710678 0.70710678] [-0.70710678 -0.70710678]] 135 [[-0.70710678 0.70710678] [-0.70710678 -0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00039076805114746094 nb_pixel_total : 143 time to create 1 rle with old method : 0.00037097930908203125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004143714904785156 nb_pixel_total : 1160 time to create 1 rle with old method : 0.0021643638610839844 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -10, 'hashtag_id': 2087736828, 'type': 529, 'x0': 258, 'x1': 286, 'y0': 199, 'y1': 251, 'score': 1.0, 'id': None, 'points': ['279,204,285,214,286,228,284,240,279,247,272,250,265,245,259,235,258,221,260,209,265,201,273,199'], 'sub_photo_id': 0, 'rles': [(-1, 272, 199, 2), (-1, 269, 200, 6), (-1, 265, 201, 1), (-1, 267, 201, 9), (-1, 277, 201, 1), (-1, 264, 202, 15), (-1, 264, 203, 16), (-1, 264, 204, 17), (-1, 263, 205, 18), (-1, 262, 206, 19), (-1, 262, 207, 20), (-1, 261, 208, 22), (-1, 260, 209, 23), (-1, 261, 210, 23), (-1, 260, 211, 25), (-1, 260, 212, 25), (-1, 260, 213, 25), (-1, 260, 214, 26), (-1, 260, 215, 27), (-1, 259, 216, 28), (-1, 259, 217, 28), (-1, 259, 218, 28), (-1, 259, 219, 28), (-1, 258, 220, 29), (-1, 258, 221, 29), (-1, 258, 222, 29), (-1, 258, 223, 29), (-1, 258, 224, 29), (-1, 259, 225, 28), (-1, 259, 226, 28), (-1, 259, 227, 28), (-1, 259, 228, 28), (-1, 259, 229, 28), (-1, 259, 230, 28), (-1, 259, 231, 28), (-1, 259, 232, 28), (-1, 260, 233, 27), (-1, 260, 234, 27), (-1, 260, 235, 27), (-1, 260, 236, 26), (-1, 261, 237, 25), (-1, 261, 238, 25), (-1, 262, 239, 23), (-1, 263, 240, 22), (-1, 263, 241, 22), (-1, 263, 242, 21), (-1, 264, 243, 20), (-1, 265, 244, 18), (-1, 265, 245, 17), (-1, 266, 246, 16), (-1, 267, 247, 15), (-1, 268, 248, 13), (-1, 270, 249, 8), (-1, 279, 249, 1), (-1, 271, 250, 5), (-1, 272, 251, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 150 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 150 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.8660254 0.5 ] [-0.5 -0.8660254]] 150 [[-0.8660254 0.5 ] [-0.5 -0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003921985626220703 nb_pixel_total : 414 time to create 1 rle with old method : 0.0008192062377929688 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00041747093200683594 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0021696090698242188 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00038361549377441406 nb_pixel_total : 1 time to create 1 rle with old method : 2.574920654296875e-05 len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -11, 'hashtag_id': 2087736828, 'type': 529, 'x0': 269, 'x1': 300, 'y0': 168, 'y1': 218, 'score': 1.0, 'id': None, 'points': ['286,171,295,179,299,192,300,204,297,213,291,218,283,214,275,206,270,193,268,180,271,172,278,168'], 'sub_photo_id': 0, 'rles': [(-1, 278, 168, 2), (-1, 277, 169, 5), (-1, 283, 169, 1), (-1, 275, 170, 10), (-1, 273, 171, 14), (-1, 272, 172, 16), (-1, 271, 173, 18), (-1, 271, 174, 19), (-1, 271, 175, 20), (-1, 271, 176, 21), (-1, 270, 177, 24), (-1, 270, 178, 24), (-1, 270, 179, 25), (-1, 269, 180, 27), (-1, 269, 181, 27), (-1, 269, 182, 27), (-1, 269, 183, 28), (-1, 269, 184, 28), (-1, 269, 185, 28), (-1, 270, 186, 27), (-1, 270, 187, 29), (-1, 269, 188, 30), (-1, 270, 189, 29), (-1, 270, 190, 29), (-1, 270, 191, 30), (-1, 270, 192, 30), (-1, 270, 193, 30), (-1, 270, 194, 30), (-1, 271, 195, 29), (-1, 271, 196, 29), (-1, 272, 197, 28), (-1, 272, 198, 29), (-1, 272, 199, 29), (-1, 273, 200, 28), (-1, 273, 201, 28), (-1, 273, 202, 28), (-1, 274, 203, 27), (-1, 274, 204, 27), (-1, 275, 205, 26), (-1, 275, 206, 26), (-1, 275, 207, 25), (-1, 276, 208, 25), (-1, 277, 209, 23), (-1, 279, 210, 20), (-1, 279, 211, 20), (-1, 280, 212, 19), (-1, 282, 213, 17), (-1, 282, 214, 16), (-1, 283, 215, 14), (-1, 285, 216, 11), (-1, 289, 217, 5), (-1, 291, 218, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 165 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 165 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.96592583 0.25881905] [-0.25881905 -0.96592583]] 165 [[-0.96592583 0.25881905] [-0.25881905 -0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00041222572326660156 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0022780895233154297 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00040459632873535156 nb_pixel_total : 1158 time to create 1 rle with old method : 0.002187967300415039 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00039124488830566406 nb_pixel_total : 264 time to create 1 rle with old method : 0.0005247592926025391 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -12, 'hashtag_id': 2087736828, 'type': 529, 'x0': 271, 'x1': 308, 'y0': 137, 'y1': 182, 'score': 1.0, 'id': None, 'points': ['285,137,295,143,303,155,307,166,306,175,302,181,293,180,283,174,275,163,270,151,271,142,277,136'], 'sub_photo_id': 0, 'rles': [(-1, 276, 137, 4), (-1, 281, 137, 3), (-1, 276, 138, 11), (-1, 274, 139, 14), (-1, 274, 140, 17), (-1, 273, 141, 18), (-1, 272, 142, 22), (-1, 271, 143, 24), (-1, 271, 144, 26), (-1, 271, 145, 26), (-1, 271, 146, 27), (-1, 271, 147, 28), (-1, 271, 148, 28), (-1, 271, 149, 29), (-1, 271, 150, 30), (-1, 271, 151, 31), (-1, 271, 152, 31), (-1, 272, 153, 31), (-1, 272, 154, 31), (-1, 272, 155, 32), (-1, 273, 156, 32), (-1, 273, 157, 32), (-1, 273, 158, 32), (-1, 274, 159, 31), (-1, 274, 160, 32), (-1, 275, 161, 32), (-1, 275, 162, 32), (-1, 275, 163, 32), (-1, 276, 164, 32), (-1, 276, 165, 32), (-1, 277, 166, 31), (-1, 278, 167, 30), (-1, 279, 168, 29), (-1, 280, 169, 28), (-1, 280, 170, 29), (-1, 281, 171, 27), (-1, 281, 172, 27), (-1, 283, 173, 25), (-1, 283, 174, 25), (-1, 284, 175, 24), (-1, 285, 176, 23), (-1, 287, 177, 21), (-1, 289, 178, 17), (-1, 290, 179, 15), (-1, 292, 180, 13), (-1, 294, 181, 10), (-1, 301, 182, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 180 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 180 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00041604042053222656 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0024230480194091797 .time for calcul the mask position with numpy : 0.00048041343688964844 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0018007755279541016 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -13, 'hashtag_id': 2087736828, 'type': 529, 'x0': 258, 'x1': 294, 'y0': 1, 'y1': 52, 'score': 1.0, 'id': None, 'points': ['287,41,279,50,269,52,262,49,258,39,259,26,265,13,273,4,282,1,290,5,294,15,293,28'], 'sub_photo_id': 0, 'rles': [(-1, 281, 1, 2), (-1, 278, 2, 7), (-1, 275, 3, 12), (-1, 273, 4, 16), (-1, 272, 5, 19), (-1, 271, 6, 20), (-1, 270, 7, 22), (-1, 269, 8, 23), (-1, 269, 9, 24), (-1, 268, 10, 25), (-1, 267, 11, 26), (-1, 266, 12, 28), (-1, 265, 13, 29), (-1, 265, 14, 30), (-1, 264, 15, 31), (-1, 264, 16, 31), (-1, 263, 17, 32), (-1, 263, 18, 32), (-1, 262, 19, 33), (-1, 262, 20, 33), (-1, 261, 21, 34), (-1, 261, 22, 33), (-1, 260, 23, 34), (-1, 260, 24, 34), (-1, 259, 25, 35), (-1, 259, 26, 35), (-1, 259, 27, 35), (-1, 259, 28, 35), (-1, 259, 29, 35), (-1, 259, 30, 34), (-1, 259, 31, 34), (-1, 259, 32, 33), (-1, 258, 33, 34), (-1, 258, 34, 33), (-1, 258, 35, 33), (-1, 258, 36, 32), (-1, 258, 37, 32), (-1, 258, 38, 31), (-1, 258, 39, 31), (-1, 258, 40, 30), (-1, 259, 41, 29), (-1, 259, 42, 28), (-1, 260, 43, 26), (-1, 260, 44, 25), (-1, 260, 45, 24), (-1, 261, 46, 23), (-1, 261, 47, 22), (-1, 262, 48, 20), (-1, 262, 49, 19), (-1, 264, 50, 16), (-1, 266, 51, 11), (-1, 268, 52, 4)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -13, 'hashtag_id': 2087736828, 'type': 529, 'x0': 263, 'x1': 306, 'y0': 107, 'y1': 146, 'score': 1.0, 'id': None, 'points': ['275,107,287,110,297,119,304,129,306,138,303,145,294,146,283,143,272,134,265,124,263,115,267,108'], 'sub_photo_id': 0, 'rles': [(-1, 271, 107, 6), (-1, 267, 108, 14), (-1, 266, 109, 19), (-1, 266, 110, 22), (-1, 265, 111, 24), (-1, 265, 112, 25), (-1, 264, 113, 27), (-1, 264, 114, 28), (-1, 263, 115, 31), (-1, 263, 116, 32), (-1, 263, 117, 33), (-1, 264, 118, 33), (-1, 264, 119, 34), (-1, 264, 120, 35), (-1, 264, 121, 35), (-1, 265, 122, 35), (-1, 265, 123, 36), (-1, 265, 124, 37), (-1, 266, 125, 36), (-1, 266, 126, 37), (-1, 267, 127, 37), (-1, 268, 128, 36), (-1, 269, 129, 36), (-1, 269, 130, 36), (-1, 270, 131, 35), (-1, 271, 132, 35), (-1, 271, 133, 35), (-1, 272, 134, 34), (-1, 273, 135, 33), (-1, 274, 136, 33), (-1, 276, 137, 31), (-1, 277, 138, 30), (-1, 278, 139, 29), (-1, 279, 140, 27), (-1, 280, 141, 26), (-1, 282, 142, 23), (-1, 283, 143, 22), (-1, 285, 144, 19), (-1, 289, 145, 15), (-1, 293, 146, 6)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 195 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 195 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.96592583 -0.25881905] [ 0.25881905 -0.96592583]] 195 [[-0.96592583 -0.25881905] [ 0.25881905 -0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003676414489746094 nb_pixel_total : 727 time to create 1 rle with old method : 0.0010426044464111328 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038123130798339844 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0014803409576416016 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -14, 'hashtag_id': 2087736828, 'type': 529, 'x0': 248, 'x1': 296, 'y0': 78, 'y1': 111, 'score': 1.0, 'id': None, 'points': ['257,78,269,77,281,83,291,91,295,99,294,107,285,110,274,110,261,104,252,97,247,88,249,81'], 'sub_photo_id': 0, 'rles': [(-1, 256, 78, 15), (-1, 254, 79, 19), (-1, 253, 80, 22), (-1, 250, 81, 28), (-1, 249, 82, 30), (-1, 249, 83, 32), (-1, 249, 84, 34), (-1, 249, 85, 35), (-1, 249, 86, 36), (-1, 248, 87, 39), (-1, 249, 88, 39), (-1, 248, 89, 41), (-1, 248, 90, 43), (-1, 248, 91, 43), (-1, 250, 92, 42), (-1, 250, 93, 42), (-1, 250, 94, 43), (-1, 251, 95, 43), (-1, 252, 96, 42), (-1, 252, 97, 43), (-1, 253, 98, 43), (-1, 254, 99, 42), (-1, 256, 100, 40), (-1, 257, 101, 40), (-1, 258, 102, 38), (-1, 259, 103, 37), (-1, 260, 104, 36), (-1, 261, 105, 35), (-1, 263, 106, 32), (-1, 266, 107, 29), (-1, 268, 108, 27), (-1, 270, 109, 21), (-1, 271, 110, 1), (-1, 273, 110, 18), (-1, 275, 111, 9), (-1, 285, 111, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 210 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 210 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.8660254 -0.5 ] [ 0.5 -0.8660254]] 210 [[-0.8660254 -0.5 ] [ 0.5 -0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003402233123779297 nb_pixel_total : 250 time to create 1 rle with old method : 0.0004284381866455078 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035381317138671875 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014414787292480469 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -15, 'hashtag_id': 2087736828, 'type': 529, 'x0': 227, 'x1': 276, 'y0': 52, 'y1': 82, 'score': 1.0, 'id': None, 'points': ['233,55,245,51,258,54,269,59,275,66,276,74,269,79,258,82,244,80,232,74,226,68,226,60'], 'sub_photo_id': 0, 'rles': [(-1, 244, 52, 3), (-1, 240, 53, 11), (-1, 252, 53, 2), (-1, 237, 54, 21), (-1, 233, 55, 28), (-1, 231, 56, 31), (-1, 230, 57, 35), (-1, 230, 58, 37), (-1, 228, 59, 41), (-1, 227, 60, 43), (-1, 227, 61, 44), (-1, 227, 62, 46), (-1, 227, 63, 46), (-1, 227, 64, 47), (-1, 227, 65, 49), (-1, 227, 66, 49), (-1, 227, 67, 50), (-1, 227, 68, 50), (-1, 227, 69, 50), (-1, 228, 70, 49), (-1, 229, 71, 48), (-1, 230, 72, 47), (-1, 231, 73, 46), (-1, 232, 74, 45), (-1, 233, 75, 43), (-1, 235, 76, 40), (-1, 237, 77, 36), (-1, 239, 78, 34), (-1, 242, 79, 30), (-1, 243, 80, 27), (-1, 247, 81, 1), (-1, 249, 81, 17), (-1, 253, 82, 9)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 225 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 225 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.70710678 -0.70710678] [ 0.70710678 -0.70710678]] 225 [[-0.70710678 -0.70710678] [ 0.70710678 -0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003631114959716797 nb_pixel_total : 169 time to create 1 rle with old method : 0.0003268718719482422 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003559589385986328 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0015256404876708984 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -16, 'hashtag_id': 2087736828, 'type': 529, 'x0': 199, 'x1': 251, 'y0': 34, 'y1': 62, 'score': 1.0, 'id': None, 'points': ['204,40,214,34,228,33,240,35,247,40,250,47,245,54,235,60,221,61,209,59,201,54,199,46'], 'sub_photo_id': 0, 'rles': [(-1, 215, 34, 21), (-1, 214, 35, 25), (-1, 211, 36, 31), (-1, 210, 37, 34), (-1, 208, 38, 37), (-1, 207, 39, 41), (-1, 204, 40, 45), (-1, 203, 41, 47), (-1, 202, 42, 47), (-1, 201, 43, 49), (-1, 202, 44, 48), (-1, 201, 45, 50), (-1, 200, 46, 51), (-1, 199, 47, 52), (-1, 199, 48, 53), (-1, 200, 49, 51), (-1, 200, 50, 50), (-1, 200, 51, 49), (-1, 201, 52, 48), (-1, 201, 53, 47), (-1, 201, 54, 46), (-1, 201, 55, 45), (-1, 202, 56, 42), (-1, 205, 57, 38), (-1, 206, 58, 34), (-1, 208, 59, 31), (-1, 209, 60, 1), (-1, 211, 60, 26), (-1, 216, 61, 17), (-1, 220, 62, 5)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 240 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 240 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.5 -0.8660254] [ 0.8660254 -0.5 ]] 240 [[-0.5 -0.8660254] [ 0.8660254 -0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00033783912658691406 nb_pixel_total : 450 time to create 1 rle with old method : 0.000682830810546875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035500526428222656 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0015041828155517578 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003330707550048828 nb_pixel_total : 1 time to create 1 rle with old method : 4.7206878662109375e-05 len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -17, 'hashtag_id': 2087736828, 'type': 529, 'x0': 168, 'x1': 218, 'y0': 20, 'y1': 51, 'score': 1.0, 'id': None, 'points': ['171,33,179,24,192,20,204,19,213,22,218,28,214,36,206,44,193,49,180,51,172,48,168,41'], 'sub_photo_id': 0, 'rles': [(-1, 198, 20, 9), (-1, 208, 20, 1), (-1, 191, 21, 19), (-1, 187, 22, 27), (-1, 187, 23, 28), (-1, 183, 24, 33), (-1, 180, 25, 37), (-1, 179, 26, 38), (-1, 177, 27, 41), (-1, 177, 28, 42), (-1, 176, 29, 43), (-1, 175, 30, 43), (-1, 174, 31, 44), (-1, 173, 32, 44), (-1, 172, 33, 45), (-1, 171, 34, 46), (-1, 171, 35, 46), (-1, 170, 36, 46), (-1, 169, 37, 47), (-1, 170, 38, 45), (-1, 169, 39, 44), (-1, 169, 40, 44), (-1, 168, 41, 44), (-1, 168, 42, 42), (-1, 169, 43, 41), (-1, 170, 44, 39), (-1, 170, 45, 38), (-1, 171, 46, 34), (-1, 171, 47, 32), (-1, 172, 48, 28), (-1, 173, 49, 24), (-1, 177, 50, 18), (-1, 180, 51, 6), (-1, 188, 51, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 255 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 255 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.25881905 -0.96592583] [ 0.96592583 -0.25881905]] 255 [[-0.25881905 -0.96592583] [ 0.96592583 -0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003566741943359375 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0016007423400878906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003552436828613281 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0014998912811279297 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00033736228942871094 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003972053527832031 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -18, 'hashtag_id': 2087736828, 'type': 529, 'x0': 137, 'x1': 182, 'y0': 12, 'y1': 49, 'score': 1.0, 'id': None, 'points': ['137,34,143,24,155,16,166,12,175,13,181,17,180,26,174,36,163,44,151,49,142,48,136,42'], 'sub_photo_id': 0, 'rles': [(-1, 170, 12, 1), (-1, 164, 13, 14), (-1, 161, 14, 17), (-1, 160, 15, 19), (-1, 156, 16, 25), (-1, 155, 17, 27), (-1, 153, 18, 30), (-1, 151, 19, 32), (-1, 150, 20, 32), (-1, 149, 21, 33), (-1, 147, 22, 35), (-1, 146, 23, 36), (-1, 144, 24, 38), (-1, 144, 25, 38), (-1, 143, 26, 39), (-1, 142, 27, 39), (-1, 142, 28, 39), (-1, 142, 29, 38), (-1, 140, 30, 40), (-1, 140, 31, 39), (-1, 140, 32, 38), (-1, 139, 33, 39), (-1, 138, 34, 39), (-1, 138, 35, 39), (-1, 138, 36, 38), (-1, 137, 37, 38), (-1, 137, 38, 36), (-1, 137, 39, 36), (-1, 138, 40, 33), (-1, 137, 41, 32), (-1, 137, 42, 31), (-1, 137, 43, 30), (-1, 137, 44, 29), (-1, 139, 45, 25), (-1, 139, 46, 22), (-1, 141, 47, 18), (-1, 142, 48, 14), (-1, 143, 49, 10)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 270 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 270 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003552436828613281 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0016949176788330078 .time for calcul the mask position with numpy : 0.0003566741943359375 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014345645904541016 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -19, 'hashtag_id': 2087736828, 'type': 529, 'x0': 1, 'x1': 52, 'y0': 26, 'y1': 62, 'score': 1.0, 'id': None, 'points': ['41,32,50,40,52,50,49,57,39,61,26,60,13,54,4,46,1,37,5,29,15,25,28,26'], 'sub_photo_id': 0, 'rles': [(-1, 14, 26, 8), (-1, 12, 27, 18), (-1, 9, 28, 23), (-1, 7, 29, 27), (-1, 5, 30, 31), (-1, 5, 31, 33), (-1, 4, 32, 36), (-1, 4, 33, 38), (-1, 3, 34, 40), (-1, 3, 35, 41), (-1, 2, 36, 43), (-1, 2, 37, 45), (-1, 1, 38, 47), (-1, 1, 39, 48), (-1, 2, 40, 48), (-1, 2, 41, 49), (-1, 2, 42, 49), (-1, 3, 43, 48), (-1, 3, 44, 49), (-1, 3, 45, 49), (-1, 4, 46, 48), (-1, 4, 47, 48), (-1, 5, 48, 47), (-1, 6, 49, 47), (-1, 7, 50, 46), (-1, 8, 51, 45), (-1, 10, 52, 43), (-1, 11, 53, 41), (-1, 12, 54, 40), (-1, 13, 55, 38), (-1, 15, 56, 36), (-1, 17, 57, 33), (-1, 19, 58, 31), (-1, 21, 59, 27), (-1, 23, 60, 23), (-1, 25, 61, 18), (-1, 33, 62, 8)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -19, 'hashtag_id': 2087736828, 'type': 529, 'x0': 107, 'x1': 146, 'y0': 14, 'y1': 57, 'score': 1.0, 'id': None, 'points': ['107,44,110,32,119,22,129,15,138,13,144,16,145,25,143,36,134,47,124,54,115,56,108,52'], 'sub_photo_id': 0, 'rles': [(-1, 136, 14, 4), (-1, 132, 15, 10), (-1, 129, 16, 15), (-1, 127, 17, 19), (-1, 126, 18, 20), (-1, 124, 19, 22), (-1, 123, 20, 23), (-1, 122, 21, 24), (-1, 120, 22, 27), (-1, 119, 23, 28), (-1, 118, 24, 29), (-1, 117, 25, 30), (-1, 116, 26, 31), (-1, 115, 27, 32), (-1, 115, 28, 31), (-1, 114, 29, 32), (-1, 113, 30, 33), (-1, 112, 31, 34), (-1, 111, 32, 34), (-1, 110, 33, 35), (-1, 110, 34, 35), (-1, 110, 35, 35), (-1, 109, 36, 35), (-1, 109, 37, 35), (-1, 109, 38, 34), (-1, 109, 39, 33), (-1, 108, 40, 34), (-1, 108, 41, 33), (-1, 108, 42, 32), (-1, 108, 43, 31), (-1, 107, 44, 31), (-1, 107, 45, 30), (-1, 107, 46, 30), (-1, 107, 47, 29), (-1, 107, 48, 28), (-1, 107, 49, 27), (-1, 108, 50, 24), (-1, 108, 51, 23), (-1, 108, 52, 21), (-1, 108, 53, 20), (-1, 109, 54, 18), (-1, 111, 55, 14), (-1, 113, 56, 9), (-1, 115, 57, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 285 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 285 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.25881905 -0.96592583] [ 0.96592583 0.25881905]] 285 [[ 0.25881905 -0.96592583] [ 0.96592583 0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00035262107849121094 nb_pixel_total : 727 time to create 1 rle with old method : 0.0009529590606689453 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003590583801269531 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0014920234680175781 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -20, 'hashtag_id': 2087736828, 'type': 529, 'x0': 78, 'x1': 111, 'y0': 24, 'y1': 72, 'score': 1.0, 'id': None, 'points': ['78,62,77,50,83,38,91,28,99,24,107,25,110,34,110,45,104,58,97,67,88,72,81,70'], 'sub_photo_id': 0, 'rles': [(-1, 101, 24, 1), (-1, 98, 25, 8), (-1, 97, 26, 12), (-1, 95, 27, 14), (-1, 94, 28, 15), (-1, 92, 29, 17), (-1, 90, 30, 21), (-1, 90, 31, 21), (-1, 89, 32, 22), (-1, 88, 33, 24), (-1, 87, 34, 25), (-1, 87, 35, 25), (-1, 86, 36, 25), (-1, 85, 37, 27), (-1, 84, 38, 28), (-1, 84, 39, 28), (-1, 83, 40, 29), (-1, 83, 41, 29), (-1, 82, 42, 30), (-1, 81, 43, 31), (-1, 81, 44, 31), (-1, 81, 45, 31), (-1, 80, 46, 31), (-1, 80, 47, 31), (-1, 79, 48, 31), (-1, 79, 49, 32), (-1, 78, 50, 32), (-1, 78, 51, 31), (-1, 78, 52, 31), (-1, 78, 53, 30), (-1, 78, 54, 30), (-1, 78, 55, 29), (-1, 78, 56, 29), (-1, 78, 57, 29), (-1, 78, 58, 28), (-1, 78, 59, 28), (-1, 78, 60, 27), (-1, 78, 61, 26), (-1, 78, 62, 25), (-1, 78, 63, 24), (-1, 78, 64, 23), (-1, 79, 65, 21), (-1, 79, 66, 21), (-1, 80, 67, 19), (-1, 81, 68, 17), (-1, 81, 69, 15), (-1, 81, 70, 14), (-1, 82, 71, 10), (-1, 87, 72, 1), (-1, 89, 72, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 300 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 300 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.5 -0.8660254] [ 0.8660254 0.5 ]] 300 [[ 0.5 -0.8660254] [ 0.8660254 0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003476142883300781 nb_pixel_total : 250 time to create 1 rle with old method : 0.00041675567626953125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003600120544433594 nb_pixel_total : 1155 time to create 1 rle with old method : 0.001847982406616211 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -21, 'hashtag_id': 2087736828, 'type': 529, 'x0': 52, 'x1': 82, 'y0': 44, 'y1': 93, 'score': 1.0, 'id': None, 'points': ['55,86,51,74,54,61,59,50,66,44,74,43,79,50,82,61,80,75,74,87,68,93,60,93'], 'sub_photo_id': 0, 'rles': [(-1, 67, 44, 8), (-1, 65, 45, 11), (-1, 65, 46, 12), (-1, 64, 47, 13), (-1, 62, 48, 17), (-1, 62, 49, 18), (-1, 61, 50, 19), (-1, 60, 51, 21), (-1, 59, 52, 22), (-1, 59, 53, 22), (-1, 58, 54, 23), (-1, 58, 55, 24), (-1, 57, 56, 25), (-1, 57, 57, 25), (-1, 57, 58, 25), (-1, 56, 59, 27), (-1, 55, 60, 28), (-1, 55, 61, 28), (-1, 55, 62, 28), (-1, 54, 63, 29), (-1, 54, 64, 29), (-1, 54, 65, 29), (-1, 54, 66, 29), (-1, 53, 67, 30), (-1, 53, 68, 29), (-1, 54, 69, 28), (-1, 53, 70, 29), (-1, 53, 71, 29), (-1, 53, 72, 28), (-1, 53, 73, 29), (-1, 52, 74, 29), (-1, 52, 75, 29), (-1, 52, 76, 29), (-1, 53, 77, 28), (-1, 53, 78, 27), (-1, 53, 79, 26), (-1, 53, 80, 26), (-1, 54, 81, 25), (-1, 54, 82, 24), (-1, 54, 83, 24), (-1, 55, 84, 22), (-1, 55, 85, 22), (-1, 55, 86, 21), (-1, 55, 87, 21), (-1, 56, 88, 19), (-1, 56, 89, 18), (-1, 57, 90, 16), (-1, 59, 91, 13), (-1, 59, 92, 12), (-1, 60, 93, 10)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 315 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 315 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]] 315 [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003402233123779297 nb_pixel_total : 169 time to create 1 rle with old method : 0.00029158592224121094 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036978721618652344 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0018877983093261719 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -22, 'hashtag_id': 2087736828, 'type': 529, 'x0': 34, 'x1': 62, 'y0': 69, 'y1': 121, 'score': 1.0, 'id': None, 'points': ['40,115,34,105,33,91,35,79,40,72,47,69,54,74,60,84,61,98,59,110,54,118,46,120'], 'sub_photo_id': 0, 'rles': [(-1, 48, 69, 1), (-1, 45, 70, 5), (-1, 41, 71, 1), (-1, 43, 71, 8), (-1, 40, 72, 13), (-1, 39, 73, 15), (-1, 39, 74, 16), (-1, 39, 75, 17), (-1, 38, 76, 18), (-1, 37, 77, 20), (-1, 37, 78, 21), (-1, 36, 79, 22), (-1, 36, 80, 22), (-1, 36, 81, 23), (-1, 35, 82, 25), (-1, 35, 83, 25), (-1, 35, 84, 26), (-1, 34, 85, 27), (-1, 34, 86, 27), (-1, 34, 87, 27), (-1, 34, 88, 28), (-1, 34, 89, 28), (-1, 34, 90, 28), (-1, 34, 91, 28), (-1, 34, 92, 28), (-1, 34, 93, 28), (-1, 34, 94, 28), (-1, 34, 95, 28), (-1, 34, 96, 29), (-1, 34, 97, 29), (-1, 34, 98, 29), (-1, 34, 99, 29), (-1, 34, 100, 29), (-1, 34, 101, 28), (-1, 34, 102, 28), (-1, 34, 103, 28), (-1, 34, 104, 28), (-1, 34, 105, 27), (-1, 35, 106, 26), (-1, 36, 107, 25), (-1, 36, 108, 25), (-1, 36, 109, 25), (-1, 37, 110, 23), (-1, 38, 111, 23), (-1, 38, 112, 22), (-1, 39, 113, 20), (-1, 40, 114, 19), (-1, 40, 115, 18), (-1, 40, 116, 17), (-1, 41, 117, 16), (-1, 42, 118, 15), (-1, 43, 119, 1), (-1, 45, 119, 11), (-1, 46, 120, 6), (-1, 47, 121, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 330 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 330 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.8660254 -0.5 ] [ 0.5 0.8660254]] 330 [[ 0.8660254 -0.5 ] [ 0.5 0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00034427642822265625 nb_pixel_total : 450 time to create 1 rle with old method : 0.0007653236389160156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003600120544433594 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0018815994262695312 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -23, 'hashtag_id': 2087736828, 'type': 529, 'x0': 20, 'x1': 51, 'y0': 102, 'y1': 152, 'score': 1.0, 'id': None, 'points': ['33,148,24,140,20,127,19,115,22,106,28,101,36,105,44,113,49,126,51,139,48,147,41,151'], 'sub_photo_id': 0, 'rles': [(-1, 28, 102, 2), (-1, 27, 103, 5), (-1, 25, 104, 11), (-1, 24, 105, 14), (-1, 23, 106, 16), (-1, 22, 107, 17), (-1, 22, 108, 19), (-1, 22, 109, 20), (-1, 22, 110, 20), (-1, 21, 111, 23), (-1, 20, 112, 25), (-1, 21, 113, 25), (-1, 20, 114, 26), (-1, 20, 115, 26), (-1, 20, 116, 27), (-1, 20, 117, 27), (-1, 20, 118, 28), (-1, 20, 119, 28), (-1, 20, 120, 28), (-1, 20, 121, 29), (-1, 20, 122, 29), (-1, 21, 123, 28), (-1, 21, 124, 29), (-1, 21, 125, 29), (-1, 21, 126, 30), (-1, 21, 127, 30), (-1, 21, 128, 30), (-1, 21, 129, 30), (-1, 22, 130, 29), (-1, 22, 131, 29), (-1, 22, 132, 30), (-1, 22, 133, 29), (-1, 24, 134, 27), (-1, 24, 135, 28), (-1, 24, 136, 28), (-1, 24, 137, 28), (-1, 25, 138, 27), (-1, 25, 139, 27), (-1, 25, 140, 27), (-1, 26, 141, 25), (-1, 27, 142, 24), (-1, 27, 143, 24), (-1, 29, 144, 21), (-1, 30, 145, 20), (-1, 31, 146, 19), (-1, 32, 147, 18), (-1, 33, 148, 16), (-1, 34, 149, 14), (-1, 36, 150, 10), (-1, 37, 151, 1), (-1, 39, 151, 5), (-1, 41, 152, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1346373495 of 345 degree temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 345 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.96592583 -0.25881905] [ 0.25881905 0.96592583]] 345 [[ 0.96592583 -0.25881905] [ 0.25881905 0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00036406517028808594 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0020067691802978516 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004439353942871094 nb_pixel_total : 1157 time to create 1 rle with old method : 0.018415212631225586 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00039505958557128906 nb_pixel_total : 234 time to create 1 rle with old method : 0.0005047321319580078 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -24, 'hashtag_id': 2087736828, 'type': 529, 'x0': 12, 'x1': 49, 'y0': 138, 'y1': 183, 'score': 1.0, 'id': None, 'points': ['34,182,24,176,16,164,12,153,13,144,17,138,26,139,36,145,44,156,49,168,48,177,42,183'], 'sub_photo_id': 0, 'rles': [(-1, 18, 138, 2), (-1, 17, 139, 10), (-1, 16, 140, 13), (-1, 16, 141, 15), (-1, 15, 142, 17), (-1, 13, 143, 21), (-1, 13, 144, 23), (-1, 13, 145, 24), (-1, 13, 146, 25), (-1, 13, 147, 25), (-1, 13, 148, 27), (-1, 13, 149, 27), (-1, 12, 150, 29), (-1, 13, 151, 28), (-1, 13, 152, 29), (-1, 13, 153, 30), (-1, 13, 154, 31), (-1, 13, 155, 32), (-1, 13, 156, 32), (-1, 14, 157, 32), (-1, 14, 158, 32), (-1, 14, 159, 32), (-1, 15, 160, 32), (-1, 16, 161, 31), (-1, 16, 162, 32), (-1, 16, 163, 32), (-1, 16, 164, 32), (-1, 17, 165, 32), (-1, 18, 166, 31), (-1, 18, 167, 31), (-1, 19, 168, 31), (-1, 19, 169, 31), (-1, 20, 170, 30), (-1, 21, 171, 29), (-1, 22, 172, 28), (-1, 22, 173, 28), (-1, 23, 174, 27), (-1, 24, 175, 26), (-1, 24, 176, 26), (-1, 26, 177, 24), (-1, 27, 178, 22), (-1, 30, 179, 18), (-1, 30, 180, 17), (-1, 33, 181, 14), (-1, 34, 182, 11), (-1, 37, 183, 3), (-1, 41, 183, 3)], 'hashtag': '', 'sum_segment': 0} About to upload 24 photos upload in portfolio : 21607075 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1742632870_394949 we have uploaded 24 photos in the portfolio 21607075 time of upload the photos Elapsed time : 5.487287521362305 map_filename_photo_id : 24 map_filename_photo_id : {'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg': 1346373515, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg': 1346373516, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg': 1346373517, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg': 1346373518, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg': 1346373519, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg': 1346373520, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg': 1346373521, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg': 1346373522, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg': 1346373523, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg': 1346373524, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg': 1346373525, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg': 1346373526, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg': 1346373527, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg': 1346373528, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg': 1346373529, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg': 1346373530, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg': 1346373531, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg': 1346373532, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg': 1346373533, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg': 1346373534, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg': 1346373535, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg': 1346373536, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg': 1346373537, 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg': 1346373538} Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 list_new_chi_with_photo_id : [, , , , , , , , , , , , , , , , , , , , , , , , , , , ] 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 ! 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 ! After datou_step_exec type output : time spend for datou_step_exec : 9.011034727096558 time spend to save output : 7.891654968261719e-05 total time spend for step 3 : 9.01111364364624 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1346373495'] map_info['map_portfolio_photo'] : {} final : True mtd_id 243 list_pids : [937852786, 937852786, '1346373495'] Looping around the photos to save general results len do output : 24 /1346373515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373532Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373538Didn'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, '1346373495', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 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 : [('243', None, '1346373515', 'None', None, None, None, None, None), ('243', None, '1346373516', 'None', None, None, None, None, None), ('243', None, '1346373517', 'None', None, None, None, None, None), ('243', None, '1346373518', 'None', None, None, None, None, None), ('243', None, '1346373519', 'None', None, None, None, None, None), ('243', None, '1346373520', 'None', None, None, None, None, None), ('243', None, '1346373521', 'None', None, None, None, None, None), ('243', None, '1346373522', 'None', None, None, None, None, None), ('243', None, '1346373523', 'None', None, None, None, None, None), ('243', None, '1346373524', 'None', None, None, None, None, None), ('243', None, '1346373525', 'None', None, None, None, None, None), ('243', None, '1346373526', 'None', None, None, None, None, None), ('243', None, '1346373527', 'None', None, None, None, None, None), ('243', None, '1346373528', 'None', None, None, None, None, None), ('243', None, '1346373529', 'None', None, None, None, None, None), ('243', None, '1346373530', 'None', None, None, None, None, None), ('243', None, '1346373531', 'None', None, None, None, None, None), ('243', None, '1346373532', 'None', None, None, None, None, None), ('243', None, '1346373533', 'None', None, None, None, None, None), ('243', None, '1346373534', 'None', None, None, None, None, None), ('243', None, '1346373535', 'None', None, None, None, None, None), ('243', None, '1346373536', 'None', None, None, None, None, None), ('243', None, '1346373537', 'None', None, None, None, None, None), ('243', None, '1346373538', 'None', None, None, None, None, None), ('243', None, '937852786', None, None, None, None, None, None), ('243', None, '1346373495', None, None, None, None, None, None)] time used for this insertion : 0.018169164657592773 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1346373515: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1346373516: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1346373517: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1346373518: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1346373519: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1346373520: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1346373521: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1346373522: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1346373523: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1346373524: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1346373525: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1346373526: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1346373527: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1346373528: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1346373529: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1346373530: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1346373531: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1346373532: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1346373533: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1346373534: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1346373535: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1346373536: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1346373537: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1346373538: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} ret_da : {1346373515: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1346373516: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1346373517: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1346373518: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1346373519: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1346373520: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1346373521: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1346373522: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1346373523: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1346373524: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1346373525: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1346373526: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1346373527: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1346373528: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1346373529: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1346373530: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1346373531: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1346373532: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1346373533: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1346373534: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1346373535: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1346373536: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1346373537: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1346373538: ['937852786', 'temp/1742632835_394949_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} list chi : [[, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], []] ############################### TEST flip ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=571 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=571 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 571 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=571 # 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 ! no param json to modify List Step Type Loaded in datou : flip list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (911785586) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 911785586 download finish for photo 911785586 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.13427472114562988 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:flip Sat Mar 22 09:41:21 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632881_394949_911785586_d8582feabcd359151ff718b5832248c7-big.jpg': 911785586} map_photo_id_path_extension : {911785586: {'path': 'temp/1742632881_394949_911785586_d8582feabcd359151ff718b5832248c7-big.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_flip ! We are in a linear step without datou_depend ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 911785586) and `type` in (741) Loaded 6 chid ids of type : 741 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points +SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) map_chi_objs : {911785586: [, , , , , ]} photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 version de PIL : 9.5.0 vertically flipped image is saved in temp/1742632881_394949_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg Horizontal flip of photo 911785586 version de PIL : 9.5.0 horizontally flipped image is saved in temp/1742632881_394949_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg 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/1742632882_394949 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.7178511619567871 map_filename_photo_id : 2 map_filename_photo_id : {'temp/1742632881_394949_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg': 1346373544, 'temp/1742632881_394949_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg': 1346373545} Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 list_new_chi_with_photo_id : [, , , , , , , , , , , ] insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 12 chid ids of type : 741 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 0 INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 1.0044140815734863 time spend to save output : 0.00014901161193847656 total time spend for step 1 : 1.0045630931854248 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] map_info['map_portfolio_photo'] : {} final : True mtd_id 571 list_pids : [911785586] Looping around the photos to save general results len do output : 2 /1346373544 /1346373545 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 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 : [('571', None, '911785586', None, None, None, None, None, None)] time used for this insertion : 0.012432336807250977 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1346373544': ['911785586', 'temp/1742632881_394949_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1346373545': ['911785586', 'temp/1742632881_394949_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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=686 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=686 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 686 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=686 # 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 ! no param json to modify List Step Type Loaded in datou : crop list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (950103132) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 950103132 download finish for photo 950103132 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.19179463386535645 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:crop Sat Mar 22 09:41:23 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00.jpg': 950103132} map_photo_id_path_extension : {950103132: {'path': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} 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 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 950103132) and `type` in (755) and score>0.3 Loaded 8 chid ids of type : 755 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) ++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos where photo_id in ( 950103132) WARNING : margin is only used for type bib ! type of cropped photo chosen : rle we resize croppped photo by 1 on x axis and by 1 on y axis we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! map_result returned by crop_photo_return_map_crop : length : 8 map_result after crop : {1947670931: {'crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670931_0.jpg', 'coordonates': (183, 199, 15, 41), 'sub_photo_id': -1, 'same_chi': False}, 1947670932: {'crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670932_0.jpg', 'coordonates': (38, 85, 113, 140), 'sub_photo_id': -1, 'same_chi': False}, 1947670933: {'crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670933_0.jpg', 'coordonates': (168, 194, 141, 151), 'sub_photo_id': -1, 'same_chi': False}, 1947670934: {'crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670934_0.jpg', 'coordonates': (47, 101, 16, 110), 'sub_photo_id': -1, 'same_chi': False}, 1947670935: {'crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670935_0.jpg', 'coordonates': (175, 199, 104, 111), 'sub_photo_id': -1, 'same_chi': False}, 1947670936: {'crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670936_0.jpg', 'coordonates': (86, 130, 184, 196), 'sub_photo_id': -1, 'same_chi': False}, 1947670937: {'crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670937_0.jpg', 'coordonates': (79, 195, 0, 61), 'sub_photo_id': -1, 'same_chi': False}, 1947670938: {'crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670938_0.jpg', 'coordonates': (131, 155, 181, 195), 'sub_photo_id': -1, 'same_chi': False}} 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 : 21607076 in upload media Upload medias : ['temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0 temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg after data_to_send, before sending request after request b'{"photo_ids":["1346373562","1346373559","1346373567","1346373555","1346373560","1346373557","1346373551","1346373565"],"photo_ids_order":["1346373551","1346373555","1346373557","1346373559","1346373560","1346373562","1346373565","1346373567"],"photo_detail":[{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/22/355d9be5818acd369ba3393f05876c64.jpg","text":"TemporaryFile(/tmp/multipartBody1544797592156546136asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1742632885124,"filename":"1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/22/1bdb54c93a747217a49ad366538b72ce.jpg","text":"TemporaryFile(/tmp/multipartBody3925576052665027757asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1742632885124,"filename":"1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/22/7a439e8ce969828e14ed0f3009d7f69c.jpg","text":"TemporaryFile(/tmp/multipartBody4461947491758964452asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1742632885124,"filename":"1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/22/970608e37d4cbf3e9d63fc98b772101c.jpg","text":"TemporaryFile(/tmp/multipartBody8525834899117565862asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1742632885124,"filename":"1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/22/fc5c4e1521a87ae2e4e88a2dc48d89d8.jpg","text":"TemporaryFile(/tmp/multipartBody4557083337012346249asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1742632885124,"filename":"1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/22/63fb30fde02cf56b4ad4cd3cac5d38ef.jpg","text":"TemporaryFile(/tmp/multipartBody6171276892582425844asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1742632885124,"filename":"1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/22/d8049787436260dcd46de9b3eef41b96.jpg","text":"TemporaryFile(/tmp/multipartBody2866508524176441212asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1742632885124,"filename":"1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/3/22/0e0008cd66cc7cf1a55584e151fb4fa9.jpg","text":"TemporaryFile(/tmp/multipartBody7969057522585197843asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1742632885124,"filename":"1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg","height":0,"width":0}],"map_files_photo_id":{"file2":"1346373557","file6":"1346373565","file1":"1346373555","file7":"1346373567","file0":"1346373551","file4":"1346373560","file5":"1346373562","file3":"1346373559"},"map_files_photo_id_array":[{"photo_id":"1346373562","filename":"file5"},{"photo_id":"1346373557","filename":"file2"},{"photo_id":"1346373560","filename":"file4"},{"photo_id":"1346373567","filename":"file7"},{"photo_id":"1346373555","filename":"file1"},{"photo_id":"1346373551","filename":"file0"},{"photo_id":"1346373559","filename":"file3"},{"photo_id":"1346373565","filename":"file6"}],"portfolio_id":21607076,"hashtag_by_photo_ids":[{"1346373562":["hashtag1","hashtag2"]},{"1346373559":["hashtag1","hashtag2"]},{"1346373567":["hashtag1","hashtag2"]},{"1346373555":["hashtag1","hashtag2"]},{"1346373560":["hashtag1","hashtag2"]},{"1346373557":["hashtag1","hashtag2"]},{"1346373551":["hashtag1","hashtag2"]},{"1346373565":["hashtag1","hashtag2"]}],"comms":"Portfolio 21607076 used, photo_id : ArrayBuffer(1346373562, 1346373559, 1346373567, 1346373555, 1346373560, 1346373557, 1346373551, 1346373565)","result":[],"list_datou_current":[]}' Result OK ! uploaded one batch 0 Elapsed time : 19.23991823196411 map_result_insert : {'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg': 1346373557, 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg': 1346373565, 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg': 1346373555, 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg': 1346373567, 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg': 1346373551, 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg': 1346373560, 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg': 1346373562, 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg': 1346373559} Now we prepare data that will be used for ellipse search ! chi_id found to be used 1947670931 path of cropped varroa found to be used to match on an ellipse temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg sub_photo_id found to be used 1346373551 chi_id found to be used 1947670932 path of cropped varroa found to be used to match on an ellipse temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg sub_photo_id found to be used 1346373555 chi_id found to be used 1947670933 path of cropped varroa found to be used to match on an ellipse temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg sub_photo_id found to be used 1346373557 chi_id found to be used 1947670934 path of cropped varroa found to be used to match on an ellipse temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg sub_photo_id found to be used 1346373559 chi_id found to be used 1947670935 path of cropped varroa found to be used to match on an ellipse temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg sub_photo_id found to be used 1346373560 chi_id found to be used 1947670936 path of cropped varroa found to be used to match on an ellipse temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg sub_photo_id found to be used 1346373562 chi_id found to be used 1947670937 path of cropped varroa found to be used to match on an ellipse temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg sub_photo_id found to be used 1346373565 chi_id found to be used 1947670938 path of cropped varroa found to be used to match on an ellipse temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg sub_photo_id found to be used 1346373567 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1947670931, '1346373551', 31), (1947670932, '1346373555', 31), (1947670933, '1346373557', 31), (1947670934, '1346373559', 31), (1947670935, '1346373560', 31), (1947670936, '1346373562', 31), (1947670937, '1346373565', 31), (1947670938, '1346373567', 31)] map of cropped photos with some data : {'1346373551': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1346373555': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1346373557': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1346373559': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1346373560': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1346373562': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1346373565': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1346373567': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} After datou_step_exec type output : time spend for datou_step_exec : 19.839054822921753 time spend to save output : 3.3855438232421875e-05 total time spend for step 1 : 19.839088678359985 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] map_info['map_portfolio_photo'] : {} final : True mtd_id 686 list_pids : [950103132] Looping around the photos to save general results len do output : 8 /1346373551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1346373567Didn'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 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 : [('686', None, '1346373551', 'None', None, None, None, None, None), ('686', None, '1346373555', 'None', None, None, None, None, None), ('686', None, '1346373557', 'None', None, None, None, None, None), ('686', None, '1346373559', 'None', None, None, None, None, None), ('686', None, '1346373560', 'None', None, None, None, None, None), ('686', None, '1346373562', 'None', None, None, None, None, None), ('686', None, '1346373565', 'None', None, None, None, None, None), ('686', None, '1346373567', 'None', None, None, None, None, None), ('686', None, '950103132', None, None, None, None, None, None)] time used for this insertion : 0.014122486114501953 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1346373551': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1346373555': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1346373557': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1346373559': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1346373560': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1346373562': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1346373565': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1346373567': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} ret_da : {'1346373551': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1346373555': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1346373557': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1346373559': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1346373560': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1346373562': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1346373565': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1346373567': ['950103132', 'temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 Found filename_to_hash : temp/1742632882_394949_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=852 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=852 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 852 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=852 # 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 ! no param json to modify List Step Type Loaded in datou : angular_coeff list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (932296368) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 932296368 download finish for photo 932296368 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.12520360946655273 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:angular_coeff Sat Mar 22 09:41:43 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632902_394949_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg': 932296368} map_photo_id_path_extension : {932296368: {'path': 'temp/1742632902_394949_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} 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 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 932296368) and `type` in (846) Loaded 19 chid ids of type : 846 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872 treating photo 932296368 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872 After datou_step_exec type output : time spend for datou_step_exec : 0.09146904945373535 time spend to save output : 0.0009737014770507812 total time spend for step 1 : 0.09244275093078613 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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=708 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=708 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 708 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=708 # 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 ! no param json to modify List Step Type Loaded in datou : detection_filter_by_crop list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (946711423) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 946711423 download finish for photo 946711423 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.28049659729003906 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:detection_filter_by_crop Sat Mar 22 09:41:43 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632903_394949_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg': 946711423} map_photo_id_path_extension : {946711423: {'path': 'temp/1742632903_394949_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} 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 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (445) Loaded 3 chid ids of type : 445 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276) +++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (631) Loaded 35 chid ids of type : 631 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (445) Loaded 3 chid ids of type : 445 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276) +++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) treating photo 946711423 SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 surface aoutside conditional crop crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', surface aoutside conditional crop crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', ' crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, surface aoutside conditional crop After datou_step_exec type output : time spend for datou_step_exec : 0.12167930603027344 time spend to save output : 3.218650817871094e-05 total time spend for step 1 : 0.12171149253845215 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, 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['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 SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=946711423 AND `type`=816 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3723841136,3723841135,3723841134,3723841143,3723841142,3723841141,3723841140,3723841139,3723841148,3723841151,3723841137,3723841138,3723841147,3723841146,3723841152,3723841153,3723841154,3723841156,3723841144,3723841150,3723841149,3723841155,3723841145) Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=672 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=672 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 672 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=672 # 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 ! no param json to modify List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (946711423) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos ##### After load_data_input time to download the photos : 0.004349946975708008 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:detection_filter_by_classif Sat Mar 22 09:41:43 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 After prepare type args : Here we display some param of map_info ! map_filenames : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} 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 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (631) Loaded 35 chid ids of type : 631 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) treating photo 946711423 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872 list of crops kept {'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} for hahstag car_exterieur_angle_avant_droit_merge__port_551052 crop not duplicated for hashtag aile-arriere : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249 crop not duplicated for hashtag coffre : : {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274, crop not duplicated for hashtag aile-arriere : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 crop duplicated for hashtag retroviseur : : {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139 crop duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380, crop duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 crop not duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 crop duplicated for hashtag capot : : {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3 crop duplicated for hashtag pare-brise : : {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134, crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 crop duplicated for hashtag phare : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3 crop duplicated for hashtag phare : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286, crop duplicated for hashtag feu-antibrouillard : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3 crop duplicated for hashtag feu-antibrouillard : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362 crop duplicated for hashtag poignee : : {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13 crop duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137 crop duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 crop not duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 crop not duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', ' crop duplicated for hashtag calandre : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35 crop not duplicated for hashtag calandre : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3 crop duplicated for hashtag logo-marque : : {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232 crop duplicated for hashtag plaque-immatriculation : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348, crop not duplicated for hashtag plaque-immatriculation : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280, crop duplicated for hashtag essuie-glace : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136, crop not duplicated for hashtag essuie-glace : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269 crop duplicated for hashtag pare-choc : : {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417, crop duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5 crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253 crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 list of crops kept {'pare-choc': ('pare-chocs-avant',), 'phare': ('phare-droite', 'a-gauche-de', 'phare-gauche'), 'porte': ('porte-avant', 'a-droite-de', 'porte-arriere')} for hahstag car_exterieur_angle_avant_droit_merge__port_551052 batch 1 Loaded 0 chid ids of type : 0 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 23 chid ids of type : 816 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 1600 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3724021973', '117', '95', '16') ... last line : ('3724021995', '70', '147', '1') INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 0.2879931926727295 time spend to save output : 0.00012946128845214844 total time spend for step 1 : 0.28812265396118164 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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1243 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1243 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1243 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1243 # 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 ! no param json to modify List Step Type Loaded in datou : blur_detection list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (930729675) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 930729675 download finish for photo 930729675 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.177232027053833 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:blur_detection Sat Mar 22 09:41:44 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632903_394949_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg': 930729675} map_photo_id_path_extension : {930729675: {'path': 'temp/1742632903_394949_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blur_detection methode: ratio et variance treat image : temp/1742632903_394949_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 score_blur_detection : {930729675: [(930729675, 12.961859636534896, 492688767)]} After datou_step_exec type output : time spend for datou_step_exec : 0.19789838790893555 time spend to save output : 5.555152893066406e-05 total time spend for step 1 : 0.1979539394378662 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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1908 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1908 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1908 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1908 # 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 ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (987515175,987515176,987515177,987515178,987515179,987515180,987515181,987515182,987515183,987515184,987515185,987515186,987515187,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515198,987515200,987515201,987515202,987515204,987515205,987515207,987515208,987515209,987515211,987515212,987515213,987515215,987515216,987515217,987515219,987515220,987515222,987515223,987515224,987515226,987515227,987515228,987515230,987515231,987515232,987515233,987515234,987515235,987515236,987515237,987515238,987515239,987515240,987515241,987515242,987515243,987515244,987515245,987515246,987515247,987515248,987515249,987515250) Found this number of photos: 64 ##### Call download_photos : nb_thread : 5 begin to download photo : 987515175 begin to download photo : 987515188 begin to download photo : 987515207 begin to download photo : 987515224 begin to download photo : 987515239 download finish for photo 987515224 begin to download photo : 987515226 download finish for photo 987515239 begin to download photo : 987515240 download finish for photo 987515207 begin to download photo : 987515208 download finish for photo 987515188 begin to download photo : 987515189 download finish for photo 987515226 begin to download photo : 987515227 download finish for photo 987515175 begin to download photo : 987515176 download finish for photo 987515240 begin to download photo : 987515241 download finish for photo 987515189 begin to download photo : 987515190 download finish for photo 987515208 begin to download photo : 987515209 download finish for photo 987515176 begin to download photo : 987515177 download finish for photo 987515190 begin to download photo : 987515192 download finish for photo 987515241 begin to download photo : 987515242 download finish for photo 987515227 begin to download photo : 987515228 download finish for photo 987515177 begin to download photo : 987515178 download finish for photo 987515192 begin to download photo : 987515193 download finish for photo 987515242 begin to download photo : 987515243 download finish for photo 987515209 begin to download photo : 987515211 download finish for photo 987515228 begin to download photo : 987515230 download finish for photo 987515230 begin to download photo : 987515231 download finish for photo 987515211 begin to download photo : 987515212 download finish for photo 987515178 begin to download photo : 987515179 download finish for photo 987515243 begin to download photo : 987515244 download finish for photo 987515193 begin to download photo : 987515195 download finish for photo 987515231 begin to download photo : 987515232 download finish for photo 987515212 begin to download photo : 987515213 download finish for photo 987515244 begin to download photo : 987515245 download finish for photo 987515179 begin to download photo : 987515180 download finish for photo 987515213 begin to download photo : 987515215 download finish for photo 987515195 begin to download photo : 987515196 download finish for photo 987515232 begin to download photo : 987515233 download finish for photo 987515245 begin to download photo : 987515246 download finish for photo 987515196 begin to download photo : 987515198 download finish for photo 987515180 begin to download photo : 987515181 download finish for photo 987515233 begin to download photo : 987515234 download finish for photo 987515215 begin to download photo : 987515216 download finish for photo 987515181 begin to download photo : 987515182 download finish for photo 987515198 begin to download photo : 987515200 download finish for photo 987515234 begin to download photo : 987515235 download finish for photo 987515216 begin to download photo : 987515217 download finish for photo 987515246 begin to download photo : 987515247 download finish for photo 987515200 begin to download photo : 987515201 download finish for photo 987515235 begin to download photo : 987515236 download finish for photo 987515217 begin to download photo : 987515219 download finish for photo 987515182 begin to download photo : 987515183 download finish for photo 987515247 begin to download photo : 987515248 download finish for photo 987515201 begin to download photo : 987515202 download finish for photo 987515219 begin to download photo : 987515220 download finish for photo 987515183 begin to download photo : 987515184 download finish for photo 987515236 begin to download photo : 987515237 download finish for photo 987515202 begin to download photo : 987515204 download finish for photo 987515248 begin to download photo : 987515249 download finish for photo 987515220 begin to download photo : 987515222 download finish for photo 987515184 begin to download photo : 987515185 download finish for photo 987515237 begin to download photo : 987515238 download finish for photo 987515204 begin to download photo : 987515205 download finish for photo 987515249 begin to download photo : 987515250 download finish for photo 987515222 begin to download photo : 987515223 download finish for photo 987515185 begin to download photo : 987515186 download finish for photo 987515205 download finish for photo 987515238 download finish for photo 987515223 download finish for photo 987515250 download finish for photo 987515186 begin to download photo : 987515187 download finish for photo 987515187 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 ##### After load_data_input time to download the photos : 1.4481968879699707 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:thcl Sat Mar 22 09:41:45 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632904_394949_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1742632904_394949_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1742632904_394949_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1742632904_394949_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1742632904_394949_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1742632904_394949_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1742632904_394949_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1742632904_394949_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1742632904_394949_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1742632904_394949_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1742632904_394949_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1742632904_394949_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1742632904_394949_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1742632904_394949_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1742632904_394949_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1742632904_394949_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1742632904_394949_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1742632904_394949_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1742632904_394949_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1742632904_394949_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1742632904_394949_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1742632904_394949_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1742632904_394949_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1742632904_394949_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1742632904_394949_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1742632904_394949_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 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l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! dimension displayed ! time to import caffe and check if the image exist : 0.004679679870605469 time to convert the images to numpy array : 0.005705833435058594 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0035245418548583984 time to convert the images to numpy array : 0.03743386268615723 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.007310390472412109 time to convert the images to numpy array : 0.03602862358093262 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.007501363754272461 time to convert the images to numpy array : 0.03427267074584961 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.008147001266479492 time to convert the images to numpy array : 0.03606557846069336 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.007765293121337891 time to convert the images to numpy array : 0.038789987564086914 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0038568973541259766 time to convert the images to numpy array : 0.042612314224243164 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.01683330535888672 time to convert the images to numpy array : 0.02900218963623047 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.013784646987915039 time to convert the images to numpy array : 0.03518414497375488 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.018236637115478516 time to convert the images to numpy array : 0.029633760452270508 total time to convert the images to numpy array : 0.05040264129638672 list photo_ids error: [] list photo_ids correct : [987515187, 987515219, 987515220, 987515222, 987515223, 987515239, 987515240, 987515241, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515248, 987515234, 987515235, 987515236, 987515237, 987515238, 987515207, 987515208, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515249, 987515250, 987515175, 987515176, 987515177, 987515178, 987515179] 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 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (1528) 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 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (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 : 1968 wait 20 seconds l 3637 free memory gpu now : 1968 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (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)) param : , param.caffemodel : learn_refus_upm_blanches_1924 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/caffe_cuda8_python3/python/:/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 havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 1968 wait 20 seconds l 3637 free memory gpu now : 1968 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'res5b']) time used to do the prepocess of the images : 0.06921625137329102 time used to do the prediction : 0.26862072944641113 save descriptor for thcl : 1528 (64, 512, 7, 7) Got the blobs of the net to insert : [3, 1, 1, 1, 1, 1, 2, 1, 1, 2] code_as_byte_string:b'0301010101'| Got the blobs of the net to insert : [0, 0, 2, 3, 3, 1, 0, 0, 0, 0] code_as_byte_string:b'0000020303'| Got the blobs of the net to insert : [1, 0, 0, 1, 0, 0, 0, 3, 4, 5] code_as_byte_string:b'0100000100'| Got the blobs of the net to insert : [0, 1, 1, 4, 2, 1, 3, 7, 9, 9] code_as_byte_string:b'0001010402'| Got the blobs of the net to insert : [7, 10, 10, 2, 4, 7, 8, 5, 5, 2] code_as_byte_string:b'070a0a0204'| Got the blobs of the net to insert : [1, 1, 0, 0, 0, 0, 0, 3, 5, 1] code_as_byte_string:b'0101000000'| Got the blobs of the net to insert : [0, 3, 0, 1, 1, 6, 7, 5, 5, 3] code_as_byte_string:b'0003000101'| Got the blobs of the net to insert : [6, 6, 3, 3, 8, 8, 6, 6, 2, 0] code_as_byte_string:b'0606030308'| Got the blobs of the net to insert : [1, 1, 1, 2, 0, 3, 4, 3, 4, 5] code_as_byte_string:b'0101010200'| Got the blobs of the net to insert : [3, 2, 1, 4, 5, 7, 6, 5, 6, 5] code_as_byte_string:b'0302010405'| Got the blobs of the net to insert : [3, 4, 5, 5, 9, 9, 9, 9, 11, 7] code_as_byte_string:b'0304050509'| Got the blobs of the net to insert : [5, 3, 3, 6, 8, 12, 9, 9, 3, 5] code_as_byte_string:b'0503030608'| Got the blobs of the net to insert : [6, 3, 3, 0, 1, 2, 2, 5, 2, 4] code_as_byte_string:b'0603030001'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 2, 5, 1, 0, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 2, 5, 1, 0, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [5, 5, 2, 4, 6, 5, 9, 9, 4, 2] code_as_byte_string:b'0505020406'| Got the blobs of the net to insert : [2, 1, 3, 5, 7, 5, 3, 4, 1, 3] code_as_byte_string:b'0201030507'| Got the blobs of the net to insert : [0, 0, 0, 1, 1, 3, 2, 1, 0, 2] code_as_byte_string:b'0000000101'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 1, 3, 1, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [0, 0, 0, 1, 0, 0, 0, 0, 1, 3] code_as_byte_string:b'0000000100'| Got the blobs of the net to insert : [0, 0, 0, 1, 2, 0, 1, 4, 0, 2] code_as_byte_string:b'0000000102'| Got the blobs of the net to insert : [4, 1, 3, 4, 7, 3, 4, 2, 1, 0] code_as_byte_string:b'0401030407'| Got the blobs of the net to insert : [6, 8, 3, 6, 6, 6, 5, 10, 10, 3] code_as_byte_string:b'0608030606'| Got the blobs of the net to insert : [1, 2, 1, 5, 7, 10, 8, 2, 2, 1] code_as_byte_string:b'0102010507'| Got the blobs of the net to insert : [0, 0, 0, 1, 5, 4, 6, 3, 1, 3] code_as_byte_string:b'0000000105'| Got the blobs of the net to insert : [0, 2, 1, 0, 0, 0, 0, 0, 4, 2] code_as_byte_string:b'0002010000'| Got the blobs of the net to insert : [0, 2, 0, 1, 1, 0, 0, 0, 1, 3] code_as_byte_string:b'0002000101'| Got the blobs of the net to insert : [4, 2, 1, 2, 3, 1, 0, 1, 0, 1] code_as_byte_string:b'0402010203'| Got the blobs of the net to insert : [0, 0, 0, 1, 2, 2, 0, 1, 0, 0] code_as_byte_string:b'0000000102'| Got the blobs of the net to insert : [1, 1, 0, 0, 1, 2, 0, 0, 0, 1] code_as_byte_string:b'0101000001'| Got the blobs of the net to insert : [0, 1, 0, 1, 3, 4, 2, 1, 3, 5] code_as_byte_string:b'0001000103'| Got the blobs of the net to insert : [3, 3, 3, 8, 7, 5, 5, 4, 5, 3] code_as_byte_string:b'0303030807'| Got the blobs of the net to insert : [2, 5, 4, 6, 9, 9, 10, 9, 11, 5] code_as_byte_string:b'0205040609'| Got the blobs of the net to insert : [5, 2, 3, 7, 5, 7, 6, 2, 4, 3] code_as_byte_string:b'0502030705'| Got the blobs of the net to insert : [5, 7, 3, 2, 1, 2, 2, 2, 0, 0] code_as_byte_string:b'0507030201'| Got the blobs of the net to insert : [7, 7, 3, 8, 9, 6, 7, 10, 11, 6] code_as_byte_string:b'0707030809'| Got the blobs of the net to insert : [3, 3, 3, 1, 6, 7, 10, 5, 3, 5] code_as_byte_string:b'0303030106'| Got the blobs of the net to insert : [3, 2, 2, 1, 1, 2, 2, 3, 2, 5] code_as_byte_string:b'0302020101'| Got the blobs of the net to insert : [1, 0, 3, 1, 0, 0, 0, 0, 0, 5] code_as_byte_string:b'0100030100'| Got the blobs of the net to insert : [0, 0, 2, 2, 0, 0, 0, 0, 0, 3] code_as_byte_string:b'0000020200'| Got the blobs of the net to insert : [1, 1, 2, 1, 0, 0, 0, 3, 6, 7] code_as_byte_string:b'0101020100'| Got the blobs of the net to insert : [1, 3, 3, 3, 0, 1, 5, 6, 7, 4] code_as_byte_string:b'0103030300'| Got the blobs of the net to insert : [5, 7, 3, 2, 5, 9, 10, 3, 5, 0] code_as_byte_string:b'0507030205'| Got the blobs of the net to insert : [1, 0, 1, 2, 1, 0, 3, 3, 3, 8] code_as_byte_string:b'0100010201'| Got the blobs of the net to insert : [5, 5, 6, 4, 3, 6, 9, 7, 7, 7] code_as_byte_string:b'0505060403'| Got the blobs of the net to insert : [4, 6, 6, 4, 7, 8, 8, 8, 12, 8] code_as_byte_string:b'0406060407'| Got the blobs of the net to insert : [11, 8, 5, 9, 12, 14, 13, 14, 12, 6] code_as_byte_string:b'0b0805090c'| Got the blobs of the net to insert : [8, 7, 6, 4, 2, 1, 2, 4, 4, 4] code_as_byte_string:b'0807060402'| Got the blobs of the net to insert : [2, 3, 5, 5, 2, 2, 3, 0, 1, 4] code_as_byte_string:b'0203050502'| Got the blobs of the net to insert : [0, 0, 0, 1, 1, 1, 3, 3, 0, 0] code_as_byte_string:b'0000000101'| Got the blobs of the net to insert : [1, 0, 0, 1, 1, 1, 0, 1, 1, 3] code_as_byte_string:b'0100000101'| Got the blobs of the net to insert : [1, 1, 2, 3, 4, 4, 1, 2, 8, 7] code_as_byte_string:b'0101020304'| Got the blobs of the net to insert : [6, 8, 5, 7, 7, 8, 10, 12, 12, 6] code_as_byte_string:b'0608050707'| Got the blobs of the net to insert : [6, 8, 5, 7, 7, 8, 10, 12, 12, 6] code_as_byte_string:b'0608050707'| Got the blobs of the net to insert : [5, 5, 4, 3, 5, 6, 3, 4, 3, 3] code_as_byte_string:b'0505040305'| Got the blobs of the net to insert : [5, 3, 3, 1, 1, 1, 2, 2, 2, 4] code_as_byte_string:b'0503030101'| Got the blobs of the net to insert : [2, 1, 2, 2, 2, 0, 0, 1, 0, 3] code_as_byte_string:b'0201020202'| Got the blobs of the net to insert : [4, 1, 3, 4, 7, 3, 4, 2, 1, 0] code_as_byte_string:b'0401030407'| Got the blobs of the net to insert : [3, 3, 1, 4, 7, 7, 9, 6, 2, 1] code_as_byte_string:b'0303010407'| Got the blobs of the net to insert : [13, 9, 9, 8, 11, 13, 6, 14, 9, 18] code_as_byte_string:b'0d0909080b'| Got the blobs of the net to insert : [13, 9, 9, 8, 11, 13, 6, 14, 9, 18] code_as_byte_string:b'0d0909080b'| Got the blobs of the net to insert : [2, 2, 6, 7, 8, 6, 4, 3, 1, 8] code_as_byte_string:b'0202060708'| Got the blobs of the net to insert : [1, 2, 2, 1, 0, 1, 1, 2, 1, 1] code_as_byte_string:b'0102020100'| Got the blobs of the net to insert : [2, 2, 1, 3, 2, 3, 2, 0, 0, 1] code_as_byte_string:b'0202010302'| time to traite the descriptors : 3.1507391929626465 Testing : ['987515187', '987515219', '987515220', '987515222', '987515223', '987515239', '987515240', '987515241', '987515188', '987515189', '987515190', '987515192', '987515193', '987515195', '987515196', '987515242', '987515243', '987515244', '987515245', '987515246', '987515247', '987515248', '987515234', '987515235', '987515236', '987515237', '987515238', '987515207', '987515208', '987515198', '987515200', '987515201', '987515202', '987515204', '987515205', '987515224', '987515226', '987515227', '987515228', '987515230', '987515231', '987515232', '987515233', '987515180', '987515181', '987515182', '987515183', '987515184', '987515185', '987515186', '987515209', '987515211', '987515212', '987515213', '987515215', '987515216', '987515217', '987515249', '987515250', '987515175', '987515176', '987515177', '987515178', '987515179'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (987515187,987515219,987515220,987515222,987515223,987515239,987515240,987515241,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515242,987515243,987515244,987515245,987515246,987515247,987515248,987515234,987515235,987515236,987515237,987515238,987515207,987515208,987515198,987515200,987515201,987515202,987515204,987515205,987515224,987515226,987515227,987515228,987515230,987515231,987515232,987515233,987515180,987515181,987515182,987515183,987515184,987515185,987515186,987515209,987515211,987515212,987515213,987515215,987515216,987515217,987515249,987515250,987515175,987515176,987515177,987515178,987515179) result : {987515175: {'photo_id': 987515175, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_14_16_02_694514_0001.jpg'}, 987515176: {'photo_id': 987515176, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_144.jpg'}, 987515177: {'photo_id': 987515177, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4a54e9967227806219ddf45d256539d8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_176.jpg'}, 987515178: {'photo_id': 987515178, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/298b3d2bfe0fda6787b59a78e2e68867.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_208.jpg'}, 987515179: {'photo_id': 987515179, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_240.jpg'}, 987515180: {'photo_id': 987515180, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_272.jpg'}, 987515181: {'photo_id': 987515181, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1738c2798fb31152809ecb443ac286d6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_304.jpg'}, 987515182: {'photo_id': 987515182, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fe7f29bf6d13e08c3e985f91b5232178.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_336.jpg'}, 987515183: {'photo_id': 987515183, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/6aab9ca0421398b4899892c10c2594c6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_112.jpg'}, 987515184: {'photo_id': 987515184, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/19c8c2177209a285df6014d95fe53f2c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_144.jpg'}, 987515185: {'photo_id': 987515185, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e172d54457cabee9d7f02ee1300f3ae9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_176.jpg'}, 987515186: {'photo_id': 987515186, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/797def426440b544aa80dbd63a19234a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_208.jpg'}, 987515187: {'photo_id': 987515187, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9f62f98efd3caca0b9c17d27f5c70440.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_240.jpg'}, 987515188: {'photo_id': 987515188, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4116f9906657a69bb76c2fda982037b9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_272.jpg'}, 987515189: {'photo_id': 987515189, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8e8590a26f72249d4c2116dffd0cf668.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_304.jpg'}, 987515190: {'photo_id': 987515190, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/d56932bfc6ba2a8c974c691108755017.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_336.jpg'}, 987515192: {'photo_id': 987515192, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b661073b218f5f056833d6af1c617153.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_112.jpg'}, 987515193: {'photo_id': 987515193, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_144.jpg'}, 987515195: {'photo_id': 987515195, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/30ccb89dfe410c445878a7f2819ddc36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_17_37_58_622227.jpg'}, 987515196: {'photo_id': 987515196, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/30ccb89dfe410c445878a7f2819ddc36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_208.jpg'}, 987515198: {'photo_id': 987515198, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/599e80f444c876f407e94b533c89360b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_240.jpg'}, 987515200: {'photo_id': 987515200, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/978964436b5d5fb0eeda17e3bfafe889.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_272.jpg'}, 987515201: {'photo_id': 987515201, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_304.jpg'}, 987515202: {'photo_id': 987515202, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/3314bd90d1404f31b827d8925abf2d62.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_336.jpg'}, 987515204: {'photo_id': 987515204, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9779c4f9d44360a9c80499e3b01e8a09.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_112.jpg'}, 987515205: {'photo_id': 987515205, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_144.jpg'}, 987515207: {'photo_id': 987515207, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/de216ddb041e249524b0fb2b949064a5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_176.jpg'}, 987515208: {'photo_id': 987515208, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_208.jpg'}, 987515209: {'photo_id': 987515209, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/02dfe1ae39f51994652f4a8538844aea.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_240.jpg'}, 987515211: {'photo_id': 987515211, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/72cc7664d45bd40477351b9b764f1500.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_272.jpg'}, 987515212: {'photo_id': 987515212, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_09_32_14_525625.jpg'}, 987515213: {'photo_id': 987515213, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_336.jpg'}, 987515215: {'photo_id': 987515215, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/902ef348a7eebb9a8b87f42927347936.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_112.jpg'}, 987515216: {'photo_id': 987515216, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_144.jpg'}, 987515217: {'photo_id': 987515217, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/78877bb2c5760be28518d17f77d1c609.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_176.jpg'}, 987515219: {'photo_id': 987515219, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_208.jpg'}, 987515220: {'photo_id': 987515220, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e729f316c4c3b32049adfbaaa336d95c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_240.jpg'}, 987515222: {'photo_id': 987515222, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/067a027bc7402f969b6277d0dcb47eaa.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_272.jpg'}, 987515223: {'photo_id': 987515223, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/ebb57f09941cd11d7ee45a9368a883c1.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_304.jpg'}, 987515224: {'photo_id': 987515224, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e8747b400e713ecbd08d5b75db4d7568.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_336.jpg'}, 987515226: {'photo_id': 987515226, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a18048dca1a77ae086b62cf07759f704.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_112.jpg'}, 987515227: {'photo_id': 987515227, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_144.jpg'}, 987515228: {'photo_id': 987515228, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_176.jpg'}, 987515230: {'photo_id': 987515230, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/846ad925884264181565c81d152a2e94.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_208.jpg'}, 987515231: {'photo_id': 987515231, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_240.jpg'}, 987515232: {'photo_id': 987515232, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/38db7950cdb3c674ee0ad65915b021f3.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_272.jpg'}, 987515233: {'photo_id': 987515233, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_304.jpg'}, 987515234: {'photo_id': 987515234, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/2eca3480aed0f8b876242675ad99b666.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_336.jpg'}, 987515235: {'photo_id': 987515235, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/87075955a2f76b3948b47ffe1825ecd9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_112.jpg'}, 987515236: {'photo_id': 987515236, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b44a98b1aceadad73ed000d65836a9a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_144.jpg'}, 987515237: {'photo_id': 987515237, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1183dfa371a457f11ce2b622c7cf9467.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_176.jpg'}, 987515238: {'photo_id': 987515238, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_208.jpg'}, 987515239: {'photo_id': 987515239, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b3fa6f29636080b5138c8d8c33fea309.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_240.jpg'}, 987515240: {'photo_id': 987515240, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_272.jpg'}, 987515241: {'photo_id': 987515241, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/073420d938f5f010ffd5b4353c064e09.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_304.jpg'}, 987515242: {'photo_id': 987515242, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/327abb5215d6fd1f0aad51f53ed8c324.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_336.jpg'}, 987515243: {'photo_id': 987515243, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_112.jpg'}, 987515244: {'photo_id': 987515244, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/419530eaef5ef868f75c758b94eea4b4.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_144.jpg'}, 987515245: {'photo_id': 987515245, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/757d9d208d5bd4375c5f21f68b699148.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_176.jpg'}, 987515246: {'photo_id': 987515246, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/671a708f67f2efa19004b8257fc7b9c8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_208.jpg'}, 987515247: {'photo_id': 987515247, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e47b65403df916ba909bc9c439b0af73.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_240.jpg'}, 987515248: {'photo_id': 987515248, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a70ad88462a22fb62a120721a42b2d42.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_14_16_02_694514_0002.jpg'}, 987515249: {'photo_id': 987515249, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a70ad88462a22fb62a120721a42b2d42.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_304.jpg'}, 987515250: {'photo_id': 987515250, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b2827c9639df69656f23abcc7f2f82d9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_336.jpg'}} list_photo_exists : [987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515248, 987515249, 987515250] storage_type for insertDescriptorsMulti : 1 To insert : 987515187 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515223 To insert : 987515239 To insert : 987515240 To insert : 987515241 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515247 To insert : 987515248 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515207 To insert : 987515208 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515209 To insert : 987515211 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515249 To insert : 987515250 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 time to insert the descriptors : 17.124996185302734 After datou_step_exec type output : time spend for datou_step_exec : 64.33099436759949 time spend to save output : 0.0001671314239501953 total time spend for step 1 : 64.33116149902344 step2:argmax Sat Mar 22 09:42:50 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632904_394949_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1742632904_394949_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1742632904_394949_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1742632904_394949_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1742632904_394949_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1742632904_394949_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1742632904_394949_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1742632904_394949_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 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987515232, 'temp/1742632904_394949_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1742632904_394949_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1742632904_394949_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1742632904_394949_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1742632904_394949_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1742632904_394949_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 'temp/1742632904_394949_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1742632904_394949_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1742632904_394949_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1742632904_394949_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1742632904_394949_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515212, 'temp/1742632904_394949_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1742632904_394949_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1742632904_394949_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1742632904_394949_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1742632904_394949_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1742632904_394949_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1742632904_394949_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 'temp/1742632904_394949_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg': 987515223, 'temp/1742632904_394949_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1742632904_394949_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1742632904_394949_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1742632904_394949_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1742632904_394949_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1742632904_394949_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1742632904_394949_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1742632904_394949_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1742632904_394949_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1742632904_394949_987515248_a70ad88462a22fb62a120721a42b2d42.jpg': 987515248, 'temp/1742632904_394949_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1742632904_394949_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250, 'temp/1742632904_394949_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515175, 'temp/1742632904_394949_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515176, 'temp/1742632904_394949_987515177_4a54e9967227806219ddf45d256539d8.jpg': 987515177, 'temp/1742632904_394949_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg': 987515178, 'temp/1742632904_394949_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg': 987515179, 'temp/1742632904_394949_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg': 987515180, 'temp/1742632904_394949_987515181_1738c2798fb31152809ecb443ac286d6.jpg': 987515181, 'temp/1742632904_394949_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg': 987515182, 'temp/1742632904_394949_987515183_6aab9ca0421398b4899892c10c2594c6.jpg': 987515183, 'temp/1742632904_394949_987515184_19c8c2177209a285df6014d95fe53f2c.jpg': 987515184, 'temp/1742632904_394949_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg': 987515185, 'temp/1742632904_394949_987515186_797def426440b544aa80dbd63a19234a.jpg': 987515186, 'temp/1742632904_394949_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg': 987515187} map_photo_id_path_extension : {987515188: {'path': 'temp/1742632904_394949_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1742632904_394949_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 'temp/1742632904_394949_987515190_d56932bfc6ba2a8c974c691108755017.jpg', 'extension': 'jpg'}, 987515192: {'path': 'temp/1742632904_394949_987515192_b661073b218f5f056833d6af1c617153.jpg', 'extension': 'jpg'}, 987515193: {'path': 'temp/1742632904_394949_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'extension': 'jpg'}, 987515195: {'path': 'temp/1742632904_394949_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515196: {'path': 'temp/1742632904_394949_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515198: {'path': 'temp/1742632904_394949_987515198_599e80f444c876f407e94b533c89360b.jpg', 'extension': 'jpg'}, 987515200: {'path': 'temp/1742632904_394949_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg', 'extension': 'jpg'}, 987515201: {'path': 'temp/1742632904_394949_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'extension': 'jpg'}, 987515202: {'path': 'temp/1742632904_394949_987515202_3314bd90d1404f31b827d8925abf2d62.jpg', 'extension': 'jpg'}, 987515204: {'path': 'temp/1742632904_394949_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg', 'extension': 'jpg'}, 987515205: {'path': 'temp/1742632904_394949_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1742632904_394949_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1742632904_394949_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1742632904_394949_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1742632904_394949_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1742632904_394949_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1742632904_394949_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1742632904_394949_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1742632904_394949_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1742632904_394949_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1742632904_394949_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1742632904_394949_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1742632904_394949_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1742632904_394949_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515207: {'path': 'temp/1742632904_394949_987515207_de216ddb041e249524b0fb2b949064a5.jpg', 'extension': 'jpg'}, 987515208: {'path': 'temp/1742632904_394949_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'extension': 'jpg'}, 987515209: {'path': 'temp/1742632904_394949_987515209_02dfe1ae39f51994652f4a8538844aea.jpg', 'extension': 'jpg'}, 987515211: {'path': 'temp/1742632904_394949_987515211_72cc7664d45bd40477351b9b764f1500.jpg', 'extension': 'jpg'}, 987515212: {'path': 'temp/1742632904_394949_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515213: {'path': 'temp/1742632904_394949_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515215: {'path': 'temp/1742632904_394949_987515215_902ef348a7eebb9a8b87f42927347936.jpg', 'extension': 'jpg'}, 987515216: {'path': 'temp/1742632904_394949_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1742632904_394949_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1742632904_394949_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1742632904_394949_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1742632904_394949_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1742632904_394949_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515239: {'path': 'temp/1742632904_394949_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1742632904_394949_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1742632904_394949_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1742632904_394949_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1742632904_394949_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1742632904_394949_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1742632904_394949_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1742632904_394949_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 'temp/1742632904_394949_987515247_e47b65403df916ba909bc9c439b0af73.jpg', 'extension': 'jpg'}, 987515248: {'path': 'temp/1742632904_394949_987515248_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515249: {'path': 'temp/1742632904_394949_987515249_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515250: {'path': 'temp/1742632904_394949_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg', 'extension': 'jpg'}, 987515175: {'path': 'temp/1742632904_394949_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1742632904_394949_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1742632904_394949_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1742632904_394949_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1742632904_394949_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1742632904_394949_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1742632904_394949_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1742632904_394949_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1742632904_394949_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1742632904_394949_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1742632904_394949_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1742632904_394949_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1742632904_394949_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 After datou_step_exec type output : time spend for datou_step_exec : 0.001491546630859375 time spend to save output : 9.250640869140625e-05 total time spend for step 2 : 0.0015840530395507812 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', 0.98113465, 1927, '1528'), 'temp/1742632904_394949_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515219': [('987515219', 'Carton', 0.9993687, 1927, '1528'), 'temp/1742632904_394949_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.99638087, 1927, '1528'), 'temp/1742632904_394949_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.9974718, 1927, '1528'), 'temp/1742632904_394949_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920893, 1927, '1528'), 'temp/1742632904_394949_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515239': [('987515239', 'Carton', 0.9997838, 1927, '1528'), 'temp/1742632904_394949_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.9995202, 1927, '1528'), 'temp/1742632904_394949_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9821364, 1927, '1528'), 'temp/1742632904_394949_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515188': [('987515188', 'Carton', 0.9956559, 1927, '1528'), 'temp/1742632904_394949_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977871, 1927, '1528'), 'temp/1742632904_394949_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.97633135, 1927, '1528'), 'temp/1742632904_394949_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999112, 1927, '1528'), 'temp/1742632904_394949_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.99939525, 1927, '1528'), 'temp/1742632904_394949_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.98465675, 1927, '1528'), 'temp/1742632904_394949_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98465085, 1927, '1528'), 'temp/1742632904_394949_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515242': [('987515242', 'Carton', 0.93581206, 1927, '1528'), 'temp/1742632904_394949_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.87448514, 1927, '1528'), 'temp/1742632904_394949_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.8175773, 1927, '1528'), 'temp/1742632904_394949_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8657972, 1927, '1528'), 'temp/1742632904_394949_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992323, 1927, '1528'), 'temp/1742632904_394949_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966776, 1927, '1528'), 'temp/1742632904_394949_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.98134696, 1927, '1528'), 'temp/1742632904_394949_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515234': [('987515234', 'Carton', 0.94461477, 1927, '1528'), 'temp/1742632904_394949_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8921003, 1927, '1528'), 'temp/1742632904_394949_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.53760046, 1927, '1528'), 'temp/1742632904_394949_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7704212, 1927, '1528'), 'temp/1742632904_394949_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.9995739, 1927, '1528'), 'temp/1742632904_394949_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8739202, 1927, '1528'), 'temp/1742632904_394949_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.991702, 1927, '1528'), 'temp/1742632904_394949_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515198': [('987515198', 'Carton', 0.9659295, 1927, '1528'), 'temp/1742632904_394949_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.98595077, 1927, '1528'), 'temp/1742632904_394949_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99545175, 1927, '1528'), 'temp/1742632904_394949_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911265, 1927, '1528'), 'temp/1742632904_394949_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950766, 1927, '1528'), 'temp/1742632904_394949_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908805, 1927, '1528'), 'temp/1742632904_394949_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.9085119, 1927, '1528'), 'temp/1742632904_394949_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.986969, 1927, '1528'), 'temp/1742632904_394949_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.90034914, 1927, '1528'), 'temp/1742632904_394949_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5211269, 1927, '1528'), 'temp/1742632904_394949_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.9994062, 1927, '1528'), 'temp/1742632904_394949_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.9994216, 1927, '1528'), 'temp/1742632904_394949_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992459, 1927, '1528'), 'temp/1742632904_394949_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.98338896, 1927, '1528'), 'temp/1742632904_394949_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515180': [('987515180', 'Carton', 0.9899866, 1927, '1528'), 'temp/1742632904_394949_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.99777955, 1927, '1528'), 'temp/1742632904_394949_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.9924273, 1927, '1528'), 'temp/1742632904_394949_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1742632904_394949_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997322, 1927, '1528'), 'temp/1742632904_394949_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.79737234, 1927, '1528'), 'temp/1742632904_394949_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.98471916, 1927, '1528'), 'temp/1742632904_394949_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515209': [('987515209', 'Carton', 0.96775526, 1927, '1528'), 'temp/1742632904_394949_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.97343624, 1927, '1528'), 'temp/1742632904_394949_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.9869164, 1927, '1528'), 'temp/1742632904_394949_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.98693246, 1927, '1528'), 'temp/1742632904_394949_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99391127, 1927, '1528'), 'temp/1742632904_394949_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.97741985, 1927, '1528'), 'temp/1742632904_394949_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5297338, 1927, '1528'), 'temp/1742632904_394949_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515249': [('987515249', 'Carton', 0.981307, 1927, '1528'), 'temp/1742632904_394949_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9807726, 1927, '1528'), 'temp/1742632904_394949_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.99981385, 1927, '1528'), 'temp/1742632904_394949_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.99981385, 1927, '1528'), 'temp/1742632904_394949_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97703594, 1927, '1528'), 'temp/1742632904_394949_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.85690427, 1927, '1528'), 'temp/1742632904_394949_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9272334, 1927, '1528'), 'temp/1742632904_394949_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg']} Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1879 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1879 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1879 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1879 # 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 ! no param json to modify List Step Type Loaded in datou : detect_points list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (987515173) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 987515173 download finish for photo 987515173 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.07748293876647949 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:detect_points Sat Mar 22 09:42:50 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632970_394949_987515173_91fa471b1a04f95b356afdbaf021f623.jpg': 987515173} map_photo_id_path_extension : {987515173: {'path': 'temp/1742632970_394949_987515173_91fa471b1a04f95b356afdbaf021f623.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step predict points ! Inside try reload ! classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus'] pht : 1927 model_name : learn_refus_upm_blanches_1924 {'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'} 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/1742632970_394949_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 : 0.0413365364074707 time to do a prediction : 0.23475885391235352 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) After datou_step_exec type output : time spend for datou_step_exec : 1.682819128036499 time spend to save output : 3.552436828613281e-05 total time spend for step 1 : 1.6828546524047852 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, 6.2313227494614765e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.435133518441379e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0618297352493755e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.437910661181377e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9191829778719693e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.775317964027636e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.0001226742606377229), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9550697945524007e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.3744448185425426e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2062582871740233e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.379721084049379e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4713019709233777e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.128473650169326e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015809998149052262), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004430181288626045), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.521141767734662e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3333612969290698e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.626459834369598e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5288427423220128e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6146962025231915e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.262485840125009e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.652979886392131e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.00032724920311011374), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003061564057134092), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.852282730396837e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.92454829934286e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.70529963017907e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.7961772755370475e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.340572973480448e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.7021600797306746e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.540646386885783e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.804256140138023e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.121240858192323e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6448344695163541e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.9623280422820244e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4362558431457728e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.883072612457909e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2900499314127956e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.3367798399413e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1665155145456083e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.8297271203191485e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.550789076849469e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.9631139568664366e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.746271775322384e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.317988441471243e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.174542017513886e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.1465075658634305e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.918429501936771e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2082057764928322e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.5576255464111455e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.341852061566897e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.00015451553917955607), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.00026153141516260803), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.00018771829491015524), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.00021273022866807878), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.0001651205529924482), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.540452209766954e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.754162622091826e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.9335987569065765e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.00012101572792744264), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.0001958321954589337), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.0001878085604403168), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.00012363515270408243), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.0002724352234508842), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.573991426084831e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.0371587601839565e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 7.014282346062828e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.0008703666389919817), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.0026407463010400534), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.003376294858753681), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.03143041580915451), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.05596832558512688), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.00012417040125001222), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020863530517090112), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.00036818275111727417), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.00683168601244688), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.015898142009973526), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.00941441673785448), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.009760956279933453), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.0220535509288311), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.02190200239419937), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.19446861743927002), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.09648864716291428), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.12402499467134476), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.5329127907752991), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.4611577093601227), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.7719809412956238), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8667639493942261), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8496628999710083), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.9843323230743408), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9847413897514343), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919723272323608), (987515173, 1982, 'Carton', 240, -1, 208, -1, 0.9993777275085449), (987515173, 1982, 'Carton', 272, -1, 208, -1, 0.9994128942489624), (987515173, 1982, 'Carton', 304, -1, 208, -1, 0.9995875954627991), (987515173, 1982, 'Carton', 336, -1, 208, -1, 0.9992266893386841), (987515173, 1982, 'Carton', 112, -1, 240, -1, 0.9279059171676636), (987515173, 1982, 'Carton', 144, -1, 240, -1, 0.9810532927513123), (987515173, 1982, 'Carton', 176, -1, 240, -1, 0.9662535190582275), (987515173, 1982, 'Carton', 208, -1, 240, -1, 0.9677583575248718), (987515173, 1982, 'Carton', 240, -1, 240, -1, 0.9963585734367371), (987515173, 1982, 'Carton', 272, -1, 240, -1, 0.9994193315505981), (987515173, 1982, 'Carton', 304, -1, 240, -1, 0.9997850060462952), (987515173, 1982, 'Carton', 336, -1, 240, -1, 0.9996689558029175), (987515173, 1982, 'Carton', 112, -1, 272, -1, 0.9895156621932983), (987515173, 1982, 'Carton', 144, -1, 272, -1, 0.9954884648323059), (987515173, 1982, 'Carton', 176, -1, 272, -1, 0.9855496287345886), (987515173, 1982, 'Carton', 208, -1, 272, -1, 0.9731168746948242), (987515173, 1982, 'Carton', 240, -1, 272, -1, 0.9974668025970459), (987515173, 1982, 'Carton', 272, -1, 272, -1, 0.9992043375968933), (987515173, 1982, 'Carton', 304, -1, 272, 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'autre_refus', 208, -1, 336, -1, 0.0002371389273321256), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010680152627173811), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.611469431547448e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.00013135088374838233), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007314126123674214)]} ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1848 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1848 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1848 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1848 # 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 ! no param json to modify List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1902940) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 Catched exception ! Connect or reconnect ! We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1902940) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 987321136, 'portfolio_id': 1902940}] map_portfolio_id_photo_id: {1902940: [987321136]} ##### Call download_photos : nb_thread : 5 begin to download photo : 987321136 download finish for photo 987321136 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.5427708625793457 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 7 step1:init_dechet Sat Mar 22 09:42:52 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} debut step init detect dechets input : temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [987321136] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : False mtd_id 1848 list_pids : [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 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 : [('1848', '1902940', '987321136', 'None', None, None, None, None, None)] time used for this insertion : 0.03102707862854004 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.0001628398895263672 time spend to save output : 0.03127694129943848 total time spend for step 1 : 0.031439781188964844 step2:tile Sat Mar 22 09:42:52 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 : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 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 input_args_next_step, len :1, first value : ('temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136} map_photo_id_path_extension : {987321136: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {0: 987321136} verbose : True 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/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 987321136,987321136,987321136) and `type` in (406) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_correct_upm&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 21607091 with name tile_correct_upm feed_id_new_photos : 21607091 filename : temp/1742632972_394949_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)] calcul des nouveaux crops pour le tile x0:0,x1:562,y0:0,y1:439 chi selectionnes : [] new_crops_tiles : 1 crop_transformed : 0 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [(987321136, 2090988864, 1522, 0, 562, 0, 439, 1.0)] list_photo_ids_cropped : [987321136] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 987321136) and `type` in (1522) Loaded 1 chid ids of type : 1522 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1608847328) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1608847328) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1608847328) treat the image : temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.010667800903320312 About to upload 1 photos upload in portfolio : 21607091 Result OK ! uploaded one batch 0 Elapsed time : 4.743220090866089 upload mediasElapsed time : 4.753939390182495 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1608847328, 1346373939, 0)] Saving 0 CHIs. list_chi_tile : [] end of tileElapsed time : 4.766739130020142 map_pid_results : {'1346373939': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1346373939'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : False mtd_id 1848 list_pids : [987321136, 987321136, '1346373939'] Looping around the photos to save general results len do output : 1 /1346373939Didn'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, '1346373939', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 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 : [('1848', None, '1346373939', 'None', None, None, None, None, None), ('1848', '1902940', '987321136', None, None, None, None, None, None)] time used for this insertion : 0.011978864669799805 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.699805736541748 time spend to save output : 0.012101411819458008 total time spend for step 2 : 13.711907148361206 step3:detect_points Sat Mar 22 09:43:06 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 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 : {'1346373939': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'1346373939': ()} output_args : {'1346373939': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1346373939 depend.output_id : 0 complete output_args for input 1 : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'1346373939': ('temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',), '987321136': ()} output_args : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 2 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :2, first value : ('temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1346373939} map_photo_id_path_extension : {987321136: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1346373939: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1346373939: 987321136} Beginning of datou step predict points ! Inside try reload ! classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus'] pht : 1927 model_name : learn_refus_upm_blanches_1924 {'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'} 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/1742632972_394949_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 : 0.04578375816345215 time to do a prediction : 14.517213582992554 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) After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True Inside savePoints : final : False verbose : True threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 output flattener 5 example : {1346373939: [(1346373939, 1945, 'Autre_Environement', 185, -1, 118, -1, 1.8337410438107327e-05), (1346373939, 1945, 'Autre_Environement', 320, -1, 118, -1, 0.0001291327498620376), (1346373939, 1945, 'Autre_Environement', 388, -1, 151, -1, 1.3445685908664018e-05), (1346373939, 1945, 'Autre_Environement', 286, -1, 185, -1, 0.00013466527161654085), (1346373939, 1945, 'Autre_Environement', 118, -1, 219, -1, 9.86390750767896e-06), (1346373939, 1945, 'Autre_Environement', 219, -1, 219, -1, 0.00038494516047649086), (1346373939, 1945, 'Autre_Environement', 354, -1, 219, -1, 0.0003431888180784881), (1346373939, 1945, 'Autre_Environement', 421, -1, 253, -1, 1.494663450785083e-07), (1346373939, 1945, 'Autre_Environement', 185, -1, 286, -1, 1.814520658172114e-07), (1346373939, 1945, 'Autre_Environement', 320, -1, 286, -1, 4.115241154067917e-06), (1346373939, 1945, 'Autre_Environement', 118, -1, 320, -1, 2.5274063397695556e-10), (1346373939, 1945, 'Autre_Environement', 253, -1, 320, -1, 1.0447603199237321e-10), (1346373939, 1945, 'Autre_Environement', 388, -1, 320, -1, 4.5192118136583304e-07), (1346373939, 1945, 'Carton', 151, -1, 118, -1, 0.9897055625915527), (1346373939, 1945, 'Carton', 286, -1, 118, -1, 0.8210961222648621), (1346373939, 1945, 'Carton', 421, -1, 118, -1, 0.4851985573768616), (1346373939, 1945, 'Carton', 219, -1, 151, -1, 0.9841372966766357), (1346373939, 1945, 'Carton', 118, -1, 185, -1, 0.9978604912757874), (1346373939, 1945, 'Carton', 185, -1, 219, -1, 0.7996194958686829), (1346373939, 1945, 'Carton', 354, -1, 219, -1, 0.13047637045383453), (1346373939, 1945, 'Carton', 286, -1, 253, -1, 0.05070793256163597), (1346373939, 1945, 'Carton', 118, -1, 286, -1, 0.31989040970802307), (1346373939, 1945, 'Carton', 219, -1, 286, -1, 0.0034983144141733646), (1346373939, 1945, 'Carton', 421, -1, 286, -1, 0.01660206913948059), (1346373939, 1945, 'Carton', 354, -1, 320, -1, 0.0016258988762274384), (1346373939, 1945, 'Kraft', 185, -1, 118, -1, 0.004793279338628054), (1346373939, 1945, 'Kraft', 286, -1, 118, -1, 0.0023909551091492176), (1346373939, 1945, 'Kraft', 421, -1, 118, -1, 0.002102428814396262), (1346373939, 1945, 'Kraft', 354, -1, 151, -1, 0.05212335288524628), (1346373939, 1945, 'Kraft', 118, -1, 185, -1, 0.0017166697653010488), (1346373939, 1945, 'Kraft', 253, -1, 185, -1, 0.04599893465638161), (1346373939, 1945, 'Kraft', 185, -1, 219, -1, 0.021099306643009186), (1346373939, 1945, 'Kraft', 388, -1, 219, -1, 6.061792737455107e-05), (1346373939, 1945, 'Kraft', 286, -1, 253, -1, 0.0037522290367633104), (1346373939, 1945, 'Kraft', 118, -1, 286, -1, 0.010319208726286888), (1346373939, 1945, 'Kraft', 219, -1, 286, -1, 5.351284926291555e-05), (1346373939, 1945, 'Kraft', 421, -1, 286, -1, 9.467339623370208e-06), (1346373939, 1945, 'Kraft', 320, -1, 320, -1, 0.00017124204896390438), (1346373939, 1945, 'Lointain_Papier_Magazine', 185, -1, 118, -1, 2.352082447032444e-06), (1346373939, 1945, 'Lointain_Papier_Magazine', 320, -1, 118, -1, 1.991412864299491e-05), (1346373939, 1945, 'Lointain_Papier_Magazine', 253, -1, 151, -1, 1.1851832823595032e-05), (1346373939, 1945, 'Lointain_Papier_Magazine', 354, -1, 185, -1, 8.877575601218268e-05), (1346373939, 1945, 'Lointain_Papier_Magazine', 118, -1, 219, -1, 2.1015976017224602e-06), (1346373939, 1945, 'Lointain_Papier_Magazine', 219, -1, 219, -1, 7.86672972026281e-05), (1346373939, 1945, 'Lointain_Papier_Magazine', 421, -1, 219, -1, 5.019426794206083e-07), (1346373939, 1945, 'Lointain_Papier_Magazine', 320, -1, 253, -1, 8.220934978453442e-05), (1346373939, 1945, 'Lointain_Papier_Magazine', 185, -1, 286, -1, 3.6812457437918056e-07), (1346373939, 1945, 'Lointain_Papier_Magazine', 388, -1, 286, -1, 3.3782250739022857e-06), (1346373939, 1945, 'Lointain_Papier_Magazine', 118, -1, 320, -1, 3.5824958555252806e-09), (1346373939, 1945, 'Lointain_Papier_Magazine', 286, -1, 320, -1, 1.2866975396264024e-07), (1346373939, 1945, 'Metal', 185, -1, 118, -1, 7.473808364011347e-05), (1346373939, 1945, 'Metal', 286, -1, 118, -1, 3.432366065680981e-05), (1346373939, 1945, 'Metal', 118, -1, 151, -1, 1.1519473446242046e-06), (1346373939, 1945, 'Metal', 354, -1, 151, -1, 0.001217490527778864), (1346373939, 1945, 'Metal', 253, -1, 185, -1, 0.001836584648117423), (1346373939, 1945, 'Metal', 421, -1, 185, -1, 3.084278432652354e-05), (1346373939, 1945, 'Metal', 185, -1, 219, -1, 0.0011127882171422243), (1346373939, 1945, 'Metal', 118, -1, 253, -1, 2.7215228328714147e-05), (1346373939, 1945, 'Metal', 354, -1, 253, -1, 0.0005037166411057115), (1346373939, 1945, 'Metal', 219, -1, 286, -1, 1.657771281315945e-05), (1346373939, 1945, 'Metal', 421, -1, 286, -1, 2.2439740860136226e-05), (1346373939, 1945, 'Metal', 151, -1, 320, -1, 1.393576087860282e-10), (1346373939, 1945, 'Metal', 320, -1, 320, -1, 3.1560623028781265e-05), (1346373939, 1945, 'Papier_Magazine', 118, -1, 118, -1, 0.001665871823206544), (1346373939, 1945, 'Papier_Magazine', 253, -1, 118, -1, 0.28050497174263), (1346373939, 1945, 'Papier_Magazine', 185, -1, 151, -1, 0.003942570183426142), (1346373939, 1945, 'Papier_Magazine', 421, -1, 151, -1, 0.9828073978424072), (1346373939, 1945, 'Papier_Magazine', 354, -1, 185, -1, 0.7690255045890808), (1346373939, 1945, 'Papier_Magazine', 286, -1, 219, -1, 0.9288092851638794), (1346373939, 1945, 'Papier_Magazine', 118, -1, 253, -1, 0.04313919320702553), (1346373939, 1945, 'Papier_Magazine', 219, -1, 253, -1, 0.8978631496429443), (1346373939, 1945, 'Papier_Magazine', 388, -1, 253, -1, 0.9886879324913025), (1346373939, 1945, 'Papier_Magazine', 151, -1, 320, -1, 0.9999996423721313), (1346373939, 1945, 'Papier_Magazine', 253, -1, 320, -1, 0.9999960660934448), (1346373939, 1945, 'Papier_Magazine', 354, -1, 320, -1, 0.9942159056663513), (1346373939, 1945, 'Plastique', 118, -1, 118, -1, 1.236031312146224e-05), (1346373939, 1945, 'Plastique', 219, -1, 118, -1, 0.00030184732167981565), (1346373939, 1945, 'Plastique', 320, -1, 118, -1, 0.0002493929350748658), (1346373939, 1945, 'Plastique', 253, -1, 185, -1, 0.007031592074781656), (1346373939, 1945, 'Plastique', 354, -1, 185, -1, 0.032503753900527954), (1346373939, 1945, 'Plastique', 185, -1, 219, -1, 0.050375860184431076), (1346373939, 1945, 'Plastique', 421, -1, 219, -1, 0.00012226666149217635), (1346373939, 1945, 'Plastique', 118, -1, 253, -1, 0.003930176142603159), (1346373939, 1945, 'Plastique', 286, -1, 253, -1, 0.0025490771513432264), (1346373939, 1945, 'Plastique', 219, -1, 286, -1, 6.120907346485183e-05), (1346373939, 1945, 'Plastique', 354, -1, 286, -1, 0.00538312504068017), (1346373939, 1945, 'Plastique', 151, -1, 320, -1, 1.8926894773674263e-10), (1346373939, 1945, 'Plastique', 421, -1, 320, -1, 0.00020204381144139916), (1346373939, 1945, 'Sol_Environement', 185, -1, 118, -1, 9.372543900099117e-06), (1346373939, 1945, 'Sol_Environement', 320, -1, 118, -1, 2.7338848667568527e-05), (1346373939, 1945, 'Sol_Environement', 118, -1, 151, -1, 2.5881729470711434e-07), (1346373939, 1945, 'Sol_Environement', 253, -1, 185, -1, 0.00011454988998593763), (1346373939, 1945, 'Sol_Environement', 354, -1, 185, -1, 0.00020838991622440517), (1346373939, 1945, 'Sol_Environement', 185, -1, 219, -1, 9.349620813736692e-05), (1346373939, 1945, 'Sol_Environement', 421, -1, 219, -1, 1.1348908657282664e-07), (1346373939, 1945, 'Sol_Environement', 118, -1, 253, -1, 7.004572921687213e-07), (1346373939, 1945, 'Sol_Environement', 320, -1, 253, -1, 5.724640504922718e-05), (1346373939, 1945, 'Sol_Environement', 219, -1, 286, -1, 3.869550369017816e-08), (1346373939, 1945, 'Sol_Environement', 388, -1, 286, -1, 6.792529802623903e-06), (1346373939, 1945, 'Sol_Environement', 151, -1, 320, -1, 2.6225014184994705e-14), (1346373939, 1945, 'Sol_Environement', 286, -1, 320, -1, 1.5886031690115487e-07), (1346373939, 1945, 'Teint_Dans_La_Masse', 185, -1, 118, -1, 0.002272234996780753), (1346373939, 1945, 'Teint_Dans_La_Masse', 286, -1, 118, -1, 0.001813237671740353), (1346373939, 1945, 'Teint_Dans_La_Masse', 388, -1, 118, -1, 0.04538803920149803), (1346373939, 1945, 'Teint_Dans_La_Masse', 118, -1, 151, -1, 0.00010940170614048839), (1346373939, 1945, 'Teint_Dans_La_Masse', 253, -1, 185, -1, 0.0056123328395187855), (1346373939, 1945, 'Teint_Dans_La_Masse', 354, -1, 185, -1, 0.15166763961315155), (1346373939, 1945, 'Teint_Dans_La_Masse', 185, -1, 219, -1, 0.0013750138459727168), (1346373939, 1945, 'Teint_Dans_La_Masse', 118, -1, 253, -1, 6.252125604078174e-05), (1346373939, 1945, 'Teint_Dans_La_Masse', 286, -1, 253, -1, 0.0018639108166098595), (1346373939, 1945, 'Teint_Dans_La_Masse', 388, -1, 253, -1, 0.001438076258637011), (1346373939, 1945, 'Teint_Dans_La_Masse', 219, -1, 286, -1, 1.016586884361459e-05), (1346373939, 1945, 'Teint_Dans_La_Masse', 151, -1, 320, -1, 3.425693932967988e-07), (1346373939, 1945, 'Teint_Dans_La_Masse', 320, -1, 320, -1, 0.0020001486409455538), (1346373939, 1945, 'Teint_Dans_La_Masse', 421, -1, 320, -1, 8.311669807881117e-05), (1346373939, 1945, 'autre_refus', 185, -1, 118, -1, 0.028516046702861786), (1346373939, 1945, 'autre_refus', 354, -1, 118, -1, 0.0014720888575538993), (1346373939, 1945, 'autre_refus', 118, -1, 151, -1, 3.824138184427284e-05), (1346373939, 1945, 'autre_refus', 253, -1, 185, -1, 0.07516990602016449), (1346373939, 1945, 'autre_refus', 185, -1, 219, -1, 0.010234796442091465), (1346373939, 1945, 'autre_refus', 354, -1, 219, -1, 0.04460597038269043), (1346373939, 1945, 'autre_refus', 118, -1, 253, -1, 0.00015751634782645851), (1346373939, 1945, 'autre_refus', 286, -1, 253, -1, 0.01393966656178236), (1346373939, 1945, 'autre_refus', 219, -1, 286, -1, 4.3672833271557465e-05), (1346373939, 1945, 'autre_refus', 388, -1, 286, -1, 0.0030952864326536655), (1346373939, 1945, 'autre_refus', 151, -1, 320, -1, 1.5080686699420198e-10), (1346373939, 1945, 'autre_refus', 320, -1, 320, -1, 0.0030849112663418055)]} hashtag or score ? = 0.9897055625915527 hashtag or score ? = 0.8210961222648621 hashtag or score ? = 0.4851985573768616 hashtag or score ? = 0.9841372966766357 hashtag or score ? = 0.9978604912757874 hashtag or score ? = 0.7996194958686829 hashtag or score ? = 0.13047637045383453 hashtag or score ? = 0.05070793256163597 hashtag or score ? = 0.31989040970802307 hashtag or score ? = 0.05212335288524628 hashtag or score ? = 0.28050497174263 hashtag or score ? = 0.9828073978424072 hashtag or score ? = 0.7690255045890808 hashtag or score ? = 0.9288092851638794 hashtag or score ? = 0.8978631496429443 hashtag or score ? = 0.9886879324913025 hashtag or score ? = 0.9999996423721313 hashtag or score ? = 0.9999960660934448 hashtag or score ? = 0.9942159056663513 hashtag or score ? = 0.050375860184431076 hashtag or score ? = 0.15166763961315155 hashtag or score ? = 0.07516990602016449 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [('1346373939', '492774966', '1945', '151', '-1', '118', '-1', '0.9897055625915527'), ('1346373939', '492774966', '1945', '286', '-1', '118', '-1', '0.8210961222648621'), ('1346373939', '492774966', '1945', '421', '-1', '118', '-1', '0.4851985573768616'), ('1346373939', '492774966', '1945', '219', '-1', '151', '-1', '0.9841372966766357'), ('1346373939', '492774966', '1945', '118', '-1', '185', '-1', '0.9978604912757874'), ('1346373939', '492774966', '1945', '185', '-1', '219', '-1', '0.7996194958686829'), ('1346373939', '492774966', '1945', '354', '-1', '219', '-1', '0.13047637045383453'), ('1346373939', '492774966', '1945', '286', '-1', '253', '-1', '0.05070793256163597'), ('1346373939', '492774966', '1945', '118', '-1', '286', '-1', '0.31989040970802307'), ('1346373939', '493202403', '1945', '354', '-1', '151', '-1', '0.05212335288524628'), ('1346373939', '2107752386', '1945', '253', '-1', '118', '-1', '0.28050497174263'), ('1346373939', '2107752386', '1945', '421', '-1', '151', '-1', '0.9828073978424072'), ('1346373939', '2107752386', '1945', '354', '-1', '185', '-1', '0.7690255045890808'), ('1346373939', '2107752386', '1945', '286', '-1', '219', '-1', '0.9288092851638794'), ('1346373939', '2107752386', '1945', '219', '-1', '253', '-1', '0.8978631496429443'), ('1346373939', '2107752386', '1945', '388', '-1', '253', '-1', '0.9886879324913025'), ('1346373939', '2107752386', '1945', '151', '-1', '320', '-1', '0.9999996423721313'), ('1346373939', '2107752386', '1945', '253', '-1', '320', '-1', '0.9999960660934448'), ('1346373939', '2107752386', '1945', '354', '-1', '320', '-1', '0.9942159056663513'), ('1346373939', '492725882', '1945', '185', '-1', '219', '-1', '0.050375860184431076'), ('1346373939', '2107752385', '1945', '354', '-1', '185', '-1', '0.15166763961315155'), ('1346373939', '2107752406', '1945', '253', '-1', '185', '-1', '0.07516990602016449')] final : False save missing photos in datou_result : time spend for datou_step_exec : 15.59443998336792 time spend to save output : 0.5259437561035156 total time spend for step 3 : 16.120383739471436 step4:count_percent_refus Sat Mar 22 09:43:22 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 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 : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 1 complete output_args for input 1 : {'1346373939': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} input_args_next_step : {'987321136': (987321136,), '1346373939': ()} output_args : {'1346373939': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg']} args : 1346373939 depend.output_id : 0 complete output_args for input 2 : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': (987321136,), '1346373939': ('temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} output_args : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 2 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :2, first value : (987321136, 0.9481481481481482) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1346373939} map_photo_id_path_extension : {987321136: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1346373939: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1346373939: 987321136} debut step count percent refus args : {'987321136': (987321136, 0.9481481481481482), '1346373939': ('temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',)} (987321136, 0.9481481481481482) ('temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) on trouve le portfolio_id = 1902940 list_photo : [987321136] list_photo_correc : [1346373939] 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 After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True map_info[mapportfolio_photo] : {1902940: [987321136]} dans le for photo id : 987321136 output[photo_id] : [({'carton': 3, 'Papier_Magazine': 7}, [1346373939], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,30.0,'refus_total',1945) on duplicate key update value= 30.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,61.64383561643836,'refus_total',1945) on duplicate key update value= 61.64383561643836 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,30.0,'carton',1945) on duplicate key update value= 30.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,61.64383561643836,'carton',1945) on duplicate key update value= 61.64383561643836 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_surface',1902940,70.0,'Papier_Magazine',1945) on duplicate key update value= 70.0 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945], ['0', 'qualipapia_surface', 1902940, 70.0, 'Papier_Magazine', 1945]] insert into MTRLabel.upm_carac (ol,type_carac,portfolio_id,value,material,hashtag_type) values (0,'qualipapia_gravi',1902940,38.35616438356164,'Papier_Magazine',1945) on duplicate key update value= 38.35616438356164 list_values : [['0', 'qualipapia_surface', 1902940, 30.0, 'refus_total', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'refus_total', 1945], ['0', 'qualipapia_surface', 1902940, 30.0, 'carton', 1945], ['0', 'qualipapia_gravi', 1902940, 61.64383561643836, 'carton', 1945], ['0', 'qualipapia_surface', 1902940, 70.0, 'Papier_Magazine', 1945], ['0', 'qualipapia_gravi', 1902940, 38.35616438356164, 'Papier_Magazine', 1945]] time used for this insertion : 1.571315050125122 save missing photos in datou_result : time spend for datou_step_exec : 0.4690971374511719 time spend to save output : 1.571587085723877 total time spend for step 4 : 2.040684223175049 step5:brightness Sat Mar 22 09:43:24 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 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 : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1346373939} map_photo_id_path_extension : {987321136: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1346373939: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1346373939: 987321136} inside step calcul brightness treat image : temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg pour la photo_id : -0.39870825574700136, le score de luminosite est de 987321136 brightness_score : {987321136: [(987321136, -0.39870825574700136, 496442774)]} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True select photo_hashtag_type from MTRDatou.classification_theme where id = 1154 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.020811080932617188 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('987321136', '496442774', '1426') ... last line : ('987321136', '496442774', '1426') time used for this insertion : 1.3825957775115967 save missing photos in datou_result : time spend for datou_step_exec : 1.2222416400909424 time spend to save output : 1.4077837467193604 total time spend for step 5 : 2.6300253868103027 step6:blur_detection Sat Mar 22 09:43:27 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 : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} input_args_next_step : {'987321136': ()} output_args : {'987321136': ['temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 987321136, 0.9481481481481482]} args : 987321136 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1346373939} map_photo_id_path_extension : {987321136: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1346373939: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1346373939: 987321136} inside step blur_detection score_blur_detection : {} methode: ratio et variance treat image : temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 score_blur_detection : {987321136: [(987321136, -5.392404060312662, 492609224)]} After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : False verbose : True select photo_hashtag_type from MTRDatou.classification_theme where id = 1055 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.00870966911315918 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('987321136', '492609224', '1294') ... last line : ('987321136', '492609224', '1294') time used for this insertion : 0.2239372730255127 save missing photos in datou_result : time spend for datou_step_exec : 0.3450319766998291 time spend to save output : 0.23697113990783691 total time spend for step 6 : 0.582003116607666 step7:send_mail_dechet Sat Mar 22 09:43:27 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 : {987321136: [(987321136, -5.392404060312662, 492609224)]} input_args_next_step : {987321136: ()} output_args : {987321136: [(987321136, -5.392404060312662, 492609224)]} args : 987321136 depend.output_id : 0 complete output_args for input 1 : {987321136: [(987321136, -0.39870825574700136, 496442774)]} input_args_next_step : {987321136: ((987321136, -5.392404060312662, 492609224),)} output_args : {987321136: [(987321136, -0.39870825574700136, 496442774)]} args : 987321136 depend.output_id : 0 complete output_args for input 2 : {987321136: [({'carton': 3, 'Papier_Magazine': 7}, [1346373939], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)]} input_args_next_step : {987321136: ((987321136, -5.392404060312662, 492609224), (987321136, -0.39870825574700136, 496442774))} output_args : {987321136: [({'carton': 3, 'Papier_Magazine': 7}, [1346373939], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)]} args : 987321136 depend.output_id : 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 input_args_next_step, len :1, first value : ((987321136, -5.392404060312662, 492609224), (987321136, -0.39870825574700136, 496442774), ({'carton': 3, 'Papier_Magazine': 7}, [1346373939], {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 1902940)) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg': 987321136, 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg': 1346373939} map_photo_id_path_extension : {987321136: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea.jpg', 'extension': 'jpg'}, 1346373939: {'path': 'temp/1742632972_394949_987321136_6a08497399a24a3041045c21475a90ea_0.jpg'}} map_subphoto_mainphoto : {0: 987321136, 1346373939: 987321136} dans la step send mail dechet list_name : ['one', 'sample', 'debug', 'board', 'détect', 'port'] corps du mail : La photo est trop sombre et nette, merci de reprendre la photo
Lien affichage photo


Dans ces conditions de prise de photo, les résultats sur le tas sont les suivants :
Le pourcentage de matière impropre est de 61.64 %.

Pour plus de détails:

Teint Dans La Masse: 0%.

carton: 61.64%.

metal: 0%.

plastique: 0%.

senders@fotonower.com retour de l'envoi du mail : None After datou_step_exec type output : map_portfolio_photo : len 1 keys : dict_keys([1902940]) Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1346373939'] map_info['map_portfolio_photo'] : {1902940: [987321136]} final : True mtd_id 1848 list_pids : [987321136, 987321136, '1346373939'] 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, '1346373939', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 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 : [('1848', '1902940', '987321136', "{'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}", None, None, None, None, None), ('1848', None, '1346373939', None, None, None, None, None, None)] time used for this insertion : 0.012440919876098633 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.40802955627441406 time spend to save output : 0.012778520584106445 total time spend for step 7 : 0.4208080768585205 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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1807 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1807 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1807 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1807 # 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 ! no param json to modify List Step Type Loaded in datou : image_temperature_detection list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (984484223) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 984484223 download finish for photo 984484223 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.3692309856414795 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:image_temperature_detection Sat Mar 22 09:43:28 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742633008_394949_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg': 984484223} map_photo_id_path_extension : {984484223: {'path': 'temp/1742633008_394949_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blanche_jaune_detection treat image : temp/1742633008_394949_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 After datou_step_exec type output : time spend for datou_step_exec : 0.6523475646972656 time spend to save output : 7.295608520507812e-05 total time spend for step 1 : 0.6524205207824707 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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4041 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4041 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4041 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4041 # 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 ! no param json to modify List Step Type Loaded in datou : split_time_score list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5205529) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (5205529) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 1064921404, 'portfolio_id': 5205529}, {'photo_id': 1064921402, 'portfolio_id': 5205529}, {'photo_id': 1064921401, 'portfolio_id': 5205529}, {'photo_id': 1064921201, 'portfolio_id': 5205529}, {'photo_id': 1064921196, 'portfolio_id': 5205529}, {'photo_id': 1064919876, 'portfolio_id': 5205529}, {'photo_id': 1064919873, 'portfolio_id': 5205529}, {'photo_id': 1064919869, 'portfolio_id': 5205529}, {'photo_id': 1064919862, 'portfolio_id': 5205529}, {'photo_id': 1064919858, 'portfolio_id': 5205529}, {'photo_id': 1064919856, 'portfolio_id': 5205529}, {'photo_id': 1064919752, 'portfolio_id': 5205529}, {'photo_id': 1064919748, 'portfolio_id': 5205529}, {'photo_id': 1064919745, 'portfolio_id': 5205529}, {'photo_id': 1064919741, 'portfolio_id': 5205529}, {'photo_id': 1064919737, 'portfolio_id': 5205529}, {'photo_id': 1064919730, 'portfolio_id': 5205529}, {'photo_id': 1064919660, 'portfolio_id': 5205529}] map_portfolio_id_photo_id: {5205529: [1064921404, 1064921402, 1064921401, 1064921201, 1064921196, 1064919876, 1064919873, 1064919869, 1064919862, 1064919858, 1064919856, 1064919752, 1064919748, 1064919745, 1064919741, 1064919737, 1064919730, 1064919660]} ##### Call download_photos : nb_thread : 5 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos ##### After load_data_input time to download the photos : 0.7276082038879395 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:split_time_score Sat Mar 22 09:43:30 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 After prepare type args : Here we display some param of map_info ! map_filenames : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} split portfolio by speed calcul order for each photo with time calcul time for a portfolio query : SELECT photo_id, text FROM MTRBack.photos where photo_id in (SELECT mtr_photo_id FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5205529); result : ((1064919660, 'image_01122021_10_11_30_014389.jpg'), (1064919730, 'image_01122021_10_12_17_665202.jpg'), (1064919737, 'image_01122021_10_11_40_031052.jpg'), (1064919741, 'image_01122021_10_11_34_021658.jpg'), (1064919745, 'image_01122021_10_11_32_018001.jpg'), (1064919748, 'image_01122021_10_12_27_027057.jpg'), (1064919752, 'image_01122021_10_12_24_005017.jpg'), (1064919856, 'image_01122021_10_13_13_399843.jpg'), (1064919858, 'image_01122021_10_13_04_729164.jpg'), (1064919862, 'image_01122021_10_12_56_581019.jpg'), (1064919869, 'image_01122021_10_12_29_030603.jpg'), (1064919873, 'image_01122021_10_13_30_005720.jpg'), (1064919876, 'image_01122021_10_13_22_147712.jpg'), (1064921196, 'image_01122021_10_16_18_114975.jpg'), (1064921201, 'image_01122021_10_16_14_925132.jpg'), (1064921401, 'image_01122021_10_16_57_981306.jpg'), (1064921402, 'image_01122021_10_16_53_913663.jpg'), (1064921404, 'image_01122021_10_16_47_889875.jpg')) INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `order`) VALUES (%s, %s, %s) on duplicate key update `order`=VALUES(`order`); first line : (5205529, 1064919660, 1098136690) ... last line : (5205529, 1064921404, 1098137007) 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]]} len of dic_time_useful : 2 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"; select cs_nb_photo / nb_photo, om_nb_photo / nb_photo from (select sum(1) as nb_photo,sum(if (tags= "[CS]",1,0)) as cs_nb_photo, sum(if (tags= "[OM]",1,0)) as om_nb_photo from MTRBack.photos where photo_id in ()) t1; select cs_nb_photo / nb_photo, om_nb_photo / nb_photo from (select sum(1) as nb_photo,sum(if (tags= "[CS]",1,0)) as cs_nb_photo, sum(if (tags= "[OM]",1,0)) as om_nb_photo from MTRBack.photos where photo_id in (1064919660, 1064919745, 1064919741, 1064919737, 1064919730, 1064919752, 1064919748, 1064919869, 1064919862, 1064919858)) t1; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 (21607093, 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) After datou_step_exec type output : time spend for datou_step_exec : 1.854107141494751 time spend to save output : 8.702278137207031e-05 total time spend for step 1 : 1.854194164276123 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(21607093, 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 : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=719 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=719 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 719 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=719 # 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 ! no param json to modify List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT ph.photo_id, ph.url FROM MTRBack.photos ph WHERE ph.photo_id IN (SELECT mtr_photo_id from MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1981316) and hide_status = 0 ) ORDER BY ph.photo_id DESC LIMIT 0, 10000 We have 1 , {} SELECT mtr_photo_id, mtr_portfolio_id FROM MTRUser.mtr_portfolio_photos WHERE mtr_portfolio_id in (1981316) AND hide_status = 0 ORDER by mtr_photo_id desc LIMIT 0, 10000 list_result: [{'photo_id': 950003838, 'portfolio_id': 1981316}, {'photo_id': 950003813, 'portfolio_id': 1981316}, {'photo_id': 950003812, 'portfolio_id': 1981316}, {'photo_id': 950003696, 'portfolio_id': 1981316}, {'photo_id': 950003695, 'portfolio_id': 1981316}, {'photo_id': 926687666, 'portfolio_id': 1981316}] map_portfolio_id_photo_id: {1981316: [950003838, 950003813, 950003812, 950003696, 950003695, 926687666]} ##### Call download_photos : nb_thread : 5 begin to download photo : 950003838 begin to download photo : 950003812 begin to download photo : 950003695 download finish for photo 950003812 begin to download photo : 950003696 download finish for photo 950003838 begin to download photo : 950003813 download finish for photo 950003695 begin to download photo : 926687666 download finish for photo 950003813 download finish for photo 926687666 download finish for photo 950003696 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 ##### After load_data_input time to download the photos : 0.3409135341644287 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:frcnn Sat Mar 22 09:43:32 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1742633012_394949_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg': 950003838, 'temp/1742633012_394949_950003813_e28be02dfcce79cce594a390a9911a0a.jpg': 950003813, 'temp/1742633012_394949_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg': 950003695, 'temp/1742633012_394949_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg': 926687666, 'temp/1742633012_394949_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg': 950003812, 'temp/1742633012_394949_950003696_11e3a77b72af4b332d366d98984039c7.jpg': 950003696} map_photo_id_path_extension : {950003838: {'path': 'temp/1742633012_394949_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg', 'extension': 'jpg'}, 950003813: {'path': 'temp/1742633012_394949_950003813_e28be02dfcce79cce594a390a9911a0a.jpg', 'extension': 'jpg'}, 950003695: {'path': 'temp/1742633012_394949_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg', 'extension': 'jpg'}, 926687666: {'path': 'temp/1742633012_394949_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg', 'extension': 'jpg'}, 950003812: {'path': 'temp/1742633012_394949_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg', 'extension': 'jpg'}, 950003696: {'path': 'temp/1742633012_394949_950003696_11e3a77b72af4b332d366d98984039c7.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Faster rcnn ! Inside try reload ! classes : ['background', 'retroviseur', 'roue', 'capot', 'pare-brise', 'vitre', 'phare', 'feu-antibrouillard', 'feu-arriere', 'poignee', 'porte', 'radiateur', 'logo-marque', 'cache-reservoir', 'plaque-immatriculation', 'pot-echappement', 'info-modele', 'essuie-glace', 'pare-choc', 'coffre', 'carrosserie-autre', 'toit', 'logo-roue', 'aile-avant', 'aile-arriere', 'autre'] pht : 757 caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 685, 'mtr_user_id': 31, 'name': 'learn_piece_voiture_0808_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,retroviseur,roue,capot,pare-brise,vitre,phare,feu-antibrouillard,feu-arriere,poignee,porte,radiateur,logo-marque,cache-reservoir,plaque-immatriculation,pot-echappement,info-modele,essuie-glace,pare-choc,coffre,carrosserie-autre,toit,logo-roue,aile-avant,aile-arriere,autre', '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', 'photo_hashtag_type': 757, 'photo_desc_type': 3800, 'type_classification': 'caffe_faster_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'} 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/1742633012_394949_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 0.193s for 300 object proposals c : aile-arriere list_crops.shape (29, 5) proba : 0.052099865 (104.294205, 64.89295, 245.01991, 262.59283) proba : 0.011473607 (156.0481, 154.27849, 276.97583, 234.77766) proba : 0.010073913 (133.89203, 191.03134, 199.79337, 262.34598) c : aile-avant list_crops.shape (30, 5) proba : 0.16406178 (93.40051, 110.007195, 261.1824, 244.45413) proba : 0.011732342 (153.0839, 215.96217, 224.0026, 288.55353) c : autre list_crops.shape (31, 5) c : cache-reservoir list_crops.shape (30, 5) proba : 0.016790174 (12.162495, 54.52703, 94.04559, 89.23426) c : capot list_crops.shape (24, 5) proba : 0.101483494 (69.29623, 0.0, 280.00443, 54.912666) proba : 0.08461421 (113.76146, 84.138054, 262.94244, 231.19193) proba : 0.040667936 (6.7407074, 36.863075, 96.11422, 92.56632) c : carrosserie-autre list_crops.shape (27, 5) c : coffre list_crops.shape (23, 5) proba : 0.047648937 (53.505318, 0.0, 284.0, 92.37462) proba : 0.032658536 (10.159851, 31.721, 95.70418, 100.61199) proba : 0.012152692 (235.39491, 155.0428, 284.0, 216.00725) c : essuie-glace list_crops.shape (31, 5) proba : 0.8094089 (149.03156, 63.966175, 284.0, 87.61648) proba : 0.034233864 (13.64082, 56.07041, 92.06979, 89.41566) proba : 0.020681217 (234.3033, 175.75804, 284.0, 231.14857) c : feu-antibrouillard list_crops.shape (33, 5) proba : 0.055684708 (27.497028, 50.040955, 86.94731, 89.456696) proba : 0.018112034 (229.45755, 178.31158, 279.93893, 220.47305) proba : 0.010318852 (206.26315, 260.46304, 253.11868, 293.0) c : feu-arriere list_crops.shape (34, 5) proba : 0.06828925 (27.727896, 49.239334, 86.895294, 88.09189) proba : 0.024001127 (231.78539, 177.13173, 279.95978, 219.17116) c : info-modele list_crops.shape (34, 5) proba : 0.015899234 (27.181265, 48.77754, 87.641045, 89.72043) c : logo-marque list_crops.shape (32, 5) proba : 0.010622198 (206.26915, 219.39294, 262.95383, 293.0) c : logo-roue list_crops.shape (30, 5) c : pare-brise list_crops.shape (24, 5) proba : 0.3803873 (84.457245, 0.0, 284.0, 80.63412) proba : 0.06982875 (21.856377, 45.929268, 86.83179, 90.58222) proba : 0.010924934 (162.28836, 212.50822, 227.27826, 293.0) proba : 0.010167414 (238.71918, 156.77783, 284.0, 216.58832) c : pare-choc list_crops.shape (20, 5) proba : 0.1565834 (75.0419, 12.039207, 284.0, 83.46921) proba : 0.03917178 (173.62607, 206.89383, 284.0, 287.12973) proba : 0.029328667 (3.535038, 39.445415, 99.710846, 103.58823) proba : 0.02177713 (236.46063, 158.82742, 284.0, 219.9403) c : phare list_crops.shape (33, 5) proba : 0.29550934 (16.086021, 55.130177, 94.49313, 86.99944) proba : 0.017166164 (85.86814, 17.956604, 272.2671, 68.44019) proba : 0.013772009 (241.58214, 159.23856, 284.0, 213.74612) proba : 0.011600648 (207.11127, 255.55226, 262.26028, 293.0) c : plaque-immatriculation list_crops.shape (37, 5) proba : 0.131033 (235.82143, 174.31938, 284.0, 225.21815) proba : 0.039646972 (30.736107, 49.49195, 81.55885, 87.70542) c : poignee list_crops.shape (27, 5) c : porte list_crops.shape (24, 5) proba : 0.59999746 (23.914173, 0.0, 103.21922, 219.54459) proba : 0.028162187 (2.5909271, 0.0, 48.603813, 196.39322) proba : 0.0121180285 (103.53235, 0.104789734, 235.17151, 252.1565) c : pot-echappement list_crops.shape (30, 5) proba : 0.021319726 (228.94229, 176.45338, 280.0198, 221.25905) proba : 0.013259873 (208.91866, 255.16238, 263.81396, 293.0) proba : 0.013240703 (26.967566, 48.155884, 87.178535, 89.94545) c : radiateur list_crops.shape (30, 5) c : retroviseur list_crops.shape (32, 5) proba : 0.03768717 (14.681686, 54.764538, 94.673386, 89.58716) proba : 0.012232761 (210.45891, 255.86526, 263.11322, 293.0) proba : 0.010144236 (231.19035, 176.605, 279.573, 220.06282) c : roue list_crops.shape (34, 5) proba : 0.8121109 (146.7955, 218.47594, 218.13242, 292.02335) proba : 0.27977204 (135.7751, 224.51305, 169.61766, 293.0) proba : 0.0448568 (29.877934, 37.665012, 91.51692, 83.93808) proba : 0.025317932 (7.3848305, 0.09095001, 44.255356, 34.95914) proba : 0.018804766 (234.26018, 172.02467, 277.3579, 219.31126) proba : 0.011282656 (206.8193, 251.68613, 258.8732, 293.0) c : toit list_crops.shape (29, 5) c : vitre list_crops.shape (32, 5) proba : 0.14842488 (152.66827, 220.61093, 208.9701, 293.0) proba : 0.034730326 (235.49094, 173.76042, 284.0, 228.07857) proba : 0.030417897 (138.0265, 226.14996, 164.98973, 293.0) proba : 0.018849898 (18.039143, 51.715153, 93.8611, 87.637024) We are managing local photo_id image_path : temp/1742633012_394949_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 0.047s for 300 object proposals c : aile-arriere list_crops.shape (29, 5) proba : 0.06769732 (98.06819, 150.88416, 151.93732, 223.72394) proba : 0.04091722 (33.36003, 90.13585, 179.12851, 182.83891) proba : 0.016991708 (79.35173, 122.719925, 111.4948, 230.3658) c : aile-avant list_crops.shape (30, 5) proba : 0.048592452 (43.71638, 17.110481, 139.32396, 207.17874) proba : 0.0100964485 (62.040924, 104.14538, 183.72153, 182.30817) c : autre list_crops.shape (32, 5) c : cache-reservoir list_crops.shape (33, 5) c : capot list_crops.shape (22, 5) proba : 0.096809365 (38.04045, 75.42149, 207.15096, 166.65916) c : carrosserie-autre list_crops.shape (24, 5) c : coffre list_crops.shape (21, 5) proba : 0.06442273 (165.47429, 115.303276, 226.39851, 165.47626) c : essuie-glace list_crops.shape (34, 5) proba : 0.38612443 (88.26863, 10.883581, 179.58339, 31.356184) proba : 0.042400837 (133.08646, 1.8786526, 227.7428, 26.530336) proba : 0.024033226 (169.25412, 117.38367, 218.91489, 165.43307) proba : 0.010850897 (61.55272, 1.9375143, 117.815994, 33.0025) c : feu-antibrouillard list_crops.shape (32, 5) proba : 0.019598668 (168.92406, 117.95294, 219.53026, 165.85583) c : feu-arriere list_crops.shape (30, 5) proba : 0.022235053 (171.0728, 119.6763, 221.0089, 164.19968) c : info-modele list_crops.shape (31, 5) proba : 0.010200979 (168.52164, 116.05177, 219.95319, 166.43349) c : logo-marque list_crops.shape (34, 5) proba : 0.015149827 (154.45753, 207.10858, 180.12541, 244.77005) c : logo-roue list_crops.shape (30, 5) c : pare-brise list_crops.shape (27, 5) proba : 0.047093403 (168.76735, 117.31308, 222.88683, 165.84152) proba : 0.013596504 (53.46753, 0.0, 178.91458, 22.404936) proba : 0.012164057 (15.941336, 0.0, 44.89669, 48.295383) c : pare-choc list_crops.shape (23, 5) proba : 0.08984343 (166.06456, 118.15962, 226.87996, 168.71309) proba : 0.012229571 (108.197174, 130.85834, 228.0, 249.22684) c : phare list_crops.shape (37, 5) proba : 0.013690393 (172.01387, 118.3962, 223.3072, 162.23933) c : plaque-immatriculation list_crops.shape (38, 5) proba : 0.051210556 (173.594, 120.677246, 218.52435, 161.78412) c : poignee list_crops.shape (31, 5) c : porte list_crops.shape (25, 5) proba : 0.039308526 (4.774946, 0.0, 38.134323, 169.71004) proba : 0.014844768 (170.32434, 112.39755, 220.70547, 170.4638) proba : 0.013463212 (19.338303, 0.0, 92.45262, 176.19547) c : pot-echappement list_crops.shape (31, 5) proba : 0.021788057 (168.56726, 115.56238, 219.41544, 166.48584) proba : 0.016472278 (154.52908, 206.32013, 179.71347, 244.10431) c : radiateur list_crops.shape (30, 5) c : retroviseur list_crops.shape (34, 5) proba : 0.010121877 (169.53493, 116.2083, 219.60405, 166.28838) c : roue list_crops.shape (36, 5) proba : 0.1063435 (83.7209, 166.04697, 133.41437, 253.0) proba : 0.050482016 (73.95813, 164.6687, 104.241394, 236.14478) proba : 0.032671195 (18.13758, 0.0, 45.99768, 33.78602) c : toit list_crops.shape (33, 5) c : vitre list_crops.shape (34, 5) proba : 0.028296158 (20.599394, 0.0, 45.493385, 43.055595) proba : 0.015350714 (170.87378, 118.45813, 221.03333, 164.49426) We are managing local photo_id image_path : temp/1742633012_394949_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 0.840s for 300 object proposals c : aile-arriere list_crops.shape (45, 5) proba : 0.01639127 (3266.6943, 1031.889, 3839.0, 1714.3063) proba : 0.016076362 (16.380783, 491.09824, 406.7705, 799.8511) proba : 0.012598097 (1997.6459, 259.47388, 2506.4426, 848.9403) proba : 0.011020271 (51.40178, 1690.1356, 479.9425, 1966.6393) c : aile-avant list_crops.shape (41, 5) c : autre list_crops.shape (46, 5) c : cache-reservoir list_crops.shape (46, 5) c : capot list_crops.shape (38, 5) c : carrosserie-autre list_crops.shape (44, 5) c : coffre list_crops.shape (31, 5) c : essuie-glace list_crops.shape (49, 5) c : feu-antibrouillard list_crops.shape (44, 5) proba : 0.014235662 (3282.5713, 1219.7567, 3793.5635, 1821.4747) proba : 0.012605858 (27.948792, 498.89758, 410.0084, 789.6421) c : feu-arriere list_crops.shape (44, 5) proba : 0.07466202 (7.5950165, 454.2929, 376.2187, 791.58844) proba : 0.02520967 (3284.673, 1151.9417, 3782.7322, 1834.2368) proba : 0.011052324 (17.65567, 1586.6871, 282.00662, 1995.7555) c : info-modele list_crops.shape (44, 5) proba : 0.018715281 (35.89949, 482.71744, 414.19586, 791.46936) c : logo-marque list_crops.shape (46, 5) proba : 0.017158875 (41.252472, 486.3745, 409.73508, 792.72144) c : logo-roue list_crops.shape (45, 5) c : pare-brise list_crops.shape (38, 5) proba : 0.016599989 (24.733688, 0.0, 388.85297, 684.5049) c : pare-choc list_crops.shape (35, 5) c : phare list_crops.shape (48, 5) c : plaque-immatriculation list_crops.shape (47, 5) proba : 0.02555758 (26.2874, 479.65347, 404.917, 771.0763) proba : 0.016563468 (28.250443, 1620.7378, 299.36896, 1974.6025) proba : 0.0146960765 (18.399704, 3.5822144, 386.72815, 255.02417) c : poignee list_crops.shape (43, 5) c : porte list_crops.shape (40, 5) proba : 0.11931283 (1872.761, 10.193359, 2442.9573, 862.2322) proba : 0.114666335 (2136.5103, 52.953766, 2855.9814, 815.28503) proba : 0.024786547 (3234.8318, 69.621796, 3823.1174, 847.8688) proba : 0.0138097545 (109.56137, 1835.4795, 459.84555, 2159.0) proba : 0.011385989 (3315.0127, 1115.5896, 3779.0342, 1883.8884) proba : 0.011383509 (1348.9757, 1049.3466, 1927.9452, 1777.0001) proba : 0.010760019 (2525.6992, 168.76392, 3531.4966, 924.14124) c : pot-echappement list_crops.shape (43, 5) proba : 0.033423744 (5.1141663, 1746.7196, 343.44635, 2019.285) proba : 0.010658678 (117.196304, 1862.9641, 459.0373, 2155.1729) c : radiateur list_crops.shape (43, 5) c : retroviseur list_crops.shape (47, 5) proba : 0.018043054 (3296.5864, 1199.9825, 3788.0176, 1822.6268) proba : 0.013667049 (13.039154, 1748.9769, 340.38647, 2015.7687) proba : 0.012990563 (124.44525, 1867.7474, 456.32147, 2150.5205) c : roue list_crops.shape (45, 5) proba : 0.5839898 (3132.7412, 1107.5527, 3839.0, 1925.355) proba : 0.045891237 (3481.303, 1409.8928, 3814.2898, 1997.3828) proba : 0.037298143 (38.59897, 1751.9761, 339.53278, 2013.6787) proba : 0.029182479 (3244.3757, 40.532623, 3721.5984, 739.2012) proba : 0.018186225 (3167.0586, 383.69916, 3484.2554, 1016.3637) proba : 0.012427977 (229.07083, 0.0, 685.79443, 557.6347) proba : 0.010361094 (2689.2163, 224.94003, 3238.792, 934.2833) c : toit list_crops.shape (48, 5) c : vitre list_crops.shape (44, 5) proba : 0.018342305 (23.036316, 0.0, 379.90848, 299.2677) proba : 0.014216649 (3321.2285, 1194.2938, 3770.2236, 1805.955) We are managing local photo_id image_path : temp/1742633012_394949_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 0.039s for 300 object proposals c : aile-arriere list_crops.shape (32, 5) proba : 0.12729126 (160.85062, 172.84827, 306.19873, 321.3964) proba : 0.011537204 (538.53143, 190.83249, 613.9653, 298.88834) proba : 0.010254307 (197.33165, 160.36787, 490.4745, 311.2705) c : aile-avant list_crops.shape (40, 5) proba : 0.94816434 (161.77986, 149.5339, 330.65143, 343.4251) proba : 0.090283655 (20.760977, 105.88246, 54.774704, 195.62846) proba : 0.022175157 (152.80342, 143.27777, 217.16756, 306.26105) proba : 0.013756684 (320.70218, 330.9084, 439.3284, 414.445) proba : 0.011391525 (557.76636, 199.6687, 608.66675, 270.50757) c : autre list_crops.shape (36, 5) proba : 0.01142755 (458.2693, 15.298534, 521.1452, 98.994125) c : cache-reservoir list_crops.shape (37, 5) proba : 0.029751362 (470.56494, 21.579445, 531.46594, 89.20028) proba : 0.025542991 (368.27142, 265.86075, 446.8955, 331.27176) proba : 0.014927454 (353.33218, 360.48843, 412.49075, 423.1279) proba : 0.014713522 (451.11176, 57.613037, 523.1583, 125.93219) c : capot list_crops.shape (33, 5) proba : 0.99442756 (211.28055, 115.143105, 555.7743, 300.7413) proba : 0.23367046 (65.79217, 24.148476, 285.73343, 46.316513) proba : 0.03485191 (343.23785, 279.90906, 508.20898, 431.9209) proba : 0.017472818 (89.42647, 37.516594, 357.51447, 63.628548) proba : 0.010150346 (424.37234, 246.66425, 564.61755, 357.00665) c : carrosserie-autre list_crops.shape (32, 5) proba : 0.028547011 (454.54352, 25.081852, 523.5226, 121.41234) c : coffre list_crops.shape (31, 5) proba : 0.12965024 (446.42935, 19.302433, 528.3403, 117.91087) proba : 0.10255577 (139.89584, 45.9821, 419.52512, 192.66527) proba : 0.030859616 (269.29846, 161.99988, 553.941, 348.9406) c : essuie-glace list_crops.shape (40, 5) proba : 0.78950596 (203.86444, 108.23114, 416.75745, 147.35738) proba : 0.14470804 (369.05984, 267.53445, 445.0303, 332.9631) proba : 0.097936586 (391.0584, 261.18723, 527.01215, 318.1299) proba : 0.07720405 (470.4765, 22.205864, 532.0749, 88.02194) proba : 0.035417195 (354.38586, 360.3584, 414.2066, 422.9743) proba : 0.0141495885 (114.8482, 22.668962, 335.3676, 42.908997) proba : 0.012226297 (254.5793, 94.72179, 456.8189, 288.5595) proba : 0.010930176 (353.83875, 285.56598, 484.79016, 414.69592) c : feu-antibrouillard list_crops.shape (39, 5) proba : 0.21195422 (341.78577, 363.48444, 404.85065, 430.65588) proba : 0.055866804 (368.66, 268.63174, 446.43973, 330.09683) proba : 0.025237141 (470.4271, 21.903881, 531.3961, 89.25662) proba : 0.023559622 (389.44647, 264.0462, 529.78723, 316.9591) proba : 0.016471708 (451.4879, 58.84227, 522.9112, 125.20819) proba : 0.01056217 (467.00607, 343.54993, 573.76373, 420.20197) c : feu-arriere list_crops.shape (39, 5) proba : 0.10164389 (329.66742, 259.26016, 456.71344, 315.55087) proba : 0.04224822 (454.30328, 28.763409, 522.9126, 104.09885) proba : 0.026066594 (328.7135, 347.4895, 400.09326, 430.73694) proba : 0.010687887 (475.56412, 341.53906, 575.4429, 420.15186) c : info-modele list_crops.shape (37, 5) proba : 0.070467286 (470.4875, 21.81002, 532.04333, 89.572205) proba : 0.038773697 (353.8294, 361.5401, 412.99805, 422.85016) proba : 0.033351664 (368.70554, 266.8175, 448.23825, 331.63403) proba : 0.023050895 (450.842, 57.47313, 524.0707, 126.34108) proba : 0.015795352 (390.1405, 260.85037, 533.1684, 317.5339) c : logo-marque list_crops.shape (39, 5) proba : 0.11936858 (352.96957, 361.31326, 412.53342, 422.69803) proba : 0.032975007 (470.5547, 20.77438, 530.95984, 88.80145) proba : 0.0275123 (370.81152, 264.58826, 447.20288, 329.64575) proba : 0.025021931 (397.47125, 260.8251, 526.3074, 316.55093) proba : 0.0143313045 (584.15436, 0.7487297, 639.0, 70.73724) c : logo-roue list_crops.shape (37, 5) proba : 0.014609324 (353.32312, 360.72803, 413.2494, 423.60382) proba : 0.013223884 (470.3426, 21.596401, 532.0661, 89.5809) c : pare-brise list_crops.shape (34, 5) proba : 0.95698076 (141.09764, 42.465527, 444.09436, 147.77246) proba : 0.10513277 (319.45435, 22.013645, 424.0318, 129.1308) proba : 0.066548675 (453.85437, 19.65987, 523.2717, 91.54709) proba : 0.050993636 (287.19843, 164.06952, 547.32965, 296.7527) proba : 0.023291534 (95.59755, 40.148018, 158.49245, 162.53934) c : pare-choc list_crops.shape (29, 5) proba : 0.94537294 (272.89954, 257.50266, 580.2368, 444.44437) proba : 0.21969652 (233.48947, 224.184, 397.76407, 411.54773) proba : 0.028825564 (487.97653, 17.563778, 613.4255, 120.57503) proba : 0.02874667 (435.96033, 307.78397, 588.67847, 426.57693) c : phare list_crops.shape (38, 5) proba : 0.7776486 (326.84048, 251.89035, 477.77225, 312.8618) proba : 0.076981306 (328.22125, 359.63895, 410.24036, 426.96506) proba : 0.038822815 (292.80914, 225.13931, 466.74225, 392.34323) proba : 0.03309761 (538.23706, 197.21198, 600.2156, 303.44156) proba : 0.02604663 (466.6488, 20.835537, 531.7367, 84.118164) proba : 0.018856956 (305.61176, 209.4744, 597.71295, 307.11655) proba : 0.014258441 (478.92032, 344.1277, 578.6284, 416.75256) proba : 0.011290124 (96.669205, 66.98646, 163.91179, 147.69226) c : plaque-immatriculation list_crops.shape (38, 5) proba : 0.23008303 (518.5546, 294.16547, 582.3371, 390.75677) proba : 0.033770263 (438.43854, 271.7572, 568.4196, 386.56274) proba : 0.025870012 (347.18265, 259.7795, 452.8752, 315.94504) proba : 0.02114574 (470.38733, 23.617558, 531.61163, 85.379) c : poignee list_crops.shape (35, 5) proba : 0.05438654 (583.3559, 0.030952454, 639.0, 70.986664) proba : 0.034036487 (470.45178, 21.593555, 531.67535, 89.773926) proba : 0.026380323 (353.17163, 360.9572, 412.8966, 423.59692) proba : 0.02448525 (368.05222, 266.1263, 448.05087, 332.2728) c : porte list_crops.shape (28, 5) proba : 0.98540974 (78.16678, 43.07759, 169.65433, 306.4463) proba : 0.9672474 (33.644035, 44.181007, 90.647514, 241.28815) proba : 0.091555186 (452.36923, 19.605556, 521.0901, 113.536896) proba : 0.034177158 (158.70976, 46.13871, 421.33734, 212.15509) proba : 0.012428192 (436.96243, 223.37231, 575.0604, 403.33197) c : pot-echappement list_crops.shape (37, 5) proba : 0.05554057 (353.822, 360.70123, 412.23337, 423.33594) proba : 0.018790234 (458.0542, 14.54607, 521.22156, 99.67154) proba : 0.012939763 (584.3931, 0.0, 639.0, 71.10156) proba : 0.011321952 (466.145, 340.33972, 575.4008, 420.8783) c : radiateur list_crops.shape (38, 5) c : retroviseur list_crops.shape (39, 5) proba : 0.23813692 (452.17715, 56.800083, 522.1675, 124.12628) proba : 0.20781365 (471.15164, 21.535519, 531.2215, 89.27197) proba : 0.19335021 (369.90015, 266.02444, 447.44183, 332.5919) proba : 0.045616228 (342.4961, 362.45914, 404.12885, 431.03128) proba : 0.02658111 (176.9209, 115.94914, 409.85095, 150.13234) proba : 0.021463126 (584.38135, 0.0, 639.0, 70.69029) proba : 0.013623277 (392.94092, 259.8842, 530.6079, 318.6436) proba : 0.010495495 (101.94346, 73.787384, 164.67862, 158.12752) c : roue list_crops.shape (38, 5) proba : 0.94178015 (187.78427, 271.2489, 306.51154, 426.32574) proba : 0.18931317 (326.8299, 347.06247, 401.29364, 429.2875) proba : 0.04978404 (538.71155, 250.99658, 599.9929, 395.27728) proba : 0.037392605 (12.769388, 136.75452, 64.161644, 244.91663) proba : 0.03480667 (549.30615, 192.38683, 605.7953, 305.7131) proba : 0.015743013 (479.68573, 335.9145, 572.4792, 421.26508) c : toit list_crops.shape (34, 5) proba : 0.79197335 (83.63614, 31.277699, 341.3947, 55.13609) c : vitre list_crops.shape (37, 5) proba : 0.98047113 (95.830666, 50.274937, 162.35364, 142.9744) proba : 0.84251505 (42.275906, 43.05231, 91.28252, 114.96591) proba : 0.07669103 (277.41415, 28.77285, 404.65933, 139.55402) proba : 0.06448284 (455.88428, 20.459751, 518.8428, 90.14493) proba : 0.027514974 (148.30228, 51.204983, 356.01483, 112.50844) proba : 0.0131774675 (336.1947, 352.38443, 408.21844, 424.23087) proba : 0.012460983 (364.92773, 257.47162, 445.5205, 324.04456) We are managing local photo_id image_path : temp/1742633012_394949_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 0.043s for 300 object proposals c : aile-arriere list_crops.shape (30, 5) proba : 0.13238022 (133.29857, 172.39215, 280.82587, 321.49384) proba : 0.013275377 (153.19302, 232.5068, 369.7632, 359.7828) c : aile-avant list_crops.shape (35, 5) proba : 0.95040905 (133.9696, 146.1626, 305.77988, 344.49045) proba : 0.020750716 (128.14972, 143.01282, 188.90247, 305.03403) proba : 0.014926352 (294.24286, 331.8387, 412.49933, 415.33066) proba : 0.011201129 (533.0571, 198.27457, 583.4977, 268.45676) proba : 0.010003364 (0.0, 114.20593, 47.59384, 234.00284) c : autre list_crops.shape (36, 5) c : cache-reservoir out of memory invalid argument an illegal memory access was encountered an illegal memory access was encountered WARNING: Logging before InitGoogleLogging() is written to STDERR F0322 09:43:36.552268 394949 math_functions.cu:79] Check failed: error == cudaSuccess (700 vs. 0) an illegal memory access was encountered *** Check failure stack trace: *** Command terminated by signal 6 86.83user 46.55system 8:11.84elapsed 27%CPU (0avgtext+0avgdata 6500004maxresident)k 6035696inputs+44112outputs (5912major+6330583minor)pagefaults 0swaps