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 : 10149 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 : 1.0038585662841797 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Sat Mar 8 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 : 10149 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 /home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp 2025-03-08 09:35:31.336135: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-08 09:35:31.345256: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-08 09:35:31.346823: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f4e00000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-08 09:35:31.346910: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-08 09:35:31.350233: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-08 09:35:31.602095: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x40663da0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-08 09:35:31.602147: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-08 09:35:31.603591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:35:31.604025: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:35:31.607255: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:35:31.614834: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:35:31.615268: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:35:31.619679: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:35:31.621660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:35:31.630467: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:35:31.632390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:35:31.632479: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:35:31.633491: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 09:35:31.633511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 09:35:31.633524: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 09:35:31.635369: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9398 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 2025-03-08 09:35:36.544291: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:35:36.544396: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:35:36.544423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:35:36.544448: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:35:36.544472: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:35:36.544496: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:35:36.544532: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:35:36.544558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:35:36.546248: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:35:36.547823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:35:36.547871: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:35:36.547896: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:35:36.547920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:35:36.547943: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:35:36.547966: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:35:36.547989: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:35:36.548013: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:35:36.549689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:35:36.549731: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 09:35:36.549745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 09:35:36.549757: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 09:35:36.551508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9398 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-03-08 09:35:46.822795: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:35:47.022720: 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 3770707 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4061 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 : 9350 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 : 0.0005283355712890625 nb_pixel_total : 15552 time to create 1 rle with old method : 0.018008947372436523 length of segment : 256 time for calcul the mask position with numpy : 0.002622842788696289 nb_pixel_total : 145331 time to create 1 rle with old method : 0.1702580451965332 length of segment : 371 time for calcul the mask position with numpy : 0.000244140625 nb_pixel_total : 14253 time to create 1 rle with old method : 0.0180513858795166 length of segment : 151 time for calcul the mask position with numpy : 0.0001277923583984375 nb_pixel_total : 5614 time to create 1 rle with old method : 0.007884740829467773 length of segment : 48 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 1824 time to create 1 rle with old method : 0.002593517303466797 length of segment : 39 time spent for convertir_results : 1.017338752746582 time spend for datou_step_exec : 26.013575792312622 time spend to save output : 4.887580871582031e-05 total time spend for step 1 : 26.013624668121338 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 3314 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.017932415008544922 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, 0.9954853, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (126, 30, 28), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,226,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923804, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 311), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), (89, 111, 449), (89, 112, 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(45, 175, 524), (45, 176, 523), (44, 177, 524), (44, 178, 524), (44, 179, 524), (43, 180, 525), (43, 181, 525), (42, 182, 525), (42, 183, 525), (42, 184, 525), (41, 185, 526), (41, 186, 526), (40, 187, 526), (39, 188, 526), (39, 189, 525), (38, 190, 526), (38, 191, 525), (37, 192, 525), (37, 193, 523), (36, 194, 523), (36, 195, 522), (36, 196, 522), (35, 197, 522), (35, 198, 521), (34, 199, 521), (34, 200, 521), (34, 201, 520), (34, 202, 520), (34, 203, 520), (34, 204, 519), (33, 205, 520), (33, 206, 520), (33, 207, 519), (33, 208, 519), (33, 209, 519), (33, 210, 518), (33, 211, 518), (33, 212, 518), (33, 213, 517), (32, 214, 518), (32, 215, 517), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 512), (32, 222, 511), (32, 223, 510), (32, 224, 508), (32, 225, 507), (32, 226, 505), (32, 227, 504), (32, 228, 503), (32, 229, 502), (32, 230, 502), (32, 231, 501), (32, 232, 500), (32, 233, 499), (32, 234, 498), (32, 235, 497), (31, 236, 496), (31, 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['598,172,591,172,590,171,578,168,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,502,40,516,30,523,27,526,27,531,25,539,25,540,24,560,24,561,25,579,25,580,26,593,26,594,25,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8299817, [(291, 3, 129), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 44), (432, 38, 41), (434, 39, 40), (436, 40, 39), (438, 41, 37), (441, 42, 35), (444, 43, 32), (448, 44, 29), (452, 45, 25), (454, 46, 23), (459, 47, 17), (463, 48, 12), (468, 49, 5)], ['472,49,468,49,467,48,459,47,458,46,454,46,451,44,448,44,447,43,444,43,440,41,438,41,428,36,424,36,423,35,419,35,418,34,409,34,408,33,401,33,400,32,371,32,370,31,337,31,336,30,283,31,281,29,281,6,284,4,290,4,291,3,419,3,420,4,429,4,430,5,432,5,436,7,441,11,445,12,453,16,456,19,457,19,465,27,465,29,472,37,476,44,476,46']), (957285035, 492601069, 445, 456, 547, 6, 45, 0.74057704, [(482, 8, 19), (464, 9, 3), (481, 9, 44), (457, 10, 12), (479, 10, 50), (457, 11, 13), (476, 11, 56), (457, 12, 15), (475, 12, 65), (457, 13, 84), (457, 14, 85), (457, 15, 89), (457, 16, 89), (458, 17, 88), (459, 18, 87), (460, 19, 86), (461, 20, 80), (464, 21, 71), (466, 22, 63), (467, 23, 59), (468, 24, 55), (469, 25, 52), (469, 26, 51), (470, 27, 48), (471, 28, 46), (471, 29, 44), (472, 30, 42), (473, 31, 39), (473, 32, 38), (474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1741422927_3770391_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4096 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.7535603046417236 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Sat Mar 8 09:36:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 3840 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-08 09:36:42.721029: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-08 09:36:42.751136: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-08 09:36:42.754300: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f4e04000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-08 09:36:42.754390: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-08 09:36:42.760905: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-08 09:36:42.992109: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4103efd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-08 09:36:42.992188: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-08 09:36:42.993412: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:36:42.994281: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:36:42.999021: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:36:43.003042: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:36:43.005198: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:36:43.008444: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:36:43.010204: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:36:43.016996: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:36:43.018736: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:36:43.019002: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:36:43.019811: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 09:36:43.019835: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 09:36:43.019847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 09:36:43.021181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3388 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-03-08 09:36:43.131403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:36:43.131631: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:36:43.131668: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:36:43.131700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:36:43.131729: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:36:43.131756: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:36:43.131783: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:36:43.131829: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:36:43.132978: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:36:43.134562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:36:43.134717: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:36:43.134750: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:36:43.134779: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:36:43.134805: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:36:43.134831: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:36:43.134856: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:36:43.134883: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:36:43.136086: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:36:43.136144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 09:36:43.136156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 09:36:43.136168: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 09:36:43.137381: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3388 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 : [] 2025-03-08 09:36:47.916824: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 51380224 exceeds 10% of free system memory. 2025-03-08 09:36:51.687539: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:36:52.079884: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:36:53.892880: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.903474: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.53G (2715903488 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.904471: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.28G (2444313088 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.904529: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.905619: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.905647: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.914147: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.914211: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.915298: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.915330: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.922077: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.922102: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.922715: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.922731: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.953634: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.953667: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.954264: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.954279: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.960148: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.960169: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.960764: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.960780: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:36:53.994717: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.995350: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.997254: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:53.997874: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.041763: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.042380: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.044654: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.045262: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.053169: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.053771: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.058648: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.059258: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.070257: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.070861: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.072470: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.073073: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.078716: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.079342: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.081052: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.081650: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.087422: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.088026: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.089604: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.090205: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.116702: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.117333: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.117937: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.118534: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.122077: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.122702: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.138149: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.138798: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.139419: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.140020: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.152411: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.153022: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.153621: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.154217: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.158508: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.159133: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.163791: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.164401: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.176894: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.177548: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.181837: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.182465: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.183093: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.183710: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.204980: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.205590: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.206223: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.206827: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.207449: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.208053: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.208686: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.209286: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.223857: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.224464: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.247509: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.247556: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-03-08 09:36:54.248646: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.249708: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.257326: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.258356: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.259389: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.260451: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.269156: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.270227: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.286469: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.287482: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.288255: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.289005: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.293311: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.294072: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.294815: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.295583: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.296839: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.307082: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.307724: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.318105: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.318719: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.319349: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.319951: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.320563: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:36:54.321165: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.81G (3017670656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (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 3775015 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 2253 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 : 3446 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 : 0.0006949901580810547 nb_pixel_total : 16902 time to create 1 rle with old method : 0.03448796272277832 length of segment : 107 time for calcul the mask position with numpy : 0.024962663650512695 nb_pixel_total : 480753 time to create 1 rle with new method : 0.03810429573059082 length of segment : 632 time for calcul the mask position with numpy : 0.0006401538848876953 nb_pixel_total : 36640 time to create 1 rle with old method : 0.05162334442138672 length of segment : 133 time for calcul the mask position with numpy : 0.0001556873321533203 nb_pixel_total : 4793 time to create 1 rle with old method : 0.0063321590423583984 length of segment : 51 time spent for convertir_results : 0.3738210201263428 time spend for datou_step_exec : 18.56231117248535 time spend to save output : 6.0558319091796875e-05 total time spend for step 1 : 18.562371730804443 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 403 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 : 0.012058258056640625 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, 0.99883777, [(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, 0.9977469, [(711, 22, 22), (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, 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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/1741422999_3770391_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 : 0.17934846878051758 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Sat Mar 8 09:37:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 3446 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-08 09:37:07.504543: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-08 09:37:07.531267: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-08 09:37:07.534031: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f4e04000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-08 09:37:07.534129: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-08 09:37:07.539030: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-08 09:37:07.712327: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x41563c20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-08 09:37:07.712417: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-08 09:37:07.713417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:37:07.715484: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:37:07.746069: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:37:07.753771: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:37:07.758475: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:37:07.768273: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:37:07.771209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:37:07.797473: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:37:07.799022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:37:07.799155: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:37:07.800176: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 09:37:07.800198: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 09:37:07.800210: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 09:37:07.801458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2994 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-03-08 09:37:07.976517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:37:07.976672: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:37:07.976698: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:37:07.976721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:37:07.976743: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:37:07.976763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:37:07.976784: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:37:07.976805: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:37:07.978014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:37:07.979786: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:37:07.979923: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:37:07.979957: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:37:07.979985: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:37:07.980014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:37:07.980041: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:37:07.980070: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:37:07.980099: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:37:07.981275: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:37:07.981344: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 09:37:07.981358: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 09:37:07.981369: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 09:37:07.982565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2994 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 : [] 2025-03-08 09:37:13.887410: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 51380224 exceeds 10% of free system memory. 2025-03-08 09:37:20.079813: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:37:20.472448: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:37:23.211876: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.42G (2604531712 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-08 09:37:24.040360: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.67GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:37:24.040741: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.67GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:37:24.047542: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:37:24.047568: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:37:24.130386: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.26GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-08 09:37:24.130453: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.26GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 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 3776825 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 17 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 : 3446 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 : 0.7024469375610352 nb_pixel_total : 3693304 time to create 1 rle with new method : 1.499758005142212 length of segment : 2041 time spent for convertir_results : 3.136512041091919 time spend for datou_step_exec : 27.616992950439453 time spend to save output : 5.14984130859375e-05 total time spend for step 1 : 27.61704444885254 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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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 : 0.014165639877319336 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, 0.98502284, [(674, 120, 114), (520, 121, 481), (1050, 121, 381), (502, 122, 947), (486, 123, 981), (470, 124, 1015), (455, 125, 1046), (442, 126, 1092), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1222), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (363, 135, 1266), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (352, 140, 1303), (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), (323, 159, 1389), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1405), (317, 164, 1409), (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), (280, 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), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1681), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 1710), (203, 210, 1712), (201, 211, 1716), (199, 212, 1719), (198, 213, 1721), (196, 214, 1725), (195, 215, 1727), (193, 216, 1730), (192, 217, 1733), (191, 218, 1735), (189, 219, 1738), (188, 220, 1740), (187, 221, 1742), (186, 222, 1744), (185, 223, 1746), (183, 224, 1749), (182, 225, 1751), (181, 226, 1753), (180, 227, 1755), (179, 228, 1757), (178, 229, 1759), (177, 230, 1761), (176, 231, 1762), (176, 232, 1763), (175, 233, 1765), (174, 234, 1767), (173, 235, 1768), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1775), (168, 240, 1777), (167, 241, 1779), (166, 242, 1781), (165, 243, 1783), (164, 244, 1785), (163, 245, 1787), (162, 246, 1789), (161, 247, 1791), (159, 248, 1794), (158, 249, 1796), (157, 250, 1798), (156, 251, 1800), (154, 252, 1803), (153, 253, 1805), (152, 254, 1807), (150, 255, 1810), (149, 256, 1812), (148, 257, 1815), (146, 258, 1818), (145, 259, 1820), (143, 260, 1823), (142, 261, 1826), (140, 262, 1829), (138, 263, 1833), (137, 264, 1835), (135, 265, 1839), (133, 266, 1842), (132, 267, 1845), (130, 268, 1849), (128, 269, 1852), (126, 270, 1856), (125, 271, 1859), (124, 272, 1862), (122, 273, 1865), (121, 274, 1868), (120, 275, 1871), (119, 276, 1873), (117, 277, 1877), (116, 278, 1879), (115, 279, 1881), (114, 280, 1884), (113, 281, 1886), (112, 282, 1888), (111, 283, 1890), (110, 284, 1892), (109, 285, 1895), (108, 286, 1897), (108, 287, 1898), (107, 288, 1900), (106, 289, 1902), (105, 290, 1904), (104, 291, 1906), (103, 292, 1908), (103, 293, 1909), (102, 294, 1910), (101, 295, 1912), (101, 296, 1913), (100, 297, 1915), (99, 298, 1917), (99, 299, 1918), (98, 300, 1919), (97, 301, 1921), (97, 302, 1922), (96, 303, 1924), (95, 304, 1925), (95, 305, 1926), (94, 306, 1928), (94, 307, 1928), (93, 308, 1930), (93, 309, 1930), (93, 310, 1931), (93, 311, 1931), (92, 312, 1933), (92, 313, 1933), (92, 314, 1934), (92, 315, 1934), (91, 316, 1936), (91, 317, 1936), (91, 318, 1937), (90, 319, 1938), (90, 320, 1939), (90, 321, 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691), (671, 2066, 685), (673, 2067, 679), (675, 2068, 673), (678, 2069, 666), (680, 2070, 660), (683, 2071, 652), (686, 2072, 643), (688, 2073, 636), (691, 2074, 628), (694, 2075, 619), (699, 2076, 609), (703, 2077, 599), (708, 2078, 588), (712, 2079, 579), (717, 2080, 568), (721, 2081, 558), (725, 2082, 548), (730, 2083, 536), (734, 2084, 526), (738, 2085, 516), (743, 2086, 506), (747, 2087, 497), (751, 2088, 488), (755, 2089, 479), (760, 2090, 469), (764, 2091, 460), (768, 2092, 451), (772, 2093, 443), (776, 2094, 434), (779, 2095, 427), (782, 2096, 419), (786, 2097, 411), (789, 2098, 404), (792, 2099, 397), (795, 2100, 390), (799, 2101, 382), (802, 2102, 375), (805, 2103, 370), (808, 2104, 364), (811, 2105, 359), (814, 2106, 354), (818, 2107, 347), (821, 2108, 342), (824, 2109, 337), (827, 2110, 332), (830, 2111, 327), (833, 2112, 322), (836, 2113, 317), (839, 2114, 312), (842, 2115, 307), (845, 2116, 302), (848, 2117, 297), (851, 2118, 292), (854, 2119, 288), (857, 2120, 283), (860, 2121, 278), (863, 2122, 273), (866, 2123, 269), (869, 2124, 264), (872, 2125, 259), (874, 2126, 256), (877, 2127, 251), (880, 2128, 246), (883, 2129, 242), (886, 2130, 237), (889, 2131, 232), (892, 2132, 227), (896, 2133, 221), (899, 2134, 216), (902, 2135, 210), (906, 2136, 204), (909, 2137, 199), (913, 2138, 193), (916, 2139, 187), (920, 2140, 181), (924, 2141, 174), (928, 2142, 166), (932, 2143, 154), (936, 2144, 142), (945, 2145, 125), (956, 2146, 106), (966, 2147, 88), (977, 2148, 68), (989, 2149, 48), (1000, 2150, 28), (1012, 2151, 7)], ['1000,2150,936,2144,775,2093,694,2075,586,2031,371,1987,214,1963,128,1971,54,1825,39,1677,39,1455,29,1246,29,892,21,696,27,543,39,458,93,308,117,277,210,206,291,179,368,133,520,121,1430,121,1584,128,1663,142,1805,187,1904,204,2021,306,2094,411,2148,535,2168,614,2165,833,2128,914,2112,994,2081,1068,2031,1132,2009,1191,1967,1255,1931,1368,1879,1444,1846,1670,1765,1903,1719,1973,1662,2015,1582,2015,1496,2039,1420,2046,1339,2070,1177,2101,1093,2142'])], 'temp/1741423023_3770391_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3690725 proportion of common points : 0.999574790205008 [('test release memory', 'FAILURE', False), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)] res_total : False #&_# TEST FAILED #&_# : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_7b90246f7285e983362b27a932a2f1f627b3aad9 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_7b90246f7285e983362b27a932a2f1f627b3aad9','{"mask_detection": "fail"}','0','http://marlene.fotonower-preprod.com/job/2025/March/08032025/python_test3//data_2/data_log/job/2025/March/08032025/python_test3/log-python3----short_python3--v--marlene-09:35:00.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 : 0.9532597064971924 #### 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 8 09:40:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741423214_3770391_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1741423214_3770391_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 : 0.0020666122436523438 nb_pixel_total : 5628 time to create 1 rle with old method : 0.015919923782348633 time for calcul the mask position with numpy : 0.002446413040161133 nb_pixel_total : 3753 time to create 1 rle with old method : 0.004353046417236328 time for calcul the mask position with numpy : 0.0018715858459472656 nb_pixel_total : 84107 time to create 1 rle with old method : 0.09437918663024902 time for calcul the mask position with numpy : 0.0015568733215332031 nb_pixel_total : 14725 time to create 1 rle with old method : 0.017956018447875977 time for calcul the mask position with numpy : 0.001506805419921875 nb_pixel_total : 16280 time to create 1 rle with old method : 0.01879262924194336 time for calcul the mask position with numpy : 0.001529693603515625 nb_pixel_total : 16447 time to create 1 rle with old method : 0.01860809326171875 time for calcul the mask position with numpy : 0.0015175342559814453 nb_pixel_total : 13957 time to create 1 rle with old method : 0.0161435604095459 time for calcul the mask position with numpy : 0.001476287841796875 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0015039443969726562 time for calcul the mask position with numpy : 0.0014615058898925781 nb_pixel_total : 2374 time to create 1 rle with old method : 0.0029010772705078125 time for calcul the mask position with numpy : 0.0014505386352539062 nb_pixel_total : 2951 time to create 1 rle with old method : 0.00354766845703125 time for calcul the mask position with numpy : 0.0016634464263916016 nb_pixel_total : 38735 time to create 1 rle with old method : 0.05874824523925781 time for calcul the mask position with numpy : 0.0016427040100097656 nb_pixel_total : 2424 time to create 1 rle with old method : 0.004282951354980469 time for calcul the mask position with numpy : 0.001613616943359375 nb_pixel_total : 4241 time to create 1 rle with old method : 0.0072290897369384766 time for calcul the mask position with numpy : 0.0015513896942138672 nb_pixel_total : 10818 time to create 1 rle with old method : 0.012363433837890625 time for calcul the mask position with numpy : 0.0016710758209228516 nb_pixel_total : 29467 time to create 1 rle with old method : 0.033319950103759766 time for calcul the mask position with numpy : 0.0015416145324707031 nb_pixel_total : 6638 time to create 1 rle with old method : 0.011258602142333984 time for calcul the mask position with numpy : 0.0015223026275634766 nb_pixel_total : 5515 time to create 1 rle with old method : 0.0064945220947265625 time for calcul the mask position with numpy : 0.0015227794647216797 nb_pixel_total : 3952 time to create 1 rle with old method : 0.005039691925048828 time for calcul the mask position with numpy : 0.001506805419921875 nb_pixel_total : 7636 time to create 1 rle with old method : 0.009024381637573242 time for calcul the mask position with numpy : 0.0015077590942382812 nb_pixel_total : 4248 time to create 1 rle with old method : 0.005294322967529297 time for calcul the mask position with numpy : 0.0016238689422607422 nb_pixel_total : 1202 time to create 1 rle with old method : 0.001535177230834961 time for calcul the mask position with numpy : 0.0014445781707763672 nb_pixel_total : 1049 time to create 1 rle with old method : 0.0014274120330810547 time for calcul the mask position with numpy : 0.0015254020690917969 nb_pixel_total : 13134 time to create 1 rle with old method : 0.01526021957397461 time for calcul the mask position with numpy : 0.0015418529510498047 nb_pixel_total : 2075 time to create 1 rle with old method : 0.0024824142456054688 time for calcul the mask position with numpy : 0.0014872550964355469 nb_pixel_total : 1021 time to create 1 rle with old method : 0.001341104507446289 time for calcul the mask position with numpy : 0.001485586166381836 nb_pixel_total : 1511 time to create 1 rle with old method : 0.0018663406372070312 time for calcul the mask position with numpy : 0.0014729499816894531 nb_pixel_total : 2758 time to create 1 rle with old method : 0.003398418426513672 time for calcul the mask position with numpy : 0.0015134811401367188 nb_pixel_total : 13103 time to create 1 rle with old method : 0.01598358154296875 time for calcul the mask position with numpy : 0.0015230178833007812 nb_pixel_total : 8606 time to create 1 rle with old method : 0.010419130325317383 time for calcul the mask position with numpy : 0.0016908645629882812 nb_pixel_total : 906 time to create 1 rle with old method : 0.0017457008361816406 time for calcul the mask position with numpy : 0.0018804073333740234 nb_pixel_total : 4171 time to create 1 rle with old method : 0.007469654083251953 time for calcul the mask position with numpy : 0.0017704963684082031 nb_pixel_total : 3532 time to create 1 rle with old method : 0.0044252872467041016 time for calcul the mask position with numpy : 0.0014634132385253906 nb_pixel_total : 2748 time to create 1 rle with old method : 0.0034685134887695312 time for calcul the mask position with numpy : 0.001710653305053711 nb_pixel_total : 9905 time to create 1 rle with old method : 0.012337684631347656 time for calcul the mask position with numpy : 0.0014586448669433594 nb_pixel_total : 2462 time to create 1 rle with old method : 0.0030536651611328125 time for calcul the mask position with numpy : 0.0014386177062988281 nb_pixel_total : 2828 time to create 1 rle with old method : 0.0034804344177246094 time for calcul the mask position with numpy : 0.001461029052734375 nb_pixel_total : 5422 time to create 1 rle with old method : 0.006653785705566406 time for calcul the mask position with numpy : 0.0015397071838378906 nb_pixel_total : 11968 time to create 1 rle with old method : 0.014339447021484375 time for calcul the mask position with numpy : 0.0015454292297363281 nb_pixel_total : 10611 time to create 1 rle with old method : 0.012401342391967773 time for calcul the mask position with numpy : 0.0014731884002685547 nb_pixel_total : 3170 time to create 1 rle with old method : 0.00383758544921875 time for calcul the mask position with numpy : 0.0015053749084472656 nb_pixel_total : 4123 time to create 1 rle with old method : 0.005052804946899414 time for calcul the mask position with numpy : 0.0014786720275878906 nb_pixel_total : 3366 time to create 1 rle with old method : 0.005097627639770508 time for calcul the mask position with numpy : 0.0015158653259277344 nb_pixel_total : 1653 time to create 1 rle with old method : 0.002033710479736328 time for calcul the mask position with numpy : 0.0014433860778808594 nb_pixel_total : 1251 time to create 1 rle with old method : 0.0015501976013183594 time for calcul the mask position with numpy : 0.0014560222625732422 nb_pixel_total : 3935 time to create 1 rle with old method : 0.0047838687896728516 time for calcul the mask position with numpy : 0.0014293193817138672 nb_pixel_total : 341 time to create 1 rle with old method : 0.00045418739318847656 time for calcul the mask position with numpy : 0.0014667510986328125 nb_pixel_total : 861 time to create 1 rle with old method : 0.001165628433227539 time for calcul the mask position with numpy : 0.0014545917510986328 nb_pixel_total : 579 time to create 1 rle with old method : 0.0007481575012207031 time for calcul the mask position with numpy : 0.001430511474609375 nb_pixel_total : 594 time to create 1 rle with old method : 0.0007696151733398438 time for calcul the mask position with numpy : 0.0014183521270751953 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004715919494628906 time for calcul the mask position with numpy : 0.0014455318450927734 nb_pixel_total : 872 time to create 1 rle with old method : 0.0012257099151611328 time for calcul the mask position with numpy : 0.0014579296112060547 nb_pixel_total : 1672 time to create 1 rle with old method : 0.0020983219146728516 time for calcul the mask position with numpy : 0.001438140869140625 nb_pixel_total : 2324 time to create 1 rle with old method : 0.0027875900268554688 time for calcul the mask position with numpy : 0.0015048980712890625 nb_pixel_total : 13593 time to create 1 rle with old method : 0.0156404972076416 time for calcul the mask position with numpy : 0.0014858245849609375 nb_pixel_total : 8692 time to create 1 rle with old method : 0.010207891464233398 time for calcul the mask position with numpy : 0.0016422271728515625 nb_pixel_total : 39084 time to create 1 rle with old method : 0.04559803009033203 time for calcul the mask position with numpy : 0.0014650821685791016 nb_pixel_total : 2013 time to create 1 rle with old method : 0.002529621124267578 time for calcul the mask position with numpy : 0.0014414787292480469 nb_pixel_total : 3094 time to create 1 rle with old method : 0.0038046836853027344 time for calcul the mask position with numpy : 0.0015740394592285156 nb_pixel_total : 27929 time to create 1 rle with old method : 0.03314495086669922 time for calcul the mask position with numpy : 0.001485586166381836 nb_pixel_total : 694 time to create 1 rle with old method : 0.0009157657623291016 time for calcul the mask position with numpy : 0.0014274120330810547 nb_pixel_total : 2425 time to create 1 rle with old method : 0.0030972957611083984 time for calcul the mask position with numpy : 0.001422882080078125 nb_pixel_total : 1715 time to create 1 rle with old method : 0.002089977264404297 time for calcul the mask position with numpy : 0.0014147758483886719 nb_pixel_total : 290 time to create 1 rle with old method : 0.0004525184631347656 time for calcul the mask position with numpy : 0.001438140869140625 nb_pixel_total : 592 time to create 1 rle with old method : 0.0007638931274414062 time for calcul the mask position with numpy : 0.0014188289642333984 nb_pixel_total : 875 time to create 1 rle with old method : 0.0010941028594970703 time for calcul the mask position with numpy : 0.0014181137084960938 nb_pixel_total : 1075 time to create 1 rle with old method : 0.0013298988342285156 time for calcul the mask position with numpy : 0.0015065670013427734 nb_pixel_total : 16620 time to create 1 rle with old method : 0.019963502883911133 time for calcul the mask position with numpy : 0.0014238357543945312 nb_pixel_total : 782 time to create 1 rle with old method : 0.0010869503021240234 time for calcul the mask position with numpy : 0.0014362335205078125 nb_pixel_total : 713 time to create 1 rle with old method : 0.0009624958038330078 time for calcul the mask position with numpy : 0.0014202594757080078 nb_pixel_total : 1737 time to create 1 rle with old method : 0.002422809600830078 time for calcul the mask position with numpy : 0.0014617443084716797 nb_pixel_total : 1822 time to create 1 rle with old method : 0.0022792816162109375 time for calcul the mask position with numpy : 0.0014712810516357422 nb_pixel_total : 8451 time to create 1 rle with old method : 0.009753227233886719 time for calcul the mask position with numpy : 0.001428842544555664 nb_pixel_total : 267 time to create 1 rle with old method : 0.00034880638122558594 time for calcul the mask position with numpy : 0.001438140869140625 nb_pixel_total : 1338 time to create 1 rle with old method : 0.0017123222351074219 time for calcul the mask position with numpy : 0.001468658447265625 nb_pixel_total : 9078 time to create 1 rle with old method : 0.010644197463989258 time for calcul the mask position with numpy : 0.001476287841796875 nb_pixel_total : 9503 time to create 1 rle with old method : 0.011346817016601562 time for calcul the mask position with numpy : 0.0014524459838867188 nb_pixel_total : 969 time to create 1 rle with old method : 0.001192331314086914 time for calcul the mask position with numpy : 0.0015232563018798828 nb_pixel_total : 18536 time to create 1 rle with old method : 0.021103858947753906 time for calcul the mask position with numpy : 0.0014760494232177734 nb_pixel_total : 7499 time to create 1 rle with old method : 0.008804082870483398 time for calcul the mask position with numpy : 0.0014510154724121094 nb_pixel_total : 737 time to create 1 rle with old method : 0.001032114028930664 time for calcul the mask position with numpy : 0.0014183521270751953 nb_pixel_total : 616 time to create 1 rle with old method : 0.0008106231689453125 time for calcul the mask position with numpy : 0.0014252662658691406 nb_pixel_total : 248 time to create 1 rle with old method : 0.00035309791564941406 time for calcul the mask position with numpy : 0.001416921615600586 nb_pixel_total : 973 time to create 1 rle with old method : 0.0012907981872558594 time for calcul the mask position with numpy : 0.0014307498931884766 nb_pixel_total : 2656 time to create 1 rle with old method : 0.0034906864166259766 time for calcul the mask position with numpy : 0.0014164447784423828 nb_pixel_total : 221 time to create 1 rle with old method : 0.00033164024353027344 time for calcul the mask position with numpy : 0.0014333724975585938 nb_pixel_total : 1667 time to create 1 rle with old method : 0.0020818710327148438 time for calcul the mask position with numpy : 0.0014407634735107422 nb_pixel_total : 1500 time to create 1 rle with old method : 0.0018541812896728516 time for calcul the mask position with numpy : 0.0014412403106689453 nb_pixel_total : 5008 time to create 1 rle with old method : 0.006204366683959961 time for calcul the mask position with numpy : 0.0014314651489257812 nb_pixel_total : 1442 time to create 1 rle with old method : 0.0018734931945800781 time for calcul the mask position with numpy : 0.0014247894287109375 nb_pixel_total : 595 time to create 1 rle with old method : 0.0008132457733154297 time for calcul the mask position with numpy : 0.0014371871948242188 nb_pixel_total : 1143 time to create 1 rle with old method : 0.0014605522155761719 time for calcul the mask position with numpy : 0.0014472007751464844 nb_pixel_total : 917 time to create 1 rle with old method : 0.0013284683227539062 time for calcul the mask position with numpy : 0.001432657241821289 nb_pixel_total : 1444 time to create 1 rle with old method : 0.0019123554229736328 time for calcul the mask position with numpy : 0.0014257431030273438 nb_pixel_total : 889 time to create 1 rle with old method : 0.0011889934539794922 time for calcul the mask position with numpy : 0.0014619827270507812 nb_pixel_total : 2638 time to create 1 rle with old method : 0.003360748291015625 time for calcul the mask position with numpy : 0.0014374256134033203 nb_pixel_total : 2199 time to create 1 rle with old method : 0.0029211044311523438 time for calcul the mask position with numpy : 0.0014255046844482422 nb_pixel_total : 1321 time to create 1 rle with old method : 0.0017163753509521484 time for calcul the mask position with numpy : 0.0014231204986572266 nb_pixel_total : 885 time to create 1 rle with old method : 0.0011799335479736328 time for calcul the mask position with numpy : 0.0014345645904541016 nb_pixel_total : 947 time to create 1 rle with old method : 0.0012624263763427734 time for calcul the mask position with numpy : 0.001468658447265625 nb_pixel_total : 475 time to create 1 rle with old method : 0.0006577968597412109 time for calcul the mask position with numpy : 0.0015251636505126953 nb_pixel_total : 16233 time to create 1 rle with old method : 0.021188735961914062 time for calcul the mask position with numpy : 0.0014538764953613281 nb_pixel_total : 1626 time to create 1 rle with old method : 0.002438068389892578 time for calcul the mask position with numpy : 0.0018427371978759766 nb_pixel_total : 814 time to create 1 rle with old method : 0.0013082027435302734 time for calcul the mask position with numpy : 0.0016896724700927734 nb_pixel_total : 1325 time to create 1 rle with old method : 0.0016214847564697266 time for calcul the mask position with numpy : 0.0014407634735107422 nb_pixel_total : 829 time to create 1 rle with old method : 0.0010657310485839844 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) 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 105 chid ids of type : 4677 Number RLEs to save : 10007 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3699303969', '464', '201', '4') ... last line : ('3699304079', '815', '44', '5') 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 : 0.19739985466003418 save_final save missing photos in datou_result : time spend for datou_step_exec : 14.810070753097534 time spend to save output : 0.1977827548980713 total time spend for step 1 : 15.007853507995605 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1741423214_3770391_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 105 ############################### 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 : 0.18564915657043457 #### 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 8 09:40:30 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 : {'temp/1741423229_3770391_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1741423229_3770391_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/1741423229_3770391_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 0.091s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.06384062 (374.12692, 293.91928, 430.81015, 317.80862) proba : 0.052221723 (382.1777, 297.18866, 552.3578, 344.65796) proba : 0.012271154 (345.3568, 272.42984, 468.85754, 320.72433) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 3.141188144683838 time spend to save output : 0.00024175643920898438 total time spend for step 1 : 3.141429901123047 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, 0.06384062, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052221723, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271154, 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 : 0.17519783973693848 [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 : 0.01084280014038086 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, 0.06384062, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052221723, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271154, None)], 'temp/1741423229_3770391_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 : 0.3737173080444336 #### 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 8 09:40:33 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/1741423233_3770391_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1741423233_3770391_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 : 0.005738496780395508 time to convert the images to numpy array : 0.0017018318176269531 total time to convert the images to numpy array : 0.007674455642700195 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': 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'svm_portfolios_learning': 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'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 : 6120 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 : 5616 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.0187530517578125 time used to do the prediction : 0.06481266021728516 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 : 0.050309181213378906 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 : 5.053435802459717 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', 0.0018815055, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011635018, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.00081580365, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.0011772842, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.0025849063, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.0041699475, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.0034810875, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.0073668845, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021887412, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.00057979027, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.004591094, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.003158682, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.0022507254, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.005320422, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0010998911, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.005402121, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.003918967, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014787723, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019777212, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.0012441811, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015049677, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021692114, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.0027079482, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.004703795, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019418104, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.0009584704, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.0007699634, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019468539, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013724723, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.0016202186, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013924346, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010044402, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010793247, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.00084468414, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.001252038, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.002584474, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012430159, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.002864982, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012948674, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010339214, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018766791, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011343688, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015758396, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.003980399, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.0028739893, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023355111, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.001210549, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011447209, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.0022338764, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017463815, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.0046992656, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021913375, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022632366, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018174354, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.0009056604, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.0020402453, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025336263, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.0044889026, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.003425286, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031354076, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010255473, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.0015354324, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.00059149275, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.0040291976, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011586264, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012158172, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014967787, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.0011320605, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010545757, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011985097, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.0041861017, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.0023636173, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016324389, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015567146, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013959334, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.002197885, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.008827602, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.0014614828, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.00088245963, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.0024712216, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010092409, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015961826, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.0015330318, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.0014917016, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014585118, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.0056456807, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.001116541, 332, '355'), 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('916235064', 'viano_1027_gao__port_506211', 0.002694374, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.00083200575, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.003737932, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.0007919774, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030526055, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.0037232286, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017739697, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015303944, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004087177, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011862235, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.0026953425, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011407182, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012529051, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.0015148806, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031654942, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017348686, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.01672793, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018254868, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.002145817, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.001415188, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.0020552808, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.0022963327, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013943578, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.01196823, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010547773, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020714647, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017868982, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.0057225, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.0027665135, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017143976, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.004825154, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018685877, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012579917, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012625157, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.000924439, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.007466374, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.000729045, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.001038641, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.00069112354, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.0021416487, 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 : 1.0251998901367188e-05 save missing photos in datou_result : time spend for datou_step_exec : 11.556878328323364 time spend to save output : 33.25182318687439 total time spend for step 1 : 44.808701515197754 step2:argmax Sat Mar 8 09:41:18 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/1741423233_3770391_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1741423233_3770391_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', 0.017713448, 332, '355'), 'temp/1741423233_3770391_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 : 0.05854487419128418 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 : 0.023653030395507812 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', '0.017713448', None)] time used for this insertion : 0.016405820846557617 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 : 1.2874603271484375e-05 save missing photos in datou_result : time spend for datou_step_exec : 0.004260063171386719 time spend to save output : 0.0994863510131836 total time spend for step 2 : 0.10374641418457031 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', 0.017713448, 332, '355'), 'temp/1741423233_3770391_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 1171252784 download finish for photo 1171252487 download finish for photo 1171252764 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 : 0.1931920051574707 #### 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 8 09:41:18 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/1741423278_3770391_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1741423278_3770391_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1741423278_3770391_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764} map_photo_id_path_extension : {1171252784: {'path': 'temp/1741423278_3770391_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1741423278_3770391_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1741423278_3770391_1171252764_29d5179a892cc50aadc9d67245534b59.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 2025-03-08 09:41:22.013285: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-08 09:41:22.013949: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:41:22.014049: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:41:22.014099: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:41:22.016025: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:41:22.016227: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:41:22.018291: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:41:22.019397: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:41:22.025024: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:41:22.026239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:41:22.026693: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-08 09:41:22.059110: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-08 09:41:22.060966: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f4b74000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-08 09:41:22.060998: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-08 09:41:22.064636: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x455a6bd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-08 09:41:22.064660: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-08 09:41:22.065403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-08 09:41:22.065501: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:41:22.065525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-08 09:41:22.065600: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-08 09:41:22.065628: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-08 09:41:22.065663: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-08 09:41:22.065700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-08 09:41:22.065737: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-08 09:41:22.066871: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-08 09:41:22.066953: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-08 09:41:22.067009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-08 09:41:22.067022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-08 09:41:22.067032: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-08 09:41:22.068245: 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 : 5370 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 : 9.853929281234741 time used to load_weights : 0.1465928554534912 0it [00:00, ?it/s] 3it [00:00, 1047.01it/s]2025-03-08 09:41:34.268035: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1741423278_3770391_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1741423278_3770391_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1741423278_3770391_1171252764_29d5179a892cc50aadc9d67245534b59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.4230458736419678 ['temp/image000000000_1741423278_3770391_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'temp/image000000001_1741423278_3770391_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'temp/image000000002_1741423278_3770391_1171252764_29d5179a892cc50aadc9d67245534b59.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, 0, 1, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000001'| 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, 1, 0, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000100'| time to traite the descriptors : 0.029958724975585938 Testing : ['1171252784', '1171252487', '1171252764'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171252784,1171252487,1171252764) 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 : 1171252784 To insert : 1171252487 To insert : 1171252764 time to insert the descriptors : 2.8603084087371826 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 [1171252784, 1171252487, 1171252764] map_info['map_portfolio_photo'] : {} final : False mtd_id 4567 list_pids : [1171252784, 1171252487, 1171252764] Looping around the photos to save general results len do output : 3 /1171252784Didn't retrieve data . /1171252487Didn't retrieve data . /1171252764Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 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, '1171252784', 'None', None, None, None, None, None), ('4567', None, '1171252487', 'None', None, None, None, None, None), ('4567', None, '1171252764', 'None', None, None, None, None, None)] time used for this insertion : 0.014410257339477539 save_final save missing photos in datou_result : time spend for datou_step_exec : 21.9601788520813 time spend to save output : 0.014987945556640625 total time spend for step 1 : 21.97516679763794 step2:argmax Sat Mar 8 09:41:40 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/1741423278_3770391_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1741423278_3770391_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1741423278_3770391_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764} map_photo_id_path_extension : {1171252784: {'path': 'temp/1741423278_3770391_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1741423278_3770391_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1741423278_3770391_1171252764_29d5179a892cc50aadc9d67245534b59.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 : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.967751, 4674, '3609'), 'temp/1741423278_3770391_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.92627835, 4674, '3609'), 'temp/1741423278_3770391_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.98535776, 4674, '3609'), 'temp/1741423278_3770391_1171252764_29d5179a892cc50aadc9d67245534b59.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 : ('1171252784', '495916461', '4674') ... last line : ('1171252764', '495916461', '4674') time used for this insertion : 1.1752808094024658 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 : 0.06041765213012695 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, '1171252784', 'jrm', None, None, '495916461', '0.967751', None), ('4567', None, '1171252487', 'jrm', None, None, '495916461', '0.92627835', None), ('4567', None, '1171252764', 'jrm', None, None, '495916461', '0.98535776', None)] time used for this insertion : 0.013554096221923828 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 : 4.5299530029296875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0003006458282470703 time spend to save output : 1.2543880939483643 total time spend for step 2 : 1.2546887397766113 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252784': [(1171252784, 'jrm', 0.967751, 4674, '3609'), 'temp/1741423278_3770391_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252487': [(1171252487, 'jrm', 0.92627835, 4674, '3609'), 'temp/1741423278_3770391_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'], '1171252764': [(1171252764, 'jrm', 0.98535776, 4674, '3609'), 'temp/1741423278_3770391_1171252764_29d5179a892cc50aadc9d67245534b59.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 : 0.24113821983337402 #### 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 8 09:41:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741423302_3770391_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1741423302_3770391_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1741423302_3770391_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1741423302_3770391_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1741423302_3770391_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1741423302_3770391_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 inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 2281 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 2283 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 2279 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 2060 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 865 wait 20 seconds l 3637 free memory gpu now : 865 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 : 8.558380842208862 time used to load_weights : 0.14217209815979004 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.3208510875701904 ['temp/1741423302_3770391_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1741423302_3770391_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'temp/1741423302_3770391_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 : 0.04698038101196289 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 : 2.7960801124572754 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 : 0.01256871223449707 save_final save missing photos in datou_result : time spend for datou_step_exec : 125.26189970970154 time spend to save output : 0.012924671173095703 total time spend for step 1 : 125.27482438087463 step2:argmax Sat Mar 8 09:43:47 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/1741423302_3770391_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1741423302_3770391_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1741423302_3770391_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1741423302_3770391_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1741423302_3770391_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1741423302_3770391_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', 0.970701, 4723, '3655'), 'temp/1741423302_3770391_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.9651307, 4723, '3655'), 'temp/1741423302_3770391_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96740913, 4723, '3655'), 'temp/1741423302_3770391_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 : 1.7029199600219727 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 : 1.697652816772461 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', '0.970701', None), ('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.9651307', None), ('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.96740913', None)] time used for this insertion : 0.013000249862670898 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 : 4.291534423828125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0001747608184814453 time spend to save output : 3.41817045211792 total time spend for step 2 : 3.4183452129364014 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', 0.970701, 4723, '3655'), 'temp/1741423302_3770391_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.9651307, 4723, '3655'), 'temp/1741423302_3770391_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.96740913, 4723, '3655'), 'temp/1741423302_3770391_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 : 0.6244192123413086 #### 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 8 09:47:27 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1741423646_3770391_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/1741423646_3770391_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/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1741423646_3770391_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/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1741423646_3770391_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/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1741423646_3770391_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/1741423656_3770391 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 3.072129487991333 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1341799939, 'temp/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1341799942, 'temp/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1341799943} 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 : 11.168884515762329 time spend to save output : 0.00015807151794433594 total time spend for step 1 : 11.169042587280273 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 /1341799939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341799942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1341799943Didn'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, '1341799939', 'None', None, None, None, None, None), ('230', None, '1341799942', 'None', None, None, None, None, None), ('230', None, '1341799943', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.012819766998291016 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1341799939: ['917849322', 'temp/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1341799942: ['917849322', 'temp/1741423646_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1341799943: ['917849322', 'temp/1741423646_3770391_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 : 0.255277156829834 #### 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 8 09:47:38 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/1741423658_3770391_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1741423658_3770391_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 : 0.0007097721099853516 time to convert the images to numpy array : 1.314312219619751 total time to convert the images to numpy array : 1.3159685134887695 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 : 1307 wait 20 seconds l 3637 free memory gpu now : 1307 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 : 1307 wait 20 seconds WARNING: Logging before InitGoogleLogging() is written to STDERR F0308 09:48:29.703604 3770391 syncedmem.cpp:78] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Command terminated by signal 6 73.22user 49.05system 13:06.22elapsed 15%CPU (0avgtext+0avgdata 5050552maxresident)k 8487832inputs+27912outputs (51934major+5823495minor)pagefaults 0swaps