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 : 5665 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 : 0.10808610916137695 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 Mon Jul 7 18:35:28 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 : 5665 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 2025-07-07 18:35:30.732301: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-07-07 18:35:30.763401: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493035000 Hz 2025-07-07 18:35:30.764987: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9534000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-07-07 18:35:30.765056: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-07-07 18:35:30.767553: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-07-07 18:35:30.890075: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x21927010 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-07-07 18:35:30.890131: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-07-07 18:35:30.891348: 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 2025-07-07 18:35:30.891788: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:35:30.894777: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:35:30.897644: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:35:30.898128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:35:30.900929: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:35:30.901918: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:35:30.906188: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:35:30.907366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:35:30.907443: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:35:30.908043: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-07-07 18:35:30.908058: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-07-07 18:35:30.908066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-07-07 18:35:30.909048: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5183 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 2025-07-07 18:35:31.458437: 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 2025-07-07 18:35:31.458552: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:35:31.458571: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:35:31.458588: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:35:31.458603: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:35:31.458617: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:35:31.458647: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:35:31.458664: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:35:31.459665: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:35:31.460787: 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 2025-07-07 18:35:31.460836: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:35:31.460851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:35:31.460866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:35:31.460880: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:35:31.460894: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:35:31.460908: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:35:31.460923: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:35:31.461880: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:35:31.461917: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-07-07 18:35:31.461926: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-07-07 18:35:31.461934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-07-07 18:35:31.462946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5183 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 : [] 2025-07-07 18:35:38.199757: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:35:38.343362: 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 290709 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 376 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 : 5665 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 : 0.0005977153778076172 nb_pixel_total : 15553 time to create 1 rle with old method : 0.017195940017700195 length of segment : 256 time for calcul the mask position with numpy : 0.002825498580932617 nb_pixel_total : 145327 time to create 1 rle with old method : 0.15765905380249023 length of segment : 371 time for calcul the mask position with numpy : 0.00023126602172851562 nb_pixel_total : 14256 time to create 1 rle with old method : 0.01541757583618164 length of segment : 151 time for calcul the mask position with numpy : 0.00011229515075683594 nb_pixel_total : 5613 time to create 1 rle with old method : 0.006581306457519531 length of segment : 48 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 1825 time to create 1 rle with old method : 0.002325773239135742 length of segment : 39 time spent for convertir_results : 1.0061044692993164 time spend for datou_step_exec : 16.685859441757202 time spend to save output : 6.175041198730469e-05 total time spend for step 1 : 16.68592119216919 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 3355 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 : 0.012783050537109375 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, 0.9954894, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (127, 30, 27), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 106), (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, 46), (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)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,109,76,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,41,163,40,166,34,172,29,188,26,193,25,200,25,219,24,220,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923717, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 310), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 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(32, 234, 498), (32, 235, 497), (31, 236, 496), (31, 237, 495), (31, 238, 494), (31, 239, 493), (31, 240, 491), (31, 241, 490), (31, 242, 488), (31, 243, 487), (31, 244, 486), (31, 245, 485), (31, 246, 483), (31, 247, 482), (31, 248, 480), (31, 249, 479), (31, 250, 477), (31, 251, 475), (31, 252, 473), (31, 253, 472), (31, 254, 470), (31, 255, 468), (31, 256, 467), (31, 257, 465), (31, 258, 464), (31, 259, 463), (31, 260, 462), (31, 261, 461), (31, 262, 459), (31, 263, 458), (31, 264, 456), (31, 265, 455), (31, 266, 453), (31, 267, 451), (31, 268, 449), (31, 269, 448), (31, 270, 446), (31, 271, 445), (31, 272, 444), (31, 273, 443), (32, 274, 441), (32, 275, 440), (32, 276, 438), (32, 277, 437), (32, 278, 435), (32, 279, 434), (32, 280, 432), (33, 281, 429), (33, 282, 427), (33, 283, 426), (33, 284, 424), (33, 285, 423), (34, 286, 421), (34, 287, 420), (34, 288, 419), (35, 289, 416), (35, 290, 415), (35, 291, 414), (36, 292, 411), (36, 293, 410), (37, 294, 407), (37, 295, 406), (38, 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['598,172,591,172,576,167,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,501,40,514,32,517,29,531,25,539,25,540,24,560,24,561,25,579,25,580,26,593,26,594,25,625,25,628,24,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8299076, [(292, 3, 128), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 44), 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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)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1751906128_290448_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5665 ############################### 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 : 0.16420674324035645 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 Mon Jul 7 18:35:47 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 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 : 5665 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-07-07 18:35:50.439681: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-07-07 18:35:50.463364: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493035000 Hz 2025-07-07 18:35:50.465370: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9534000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-07-07 18:35:50.465416: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-07-07 18:35:50.468852: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-07-07 18:35:50.619105: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x215f37c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-07-07 18:35:50.619177: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-07-07 18:35:50.620358: 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 2025-07-07 18:35:50.620769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:35:50.623590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:35:50.625751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:35:50.626098: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:35:50.628392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:35:50.629608: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:35:50.634350: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:35:50.635749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:35:50.635820: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:35:50.636545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-07-07 18:35:50.636563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-07-07 18:35:50.636573: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-07-07 18:35:50.637668: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5183 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. 2025-07-07 18:35:50.712801: 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 2025-07-07 18:35:50.712889: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:35:50.712912: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:35:50.712933: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:35:50.712954: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:35:50.712974: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:35:50.712994: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:35:50.713029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:35:50.714234: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:35:50.715331: 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 2025-07-07 18:35:50.715371: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:35:50.715392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:35:50.715412: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:35:50.715431: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:35:50.715451: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:35:50.715471: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:35:50.715491: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:35:50.716677: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:35:50.716708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-07-07 18:35:50.716718: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-07-07 18:35:50.716727: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-07-07 18:35:50.717967: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5183 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 : [] 2025-07-07 18:35:57.450908: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:35:57.630642: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 291326 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 376 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 : 5665 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 : 0.0006947517395019531 nb_pixel_total : 16902 time to create 1 rle with old method : 0.022854089736938477 length of segment : 107 time for calcul the mask position with numpy : 0.021437883377075195 nb_pixel_total : 480747 time to create 1 rle with new method : 0.0326380729675293 length of segment : 632 time for calcul the mask position with numpy : 0.0009443759918212891 nb_pixel_total : 36642 time to create 1 rle with old method : 0.0395503044128418 length of segment : 133 time for calcul the mask position with numpy : 0.00010585784912109375 nb_pixel_total : 4794 time to create 1 rle with old method : 0.005505084991455078 length of segment : 51 time spent for convertir_results : 0.3059663772583008 time spend for datou_step_exec : 15.452463150024414 time spend to save output : 5.340576171875e-05 total time spend for step 1 : 15.452516555786133 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 436 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 : 0.012740612030029297 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, 0.99883705, [(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, 0.9977488, [(711, 22, 21), (926, 22, 46), (608, 23, 146), (894, 23, 103), (598, 24, 233), (851, 24, 157), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), 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492601069, 445, 390, 550, 0, 54, 0.9391133, [(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (419, 25, 100), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['450,47,449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,419,25,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46,451,46'])], 'temp/1751906147_290448_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 : 0.12322783470153809 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 Mon Jul 7 18:36:05 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 : 5665 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-07-07 18:36:07.840327: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-07-07 18:36:07.867403: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493035000 Hz 2025-07-07 18:36:07.869629: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9538000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-07-07 18:36:07.869692: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-07-07 18:36:07.873568: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-07-07 18:36:08.035228: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x21f2a960 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-07-07 18:36:08.035309: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-07-07 18:36:08.036589: 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 2025-07-07 18:36:08.037045: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:36:08.040069: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:36:08.042965: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:36:08.043505: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:36:08.046250: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:36:08.047663: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:36:08.053720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:36:08.055349: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:36:08.055470: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:36:08.056287: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-07-07 18:36:08.056305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-07-07 18:36:08.056315: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-07-07 18:36:08.057387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5183 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. 2025-07-07 18:36:08.134807: 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 2025-07-07 18:36:08.134930: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:36:08.134949: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:36:08.134966: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:36:08.134981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:36:08.134997: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:36:08.135012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:36:08.135028: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:36:08.135990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:36:08.137126: 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 2025-07-07 18:36:08.137179: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:36:08.137201: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:36:08.137220: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:36:08.137240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:36:08.137259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:36:08.137279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:36:08.137298: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:36:08.138458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:36:08.138494: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-07-07 18:36:08.138505: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-07-07 18:36:08.138514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-07-07 18:36:08.139761: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5183 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 : [] 2025-07-07 18:36:15.578746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:36:15.725405: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 291993 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 376 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 : 5665 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 : 0.4201629161834717 nb_pixel_total : 3689467 time to create 1 rle with new method : 0.23763322830200195 length of segment : 2038 time spent for convertir_results : 1.4602396488189697 time spend for datou_step_exec : 17.451577186584473 time spend to save output : 1.9311904907226562e-05 total time spend for step 1 : 17.45159649848938 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 722 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 : 0.012384891510009766 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, 2264, 122, 2241, 0.9849074, [(523, 124, 443), (1150, 124, 275), (501, 125, 949), (480, 126, 993), (461, 127, 1035), (443, 128, 1095), (427, 129, 1152), (411, 130, 1177), (397, 131, 1200), (383, 132, 1222), (370, 133, 1243), (367, 134, 1253), (364, 135, 1264), (361, 136, 1274), (359, 137, 1282), (356, 138, 1292), (354, 139, 1300), (352, 140, 1308), (349, 141, 1315), (347, 142, 1322), (345, 143, 1328), (343, 144, 1334), (341, 145, 1340), (339, 146, 1345), (337, 147, 1351), (336, 148, 1356), (334, 149, 1361), (332, 150, 1366), (331, 151, 1370), (329, 152, 1376), (328, 153, 1380), (326, 154, 1385), (325, 155, 1388), (323, 156, 1393), (322, 157, 1397), (321, 158, 1401), (319, 159, 1405), (318, 160, 1409), (317, 161, 1412), (316, 162, 1416), (315, 163, 1419), (313, 164, 1424), (312, 165, 1428), (311, 166, 1431), (309, 167, 1435), (308, 168, 1438), (306, 169, 1442), (305, 170, 1446), (303, 171, 1450), (302, 172, 1453), (300, 173, 1458), (298, 174, 1462), (297, 175, 1466), (295, 176, 1470), (293, 177, 1475), (291, 178, 1480), (289, 179, 1485), (287, 180, 1490), (284, 181, 1496), (281, 182, 1502), (279, 183, 1507), (276, 184, 1514), (273, 185, 1520), (271, 186, 1526), (268, 187, 1533), (265, 188, 1540), (263, 189, 1546), (260, 190, 1553), (257, 191, 1560), (254, 192, 1569), (252, 193, 1577), (249, 194, 1586), (246, 195, 1595), (243, 196, 1605), (240, 197, 1614), (237, 198, 1623), (235, 199, 1631), (232, 200, 1640), (229, 201, 1649), (226, 202, 1658), (223, 203, 1666), (220, 204, 1675), (217, 205, 1684), (214, 206, 1689), (211, 207, 1694), (208, 208, 1699), (206, 209, 1703), (204, 210, 1707), (203, 211, 1709), (201, 212, 1713), (199, 213, 1716), (198, 214, 1719), (196, 215, 1722), (195, 216, 1725), (193, 217, 1728), (192, 218, 1730), (190, 219, 1734), (189, 220, 1736), (188, 221, 1738), (186, 222, 1741), (185, 223, 1743), (184, 224, 1745), (183, 225, 1747), (182, 226, 1749), (180, 227, 1752), (179, 228, 1754), (178, 229, 1756), (177, 230, 1758), (176, 231, 1760), (175, 232, 1762), (174, 233, 1764), (174, 234, 1765), (173, 235, 1766), (172, 236, 1768), (171, 237, 1770), (170, 238, 1772), (169, 239, 1773), (168, 240, 1775), (167, 241, 1777), (166, 242, 1779), (165, 243, 1780), (164, 244, 1782), (163, 245, 1784), (162, 246, 1786), (161, 247, 1788), (160, 248, 1790), (159, 249, 1792), (158, 250, 1794), (157, 251, 1796), (156, 252, 1798), (155, 253, 1800), (153, 254, 1803), (152, 255, 1805), (151, 256, 1807), (150, 257, 1809), (148, 258, 1812), (147, 259, 1814), (146, 260, 1817), (144, 261, 1820), (143, 262, 1822), (141, 263, 1826), (140, 264, 1828), (138, 265, 1831), (136, 266, 1835), (135, 267, 1837), (133, 268, 1841), (131, 269, 1845), (130, 270, 1847), (128, 271, 1851), (126, 272, 1855), (125, 273, 1858), (124, 274, 1860), (122, 275, 1864), (121, 276, 1866), (120, 277, 1869), (118, 278, 1872), (117, 279, 1875), (116, 280, 1877), (115, 281, 1880), (114, 282, 1882), (113, 283, 1884), (112, 284, 1886), (111, 285, 1889), (110, 286, 1891), (109, 287, 1893), (108, 288, 1895), (107, 289, 1897), (106, 290, 1899), (106, 291, 1900), (105, 292, 1902), (104, 293, 1904), (103, 294, 1906), (102, 295, 1908), (102, 296, 1909), (101, 297, 1911), (100, 298, 1913), (100, 299, 1913), (99, 300, 1915), (98, 301, 1917), (98, 302, 1918), (97, 303, 1920), (96, 304, 1921), (96, 305, 1922), (95, 306, 1924), (95, 307, 1924), (94, 308, 1926), (93, 309, 1928), (93, 310, 1928), (92, 311, 1930), (92, 312, 1931), (92, 313, 1931), (92, 314, 1932), (91, 315, 1933), (91, 316, 1934), (91, 317, 1934), (91, 318, 1935), (90, 319, 1936), (90, 320, 1937), (90, 321, 1937), (90, 322, 1938), (89, 323, 1939), (89, 324, 1940), (89, 325, 1941), (89, 326, 1941), (88, 327, 1943), (88, 328, 1943), (88, 329, 1944), (87, 330, 1946), (87, 331, 1946), (87, 332, 1947), (87, 333, 1948), (86, 334, 1949), (86, 335, 1950), (86, 336, 1951), (86, 337, 1952), (85, 338, 1953), (85, 339, 1954), (85, 340, 1955), (84, 341, 1957), (84, 342, 1957), (84, 343, 1958), (84, 344, 1959), (83, 345, 1961), (83, 346, 1962), (83, 347, 1962), (82, 348, 1964), (82, 349, 1965), (82, 350, 1966), (82, 351, 1967), (81, 352, 1969), (81, 353, 1970), (81, 354, 1971), (80, 355, 1973), (80, 356, 1974), (80, 357, 1975), (80, 358, 1976), (79, 359, 1978), (79, 360, 1979), (79, 361, 1980), (78, 362, 1983), (78, 363, 1984), (78, 364, 1985), (77, 365, 1987), (77, 366, 1988), (77, 367, 1989), (76, 368, 1991), (76, 369, 1992), (76, 370, 1993), (76, 371, 1994), (75, 372, 1996), (75, 373, 1997), (75, 374, 1998), (74, 375, 2000), (74, 376, 2001), (74, 377, 2001), (73, 378, 2003), (73, 379, 2004), (73, 380, 2005), (72, 381, 2007), (72, 382, 2007), (72, 383, 2008), (71, 384, 2010), (71, 385, 2010), (71, 386, 2011), (70, 387, 2013), (70, 388, 2013), (70, 389, 2014), (69, 390, 2015), (69, 391, 2016), (69, 392, 2016), (69, 393, 2017), (68, 394, 2018), (68, 395, 2018), (68, 396, 2019), (67, 397, 2020), (67, 398, 2021), (67, 399, 2021), (66, 400, 2023), (66, 401, 2023), (65, 402, 2025), (65, 403, 2025), (65, 404, 2026), (64, 405, 2027), (64, 406, 2027), (64, 407, 2028), (63, 408, 2029), (63, 409, 2030), (63, 410, 2030), (62, 411, 2032), (62, 412, 2032), (61, 413, 2033), (61, 414, 2034), (61, 415, 2034), (60, 416, 2036), (60, 417, 2036), (59, 418, 2037), (59, 419, 2038), (59, 420, 2038), (58, 421, 2040), (58, 422, 2040), (57, 423, 2041), (57, 424, 2042), (56, 425, 2043), (56, 426, 2044), (55, 427, 2045), (55, 428, 2045), (55, 429, 2046), (54, 430, 2047), (54, 431, 2048), (53, 432, 2049), (53, 433, 2049), (52, 434, 2051), (52, 435, 2051), (51, 436, 2053), (51, 437, 2053), (50, 438, 2054), (50, 439, 2055), (49, 440, 2056), (49, 441, 2056), (48, 442, 2058), (47, 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'temp/1751906165_290448_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3685667 proportion of common points : 0.9982049104960465 [('test release memory', 'SUCCESS', True), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)] res_total : True #&_# TEST SUCCEEDED #&_# : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_73ab9d9ef59199f3c420665aa8d59830c97bf6e3 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_73ab9d9ef59199f3c420665aa8d59830c97bf6e3','{"mask_detection": "success"}','1','http://marlene.fotonower-preprod.com/job/2025/July/07072025/python_test3//data_4/data_log/job/2025/July/07072025/python_test3/log-python3----short_python3--v--marlene-18:35:01.txt','mask_detection','unknown'); #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4573 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4573 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4573 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4573 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : sam list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1189321094) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 1189321094 download finish for photo 1189321094 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.24341750144958496 #### 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 Mon Jul 7 18:36:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1751906189_290448_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1751906189_290448_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 : 0.0022034645080566406 nb_pixel_total : 5616 time to create 1 rle with old method : 0.0061016082763671875 time for calcul the mask position with numpy : 0.00135040283203125 nb_pixel_total : 4297 time to create 1 rle with old method : 0.004839897155761719 time for calcul the mask position with numpy : 0.0013642311096191406 nb_pixel_total : 8626 time to create 1 rle with old method : 0.009114980697631836 time for calcul the mask position with numpy : 0.001390695571899414 nb_pixel_total : 10769 time to create 1 rle with old method : 0.011419057846069336 time for calcul the mask position with numpy : 0.0014278888702392578 nb_pixel_total : 16409 time to create 1 rle with old method : 0.017670869827270508 time for calcul the mask position with numpy : 0.0018687248229980469 nb_pixel_total : 83793 time to create 1 rle with old method : 0.08746099472045898 time for calcul the mask position with numpy : 0.0016558170318603516 nb_pixel_total : 38686 time to create 1 rle with old method : 0.04129385948181152 time for calcul the mask position with numpy : 0.0014116764068603516 nb_pixel_total : 2949 time to create 1 rle with old method : 0.0033202171325683594 time for calcul the mask position with numpy : 0.0013420581817626953 nb_pixel_total : 3106 time to create 1 rle with old method : 0.003559589385986328 time for calcul the mask position with numpy : 0.0013921260833740234 nb_pixel_total : 3781 time to create 1 rle with old method : 0.004293680191040039 time for calcul the mask position with numpy : 0.0014026165008544922 nb_pixel_total : 2782 time to create 1 rle with old method : 0.003099203109741211 time for calcul the mask position with numpy : 0.001477956771850586 nb_pixel_total : 13917 time to create 1 rle with old method : 0.018276214599609375 time for calcul the mask position with numpy : 0.0014867782592773438 nb_pixel_total : 29436 time to create 1 rle with old method : 0.03151822090148926 time for calcul the mask position with numpy : 0.0013246536254882812 nb_pixel_total : 4271 time to create 1 rle with old method : 0.00452113151550293 time for calcul the mask position with numpy : 0.001302480697631836 nb_pixel_total : 1579 time to create 1 rle with old method : 0.0017390251159667969 time for calcul the mask position with numpy : 0.0012972354888916016 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0014247894287109375 time for calcul the mask position with numpy : 0.0013074874877929688 nb_pixel_total : 2410 time to create 1 rle with old method : 0.002941608428955078 time for calcul the mask position with numpy : 0.0013272762298583984 nb_pixel_total : 2374 time to create 1 rle with old method : 0.0026235580444335938 time for calcul the mask position with numpy : 0.0014157295227050781 nb_pixel_total : 3952 time to create 1 rle with old method : 0.004515409469604492 time for calcul the mask position with numpy : 0.0013051033020019531 nb_pixel_total : 573 time to create 1 rle with old method : 0.0006716251373291016 time for calcul the mask position with numpy : 0.0013761520385742188 nb_pixel_total : 16375 time to create 1 rle with old method : 0.01722240447998047 time for calcul the mask position with numpy : 0.001420736312866211 nb_pixel_total : 13070 time to create 1 rle with old method : 0.014254093170166016 time for calcul the mask position with numpy : 0.001397848129272461 nb_pixel_total : 2259 time to create 1 rle with old method : 0.002485513687133789 time for calcul the mask position with numpy : 0.0013353824615478516 nb_pixel_total : 6637 time to create 1 rle with old method : 0.007334232330322266 time for calcul the mask position with numpy : 0.0013954639434814453 nb_pixel_total : 2079 time to create 1 rle with old method : 0.0021991729736328125 time for calcul the mask position with numpy : 0.0013337135314941406 nb_pixel_total : 7625 time to create 1 rle with old method : 0.008035421371459961 time for calcul the mask position with numpy : 0.0013172626495361328 nb_pixel_total : 5485 time to create 1 rle with old method : 0.005980968475341797 time for calcul the mask position with numpy : 0.001356363296508789 nb_pixel_total : 1743 time to create 1 rle with old method : 0.0019373893737792969 time for calcul the mask position with numpy : 0.001310586929321289 nb_pixel_total : 2727 time to create 1 rle with old method : 0.003126382827758789 time for calcul the mask position with numpy : 0.0013580322265625 nb_pixel_total : 1314 time to create 1 rle with old method : 0.0015709400177001953 time for calcul the mask position with numpy : 0.001318216323852539 nb_pixel_total : 3573 time to create 1 rle with old method : 0.0039463043212890625 time for calcul the mask position with numpy : 0.0013155937194824219 nb_pixel_total : 3917 time to create 1 rle with old method : 0.004446744918823242 time for calcul the mask position with numpy : 0.0013141632080078125 nb_pixel_total : 2450 time to create 1 rle with old method : 0.002727985382080078 time for calcul the mask position with numpy : 0.0014538764953613281 nb_pixel_total : 5366 time to create 1 rle with old method : 0.005955934524536133 time for calcul the mask position with numpy : 0.0013380050659179688 nb_pixel_total : 9901 time to create 1 rle with old method : 0.010931730270385742 time for calcul the mask position with numpy : 0.0013699531555175781 nb_pixel_total : 11902 time to create 1 rle with old method : 0.012889623641967773 time for calcul the mask position with numpy : 0.0012950897216796875 nb_pixel_total : 1025 time to create 1 rle with old method : 0.0011832714080810547 time for calcul the mask position with numpy : 0.0014042854309082031 nb_pixel_total : 2385 time to create 1 rle with old method : 0.002861499786376953 time for calcul the mask position with numpy : 0.0013735294342041016 nb_pixel_total : 3331 time to create 1 rle with old method : 0.003701448440551758 time for calcul the mask position with numpy : 0.0013456344604492188 nb_pixel_total : 10622 time to create 1 rle with old method : 0.011438846588134766 time for calcul the mask position with numpy : 0.0013027191162109375 nb_pixel_total : 1251 time to create 1 rle with old method : 0.0013806819915771484 time for calcul the mask position with numpy : 0.0012969970703125 nb_pixel_total : 342 time to create 1 rle with old method : 0.00041365623474121094 time for calcul the mask position with numpy : 0.0013628005981445312 nb_pixel_total : 14669 time to create 1 rle with old method : 0.015718936920166016 time for calcul the mask position with numpy : 0.0013499259948730469 nb_pixel_total : 4115 time to create 1 rle with old method : 0.004538059234619141 time for calcul the mask position with numpy : 0.0013103485107421875 nb_pixel_total : 4171 time to create 1 rle with old method : 0.004790544509887695 time for calcul the mask position with numpy : 0.001386404037475586 nb_pixel_total : 848 time to create 1 rle with old method : 0.0011098384857177734 time for calcul the mask position with numpy : 0.0013632774353027344 nb_pixel_total : 13130 time to create 1 rle with old method : 0.01445913314819336 time for calcul the mask position with numpy : 0.0013654232025146484 nb_pixel_total : 875 time to create 1 rle with old method : 0.0010793209075927734 time for calcul the mask position with numpy : 0.0013089179992675781 nb_pixel_total : 2382 time to create 1 rle with old method : 0.002640247344970703 time for calcul the mask position with numpy : 0.001299142837524414 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007052421569824219 time for calcul the mask position with numpy : 0.0012989044189453125 nb_pixel_total : 887 time to create 1 rle with old method : 0.0010340213775634766 time for calcul the mask position with numpy : 0.0012955665588378906 nb_pixel_total : 1673 time to create 1 rle with old method : 0.0019235610961914062 time for calcul the mask position with numpy : 0.001300811767578125 nb_pixel_total : 2768 time to create 1 rle with old method : 0.003093719482421875 time for calcul the mask position with numpy : 0.0013463497161865234 nb_pixel_total : 692 time to create 1 rle with old method : 0.0008890628814697266 time for calcul the mask position with numpy : 0.0014164447784423828 nb_pixel_total : 337 time to create 1 rle with old method : 0.00046181678771972656 time for calcul the mask position with numpy : 0.0013632774353027344 nb_pixel_total : 1712 time to create 1 rle with old method : 0.0019359588623046875 time for calcul the mask position with numpy : 0.001333475112915039 nb_pixel_total : 602 time to create 1 rle with old method : 0.0007190704345703125 time for calcul the mask position with numpy : 0.0013005733489990234 nb_pixel_total : 1075 time to create 1 rle with old method : 0.001257181167602539 time for calcul the mask position with numpy : 0.0013012886047363281 nb_pixel_total : 1055 time to create 1 rle with old method : 0.0012950897216796875 time for calcul the mask position with numpy : 0.0013904571533203125 nb_pixel_total : 1200 time to create 1 rle with old method : 0.0014331340789794922 time for calcul the mask position with numpy : 0.0013370513916015625 nb_pixel_total : 8605 time to create 1 rle with old method : 0.009224414825439453 time for calcul the mask position with numpy : 0.0014650821685791016 nb_pixel_total : 16711 time to create 1 rle with old method : 0.01935291290283203 time for calcul the mask position with numpy : 0.0013363361358642578 nb_pixel_total : 1623 time to create 1 rle with old method : 0.0019860267639160156 time for calcul the mask position with numpy : 0.0015213489532470703 nb_pixel_total : 27714 time to create 1 rle with old method : 0.030614614486694336 time for calcul the mask position with numpy : 0.0014238357543945312 nb_pixel_total : 18470 time to create 1 rle with old method : 0.019910335540771484 time for calcul the mask position with numpy : 0.0013930797576904297 nb_pixel_total : 8429 time to create 1 rle with old method : 0.00907754898071289 time for calcul the mask position with numpy : 0.0013194084167480469 nb_pixel_total : 1095 time to create 1 rle with old method : 0.0012450218200683594 time for calcul the mask position with numpy : 0.0013244152069091797 nb_pixel_total : 1513 time to create 1 rle with old method : 0.0017044544219970703 time for calcul the mask position with numpy : 0.0014033317565917969 nb_pixel_total : 9082 time to create 1 rle with old method : 0.009986400604248047 time for calcul the mask position with numpy : 0.0013339519500732422 nb_pixel_total : 267 time to create 1 rle with old method : 0.000347137451171875 time for calcul the mask position with numpy : 0.0013484954833984375 nb_pixel_total : 1348 time to create 1 rle with old method : 0.0015833377838134766 time for calcul the mask position with numpy : 0.001337289810180664 nb_pixel_total : 713 time to create 1 rle with old method : 0.0009424686431884766 time for calcul the mask position with numpy : 0.0014142990112304688 nb_pixel_total : 971 time to create 1 rle with old method : 0.001188516616821289 time for calcul the mask position with numpy : 0.0013666152954101562 nb_pixel_total : 3166 time to create 1 rle with old method : 0.003585338592529297 time for calcul the mask position with numpy : 0.0013628005981445312 nb_pixel_total : 9535 time to create 1 rle with old method : 0.010628461837768555 time for calcul the mask position with numpy : 0.0013263225555419922 nb_pixel_total : 1482 time to create 1 rle with old method : 0.0017321109771728516 time for calcul the mask position with numpy : 0.001332998275756836 nb_pixel_total : 248 time to create 1 rle with old method : 0.0003249645233154297 time for calcul the mask position with numpy : 0.0013072490692138672 nb_pixel_total : 616 time to create 1 rle with old method : 0.0007953643798828125 time for calcul the mask position with numpy : 0.001355886459350586 nb_pixel_total : 974 time to create 1 rle with old method : 0.0011608600616455078 time for calcul the mask position with numpy : 0.0014531612396240234 nb_pixel_total : 1204 time to create 1 rle with old method : 0.001514434814453125 time for calcul the mask position with numpy : 0.001413106918334961 nb_pixel_total : 221 time to create 1 rle with old method : 0.00032830238342285156 time for calcul the mask position with numpy : 0.0014061927795410156 nb_pixel_total : 735 time to create 1 rle with old method : 0.0009608268737792969 time for calcul the mask position with numpy : 0.0013620853424072266 nb_pixel_total : 1638 time to create 1 rle with old method : 0.0020399093627929688 time for calcul the mask position with numpy : 0.0014197826385498047 nb_pixel_total : 914 time to create 1 rle with old method : 0.0013079643249511719 time for calcul the mask position with numpy : 0.0014491081237792969 nb_pixel_total : 299 time to create 1 rle with old method : 0.000457763671875 time for calcul the mask position with numpy : 0.0014538764953613281 nb_pixel_total : 1442 time to create 1 rle with old method : 0.0017812252044677734 time for calcul the mask position with numpy : 0.0014002323150634766 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007503032684326172 time for calcul the mask position with numpy : 0.0014154911041259766 nb_pixel_total : 7500 time to create 1 rle with old method : 0.008528947830200195 time for calcul the mask position with numpy : 0.0014772415161132812 nb_pixel_total : 1124 time to create 1 rle with old method : 0.0014331340789794922 time for calcul the mask position with numpy : 0.0013744831085205078 nb_pixel_total : 1068 time to create 1 rle with old method : 0.001371145248413086 time for calcul the mask position with numpy : 0.0014612674713134766 nb_pixel_total : 1627 time to create 1 rle with old method : 0.0018928050994873047 time for calcul the mask position with numpy : 0.0013494491577148438 nb_pixel_total : 2199 time to create 1 rle with old method : 0.002621173858642578 time for calcul the mask position with numpy : 0.0013551712036132812 nb_pixel_total : 889 time to create 1 rle with old method : 0.0011243820190429688 time for calcul the mask position with numpy : 0.0013492107391357422 nb_pixel_total : 486 time to create 1 rle with old method : 0.0005939006805419922 time for calcul the mask position with numpy : 0.0013988018035888672 nb_pixel_total : 2675 time to create 1 rle with old method : 0.0031976699829101562 time for calcul the mask position with numpy : 0.0013883113861083984 nb_pixel_total : 947 time to create 1 rle with old method : 0.001165151596069336 time for calcul the mask position with numpy : 0.0014352798461914062 nb_pixel_total : 885 time to create 1 rle with old method : 0.0011191368103027344 time for calcul the mask position with numpy : 0.001312255859375 nb_pixel_total : 1438 time to create 1 rle with old method : 0.0017635822296142578 time for calcul the mask position with numpy : 0.0013823509216308594 nb_pixel_total : 5019 time to create 1 rle with old method : 0.0057446956634521484 time for calcul the mask position with numpy : 0.0014414787292480469 nb_pixel_total : 829 time to create 1 rle with old method : 0.000986337661743164 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 100 chid ids of type : 4677 Number RLEs to save : 8973 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3874988827', '464', '201', '4') ... last line : ('3874988926', '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 : 0.016989469528198242 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.292890310287476 time spend to save output : 0.017187833786010742 total time spend for step 1 : 12.310078144073486 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1751906189_290448_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 100 ############################### 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 : 0.11593270301818848 #### 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 Mon Jul 7 18:36:41 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/1751906201_290448_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1751906201_290448_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/1751906201_290448_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 0.078s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.06383882 (374.1266, 293.9193, 430.81055, 317.80872) proba : 0.05222086 (382.17804, 297.1888, 552.35767, 344.658) proba : 0.012270783 (345.35638, 272.43033, 468.85638, 320.72354) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 2.3112549781799316 time spend to save output : 6.031990051269531e-05 total time spend for step 1 : 2.3113152980804443 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, 0.06383882, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05222086, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012270783, 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 : 0.013497352600097656 [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 : 0.014028072357177734 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, 0.06383882, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05222086, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012270783, None)], 'temp/1751906201_290448_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 : 0.10649371147155762 #### 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 Mon Jul 7 18:36:44 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1751906204_290448_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1751906204_290448_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 : 0.006629228591918945 time to convert the images to numpy array : 0.0013387203216552734 total time to convert the images to numpy array : 0.00831913948059082 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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'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 1568 wait 20 seconds l 3637 free memory gpu now : 1568 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 havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 1568 wait 20 seconds l 3637 free memory gpu now : 1568 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.013601541519165039 time used to do the prediction : 0.06942915916442871 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 : 0.050714969635009766 Testing : ['916235064'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (916235064) Catched exception ! Connect or reconnect ! result : {916235064: {'photo_id': 916235064, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2017/10/14/6293d1bb790dc6902450e7c572b7d10b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': None}} list_photo_exists : [916235064] storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.671619176864624 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', 0.0018815518, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011635364, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.0008159844, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.001177354, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.0025849724, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.004170459, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.0034806312, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.007367637, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021890253, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.00057988864, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.0045911074, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031588664, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.002250645, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.005320855, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0010998031, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.0054008826, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.0039194627, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014789057, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.001977846, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.001244069, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015049402, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021693793, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.0027086996, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.0047018793, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019415268, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.00095853995, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.00077000906, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019469515, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013726737, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.0016201461, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013926498, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010042838, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010792998, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.0008447561, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.0012518973, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.0025844472, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012429358, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.0028647173, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012950252, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010339241, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018766401, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011345748, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.001576117, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.003979351, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.0028739928, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023357493, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012108019, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011447881, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.0022335649, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.001746431, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.004700252, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021916234, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022625816, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.001817414, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.0009056674, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.002040499, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025336249, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.0044894563, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034260787, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.003134751, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.001025379, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.0015355756, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.0005915148, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.004030839, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011586195, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012157564, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014967492, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.0011319173, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010544417, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011983685, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.0041846535, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.00236367, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.001632634, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.001556632, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.001395636, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.002197937, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.008827685, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.001461694, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.00088263495, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.0024715587, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010093177, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015964449, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.0015330622, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.0014918541, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014587478, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.005646159, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.001116525, 332, '355'), 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('916235064', 'viano_1027_gao__port_506211', 0.0026946848, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.000832052, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037378133, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.0007920008, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030533543, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.0037226337, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017739829, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015305659, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004086502, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011861372, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.0026957432, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.00114087, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012529603, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.001514874, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031655142, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017347152, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.016723158, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018258448, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.0021464042, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014153376, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.0020549404, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.0022965863, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013945533, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.01196849, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010549567, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020716274, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017869622, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.0057217074, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.002766275, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.001714441, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.0048248186, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018689259, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012581785, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012626213, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.0009245574, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.0074676974, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.0007290864, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.0010386184, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.00069111906, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.00214202, 332, '355')]]} begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 6.4373016357421875e-06 save missing photos in datou_result : time spend for datou_step_exec : 45.69070887565613 time spend to save output : 2.23958420753479 total time spend for step 1 : 47.93029308319092 step2:argmax Mon Jul 7 18:37:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1751906204_290448_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1751906204_290448_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', 0.017710231, 332, '355'), 'temp/1751906204_290448_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 : 0.02346968650817871 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 : 0.018729686737060547 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', '0.017710231', None)] time used for this insertion : 0.016819238662719727 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 3.5762786865234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00023674964904785156 time spend to save output : 0.05928182601928711 total time spend for step 2 : 0.05951857566833496 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', 0.017710231, 332, '355'), 'temp/1751906204_290448_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 : 0.19290900230407715 #### 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 Mon Jul 7 18:37:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1751906252_290448_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1751906252_290448_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1751906252_290448_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764} map_photo_id_path_extension : {1171252784: {'path': 'temp/1751906252_290448_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1751906252_290448_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1751906252_290448_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 havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1568 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1568 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1568 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1568 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1568 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1568 wait 20 seconds 2025-07-07 18:39:41.255236: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-07-07 18:39:41.255913: 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 2025-07-07 18:39:41.256031: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:39:41.256094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:39:41.258688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:39:41.258909: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:39:41.261911: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:39:41.263441: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:39:41.268358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:39:41.269377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:39:41.269772: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-07-07 18:39:41.299537: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493035000 Hz 2025-07-07 18:39:41.301508: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f929c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-07-07 18:39:41.301557: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-07-07 18:39:41.305111: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2b421a00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-07-07 18:39:41.305143: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-07-07 18:39:41.306094: 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 2025-07-07 18:39:41.306212: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:39:41.306243: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-07-07 18:39:41.306331: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-07-07 18:39:41.306370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-07-07 18:39:41.306417: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-07-07 18:39:41.306469: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-07-07 18:39:41.306521: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-07-07 18:39:41.307726: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-07-07 18:39:41.307804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-07-07 18:39:41.307871: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-07-07 18:39:41.307888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-07-07 18:39:41.307900: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-07-07 18:39:41.309125: 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 : 1568 max_wait_temp : 6 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 : [] 2025-07-07 18:39:47.965310: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.02G (3246391296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-07-07 18:39:47.965857: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.72G (2921752064 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-07-07 18:39:47.966400: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.45G (2629576704 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-07-07 18:39:47.966912: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2366618880 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-07-07 18:39:47.967498: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.98G (2129957120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-07-07 18:39:47.968053: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.79G (1916961536 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-07-07 18:39:47.968615: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.61G (1725265408 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 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 ERROR in datou_step_exec, will save and exit ! assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3139, in datou_step_tfhub2 this_model = model_evaluator(model_name, model_type=model_type, fc_size=fc_size,use_multi_inputs=use_multi_inputs) File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 156, in __init__ self.model, _, _ = create_tfhub_model(module_handle=self.tfhub_module, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 77, in create_tfhub_model hub.KerasLayer(module_handle, trainable=do_fine_tuning, name="module"), File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 152, in __init__ self._func = load_module(handle, tags, self._load_options) File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 421, in load_module return module_v2.load(handle, tags=tags, options=set_load_options) File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_v2.py", line 106, in load obj = tf.compat.v1.saved_model.load_v2(module_path, tags=tags) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 578, in load return load_internal(export_dir, tags) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 602, in load_internal loader = loader_cls(object_graph_proto, File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 123, in __init__ self._load_all() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 134, in _load_all self._load_nodes() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 264, in _load_nodes node, setter = self._recreate(proto, node_id) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 370, in _recreate return factory[kind]() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 363, in "variable": lambda: self._recreate_variable(proto.variable), File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 426, in _recreate_variable return variables.Variable( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 261, in __call__ return cls._variable_v2_call(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 243, in _variable_v2_call return previous_getter( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 66, in getter return captured_getter(captured_previous, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 418, in uninitialized_variable_creator return resource_variable_ops.UninitializedVariable(**kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 263, in __call__ return super(VariableMetaclass, cls).__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 1795, in __init__ handle = _variable_handle_from_shape_and_dtype( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 174, in _variable_handle_from_shape_and_dtype gen_logging_ops._assert( # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py", line 55, in _assert _ops.raise_from_not_ok_status(e, name) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 6653, in raise_from_not_ok_status six.raise_from(core._status_to_exception(e.code, message), None) File "", line 3, in raise_from [1171252784, 1171252487, 1171252764] map_info['map_portfolio_photo'] : {} final : True mtd_id 4567 list_pids : [1171252784, 1171252487, 1171252764] 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', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4567', None, '1171252487', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4567', None, '1171252764', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None)] time used for this insertion : 0.01717090606689453 save_final ERROR in last step tfhub_classification2, assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse time spend for datou_step_exec : 135.65711522102356 time spend to save output : 0.0192110538482666 total time spend for step 0 : 135.67632627487183 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171252784': [(1171252784, 'jrm', 0.9677492, 4674, '3609'), 'temp/1687511175_1882837_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853587, 4674, '3609'), 'temp/1687511175_1882837_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262757, 4674, '3609'), 'temp/1687511175_1882837_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} got : None ######################## 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 : 0.2538893222808838 #### 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 Mon Jul 7 18:39:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1751906388_290448_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1751906388_290448_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1751906388_290448_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1751906388_290448_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1751906388_290448_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1751906388_290448_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3655': 1} we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 8 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 8 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 8 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 8 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 8 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 8 wait 20 seconds l 3637 free memory gpu now : 8 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 ERROR in datou_step_exec, will save and exit ! assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3139, in datou_step_tfhub2 this_model = model_evaluator(model_name, model_type=model_type, fc_size=fc_size,use_multi_inputs=use_multi_inputs) File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 156, in __init__ self.model, _, _ = create_tfhub_model(module_handle=self.tfhub_module, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 62, in create_tfhub_model fe_layer = hub.KerasLayer(module_handle, trainable=do_fine_tuning, name="module", File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 152, in __init__ self._func = load_module(handle, tags, self._load_options) File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 421, in load_module return module_v2.load(handle, tags=tags, options=set_load_options) File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_v2.py", line 106, in load obj = tf.compat.v1.saved_model.load_v2(module_path, tags=tags) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 578, in load return load_internal(export_dir, tags) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 602, in load_internal loader = loader_cls(object_graph_proto, File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 123, in __init__ self._load_all() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 134, in _load_all self._load_nodes() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 264, in _load_nodes node, setter = self._recreate(proto, node_id) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 370, in _recreate return factory[kind]() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 363, in "variable": lambda: self._recreate_variable(proto.variable), File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 426, in _recreate_variable return variables.Variable( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 261, in __call__ return cls._variable_v2_call(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 243, in _variable_v2_call return previous_getter( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 66, in getter return captured_getter(captured_previous, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 418, in uninitialized_variable_creator return resource_variable_ops.UninitializedVariable(**kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 263, in __call__ return super(VariableMetaclass, cls).__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 1795, in __init__ handle = _variable_handle_from_shape_and_dtype( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 174, in _variable_handle_from_shape_and_dtype gen_logging_ops._assert( # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py", line 55, in _assert _ops.raise_from_not_ok_status(e, name) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 6653, in raise_from_not_ok_status six.raise_from(core._status_to_exception(e.code, message), None) File "", line 3, in raise_from [1171291875, 1171275314, 1171275372] map_info['map_portfolio_photo'] : {} final : True mtd_id 4621 list_pids : [1171291875, 1171275314, 1171275372] 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', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4621', None, '1171275314', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4621', None, '1171275372', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None)] time used for this insertion : 0.018529653549194336 save_final ERROR in last step tfhub_classification2, assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse time spend for datou_step_exec : 133.79113507270813 time spend to save output : 0.0190579891204834 total time spend for step 0 : 133.8101930618286 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171291875': [(1171291875, 'tapis_vide', 0.97062814, 4723, '3655'), 'temp/1691745841_1143057_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9674145, 4723, '3655'), 'temp/1691745841_1143057_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96509415, 4723, '3655'), 'temp/1691745841_1143057_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} got : None ERROR tfhub2 FAILED ############################### 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 : 0.16216111183166504 #### 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 Mon Jul 7 18:42:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1751906522_290448_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/1751906522_290448_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/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1751906522_290448_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/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1751906522_290448_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/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1751906522_290448_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/1751906523_290448 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.984184741973877 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1371054204, 'temp/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1371054205, 'temp/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1371054206} Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] After datou_step_exec type output : time spend for datou_step_exec : 1.2221035957336426 time spend to save output : 7.009506225585938e-05 total time spend for step 1 : 1.2221736907958984 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 /1371054204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1371054205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1371054206Didn'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, '1371054204', 'None', None, None, None, None, None), ('230', None, '1371054205', 'None', None, None, None, None, None), ('230', None, '1371054206', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.013271331787109375 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1371054204: ['917849322', 'temp/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1371054205: ['917849322', 'temp/1751906522_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1371054206: ['917849322', 'temp/1751906522_290448_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 : 0.24340343475341797 #### 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 Mon Jul 7 18:42:04 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1751906524_290448_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1751906524_290448_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 : 0.00020170211791992188 time to convert the images to numpy array : 0.9516110420227051 total time to convert the images to numpy array : 0.9524195194244385 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 : 6 wait 20 seconds l 3637 free memory gpu now : 6 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 : 6 wait 20 seconds WARNING: Logging before InitGoogleLogging() is written to STDERR F0707 18:42:52.225721 290448 syncedmem.cpp:78] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Command terminated by signal 6 51.54user 32.01system 7:27.32elapsed 18%CPU (0avgtext+0avgdata 5330564maxresident)k 5144584inputs+23184outputs (5498major+4372955minor)pagefaults 0swaps