python /home/admin/mtr/script_for_cron.py -j coverage -m 9 -a '' -s coverage -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded root_folder /data_4/data_log/job/2025/October/03102025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_4/data_log/job/2025/October/03102025/coverage/htmlcov new path : /data_4/data_log/job/2025/October/03102025/coverage/ command : coverage3 run /home/admin/workarea/git/Velours/python/tests/python_tests.py --short_python3 `cat ~/.fotonower_pass/bdd.py.pass` cat: /home/admin/.fotonower_pass/bdd.py.pass: Aucun fichier ou dossier de ce type import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data python version used : 3 #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5066 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 : 0.17067837715148926 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 Fri Oct 3 11:20:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 5066 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 2025-10-03 11:20:33.431756: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-10-03 11:20:33.439545: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-10-03 11:20:33.440965: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb544000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-10-03 11:20:33.441008: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-10-03 11:20:33.443335: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-10-03 11:20:33.571922: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x215daff0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-10-03 11:20:33.571964: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-10-03 11:20:33.573112: 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 2025-10-03 11:20:33.573504: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:20:33.593246: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:20:33.596402: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:20:33.596889: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:20:33.600728: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:20:33.602159: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:20:33.611707: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:20:33.613094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:20:33.613454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:20:33.614169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-10-03 11:20:33.614187: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-10-03 11:20:33.614197: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-10-03 11:20:33.615434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4614 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 2025-10-03 11:20:35.410407: 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 2025-10-03 11:20:35.410487: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:20:35.410509: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:20:35.410529: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:20:35.410548: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:20:35.410567: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:20:35.410585: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:20:35.410604: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:20:35.411770: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:20:35.413023: 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 2025-10-03 11:20:35.413074: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:20:35.413090: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:20:35.413107: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:20:35.413122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:20:35.413137: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:20:35.413151: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:20:35.413166: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:20:35.414117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:20:35.414144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-10-03 11:20:35.414153: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-10-03 11:20:35.414161: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-10-03 11:20:35.415140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4614 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 : [] 2025-10-03 11:20:45.160093: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:20:45.382812: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:20:45.710740: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 2025-10-03 11:20:45.713162: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 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 error in detect the image : temp/1759483229_3034557_957285035_a42482e51c93c8025d243dd179aee85b.jpg 2 root error(s) found. (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[node conv1/convolution (defined at usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:3007) ]] [[mrcnn_detection/map/while/LoopCond/_22/_118]] (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[node conv1/convolution (defined at usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:3007) ]] 0 successful operations. 0 derived errors ignored. [Op:__inference_keras_scratch_graph_13584] Function call stack: keras_scratch_graph -> keras_scratch_graph Detection mask done ! Trying to reset tf kernel 3034716 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 9 tf kernel not reseted sub process len(results) : 0 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 0 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 : 4523 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'] WARNING : results is empty ! time spent for convertir_results : 1.9773509502410889 time spend for datou_step_exec : 20.999831438064575 time spend to save output : 4.410743713378906e-05 total time spend for step 1 : 20.99987554550171 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 ! begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.041318655014038086 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[], 'temp/1759483229_3034557_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4523 ############################### 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 : 0.1820240020751953 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 Fri Oct 3 11:20:52 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 4523 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-10-03 11:20:55.559967: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-10-03 11:20:55.588589: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-10-03 11:20:55.590975: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb548000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-10-03 11:20:55.591039: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-10-03 11:20:55.595792: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-10-03 11:20:55.718207: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x21c20300 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-10-03 11:20:55.718260: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-10-03 11:20:55.719491: 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 2025-10-03 11:20:55.720400: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:20:55.724077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:20:55.727050: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:20:55.728497: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:20:55.731392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:20:55.733028: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:20:55.738950: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:20:55.740387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:20:55.740494: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:20:55.741228: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-10-03 11:20:55.741249: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-10-03 11:20:55.741261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-10-03 11:20:55.742491: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4071 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. 2025-10-03 11:20:55.876623: 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 2025-10-03 11:20:55.876766: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:20:55.877421: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:20:55.877461: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:20:55.877480: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:20:55.877498: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:20:55.877516: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:20:55.877534: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:20:55.878464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:20:55.879537: 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 2025-10-03 11:20:55.879584: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:20:55.879603: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:20:55.879620: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:20:55.879636: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:20:55.879653: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:20:55.879669: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:20:55.879686: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:20:55.880584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:20:55.880623: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-10-03 11:20:55.880632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-10-03 11:20:55.880640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-10-03 11:20:55.881601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4071 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 : [] 2025-10-03 11:21:05.279937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:21:05.471269: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:21:06.914067: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:06.914704: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.13G (3360168192 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:06.915263: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.82G (3024151296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.627239: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature. 2025-10-03 11:21:07.690191: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.690252: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.67GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.690863: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.690880: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.67GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.698127: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.698173: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.698751: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.698769: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.704674: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.704698: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.78GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.705276: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.705292: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.78GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.729228: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.729304: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 19.91MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.729323: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-10-03 11:21:07.730402: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.730429: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 16.00MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.731478: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.731505: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 16.00MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.738641: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.738667: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 63.85MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:07.739431: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.740239: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.741019: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.749182: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.749931: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.764019: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.764864: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.765673: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.766452: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.770729: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.771513: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.772291: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.773091: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.774228: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.784327: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.784986: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.795341: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.795986: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.796640: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.797287: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.797940: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-10-03 11:21:07.798586: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.48G (3733520384 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 3035995 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 2682 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 : 3875 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 : 0.0004994869232177734 nb_pixel_total : 16902 time to create 1 rle with old method : 0.038149118423461914 length of segment : 107 time for calcul the mask position with numpy : 0.09488749504089355 nb_pixel_total : 480741 time to create 1 rle with new method : 0.041069746017456055 length of segment : 632 time for calcul the mask position with numpy : 0.0004863739013671875 nb_pixel_total : 36642 time to create 1 rle with old method : 0.07920694351196289 length of segment : 133 time for calcul the mask position with numpy : 0.00011420249938964844 nb_pixel_total : 4793 time to create 1 rle with old method : 0.010878324508666992 length of segment : 51 time spent for convertir_results : 0.5140149593353271 time spend for datou_step_exec : 19.38765573501587 time spend to save output : 4.172325134277344e-05 total time spend for step 1 : 19.387697458267212 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 443 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 : 0.04031991958618164 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, 0.99883693, [(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, 0.9977483, [(711, 22, 22), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 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313), (383, 637, 305), (389, 638, 295), (395, 639, 282), (401, 640, 270), (408, 641, 256), (416, 642, 240), (432, 643, 216), (448, 644, 193), (465, 645, 169), (480, 646, 148), (495, 647, 126), (511, 648, 104), (526, 649, 81), (565, 650, 9)], ['526,649,416,642,368,634,341,627,307,613,243,590,220,577,186,566,144,539,102,509,91,496,70,447,63,388,65,329,86,265,91,237,101,216,134,183,187,156,225,151,279,135,318,130,358,116,416,103,493,45,527,36,608,23,754,24,893,24,925,22,996,23,1032,27,1082,52,1089,72,1088,172,1082,237,1045,305,1019,322,1002,338,950,373,885,432,865,446,851,473,822,505,810,528,786,554,772,586,725,621,683,638,606,649']), (917855882, 492601069, 445, 0, 440, 0, 116, 0.99194795, [(127, 1, 141), (94, 2, 206), (384, 2, 2), (59, 3, 273), (340, 3, 57), (22, 4, 381), (19, 5, 387), (16, 6, 392), (15, 7, 394), (14, 8, 396), (14, 9, 397), (13, 10, 399), (12, 11, 400), (12, 12, 400), (11, 13, 402), (10, 14, 403), (11, 15, 403), (11, 16, 404), (12, 17, 403), (12, 18, 404), (12, 19, 405), 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126), (9, 86, 75), (98, 86, 28), (142, 86, 117), (292, 86, 112), (9, 87, 71), (152, 87, 103), (294, 87, 110), (8, 88, 68), (161, 88, 91), (296, 88, 107), (8, 89, 63), (176, 89, 73), (297, 89, 106), (7, 90, 61), (205, 90, 40), (298, 90, 104), (7, 91, 57), (299, 91, 103), (6, 92, 54), (300, 92, 102), (6, 93, 50), (301, 93, 100), (7, 94, 46), (303, 94, 97), (7, 95, 44), (306, 95, 92), (7, 96, 42), (308, 96, 89), (7, 97, 40), (310, 97, 86), (7, 98, 38), (312, 98, 83), (8, 99, 34), (314, 99, 79), (8, 100, 32), (317, 100, 75), (8, 101, 29), (319, 101, 71), (13, 102, 19), (324, 102, 63), (20, 103, 6), (330, 103, 51), (337, 104, 37), (344, 105, 22), (352, 106, 3)], ['344,105,319,101,301,93,291,85,259,85,244,90,205,90,204,89,176,89,161,88,141,85,126,85,125,86,98,86,84,85,56,92,36,101,26,102,8,101,6,92,11,80,11,59,12,58,12,17,10,14,16,6,22,4,58,4,59,3,93,3,94,2,126,2,127,1,267,1,268,2,331,3,396,3,407,6,411,10,419,25,421,34,421,51,410,62,404,71,402,80,404,85,401,92,394,98,386,102,365,105']), (917855882, 492601069, 445, 390, 550, 0, 54, 0.93916905, [(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), (420, 25, 99), (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)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1759483252_3034557_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 : 0.2358536720275879 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 Fri Oct 3 11:21:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3875 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-10-03 11:21:16.378848: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-10-03 11:21:16.404599: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-10-03 11:21:16.406796: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb548000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-10-03 11:21:16.406859: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-10-03 11:21:16.411106: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-10-03 11:21:16.572307: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x221830f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-10-03 11:21:16.572391: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-10-03 11:21:16.573397: 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 2025-10-03 11:21:16.573902: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:21:16.577878: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:21:16.581848: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:21:16.582408: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:21:16.585917: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:21:16.587349: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:21:16.592781: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:21:16.593998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:21:16.594121: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:21:16.594694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-10-03 11:21:16.594711: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-10-03 11:21:16.594719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-10-03 11:21:16.595681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3423 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. 2025-10-03 11:21:16.737663: 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 2025-10-03 11:21:16.737812: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:21:16.737832: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:21:16.737849: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:21:16.737865: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:21:16.737881: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:21:16.737896: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:21:16.737913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:21:16.738746: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:21:16.739932: 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 2025-10-03 11:21:16.739971: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-10-03 11:21:16.739990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:21:16.740006: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-10-03 11:21:16.740023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-10-03 11:21:16.740039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-10-03 11:21:16.740055: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-10-03 11:21:16.740071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:21:16.740974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-10-03 11:21:16.741016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-10-03 11:21:16.741026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-10-03 11:21:16.741049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-10-03 11:21:16.741986: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3423 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 : [] 2025-10-03 11:21:28.483520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-10-03 11:21:28.706307: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-10-03 11:21:30.882453: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-10-03 11:21:30.882564: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 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 3036727 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 959 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 : 5066 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 : 0.49131345748901367 nb_pixel_total : 3697103 time to create 1 rle with new method : 0.7497749328613281 length of segment : 2044 time spent for convertir_results : 2.836552381515503 time spend for datou_step_exec : 24.898351192474365 time spend to save output : 3.62396240234375e-05 total time spend for step 1 : 24.89838743209839 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 724 chid ids of type : 445 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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.037364959716796875 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, 0, 2279, 103, 2222, 0.9819163, [(1250, 110, 26), (652, 111, 289), (1203, 111, 133), (614, 112, 376), (1081, 112, 346), (525, 113, 909), (518, 114, 922), (511, 115, 935), (505, 116, 948), (499, 117, 959), (492, 118, 972), (487, 119, 983), (481, 120, 994), (476, 121, 1004), (470, 122, 1015), (465, 123, 1024), (460, 124, 1034), (456, 125, 1042), (451, 126, 1052), (447, 127, 1061), (442, 128, 1076), (438, 129, 1089), (434, 130, 1102), (430, 131, 1115), (427, 132, 1126), (423, 133, 1138), (420, 134, 1149), (416, 135, 1161), (413, 136, 1172), (410, 137, 1180), (406, 138, 1187), (403, 139, 1193), (400, 140, 1200), (397, 141, 1206), (395, 142, 1211), (392, 143, 1217), (388, 144, 1225), (385, 145, 1231), (382, 146, 1238), (379, 147, 1245), (375, 148, 1252), (372, 149, 1259), (368, 150, 1267), (365, 151, 1275), (363, 152, 1281), (360, 153, 1288), (358, 154, 1295), (356, 155, 1302), (353, 156, 1310), (350, 157, 1318), (348, 158, 1325), (345, 159, 1333), (342, 160, 1341), (339, 161, 1349), (336, 162, 1358), (333, 163, 1367), (330, 164, 1376), (327, 165, 1385), (324, 166, 1395), (321, 167, 1404), (317, 168, 1415), (314, 169, 1426), (311, 170, 1436), (307, 171, 1448), (303, 172, 1461), (300, 173, 1472), (296, 174, 1485), (292, 175, 1497), (288, 176, 1510), (284, 177, 1523), (280, 178, 1536), (277, 179, 1548), (274, 180, 1559), (271, 181, 1567), (268, 182, 1574), (266, 183, 1580), (263, 184, 1588), (260, 185, 1595), (258, 186, 1600), (255, 187, 1607), (253, 188, 1613), (250, 189, 1619), (248, 190, 1625), (246, 191, 1630), (243, 192, 1636), (241, 193, 1641), (239, 194, 1646), (237, 195, 1651), (235, 196, 1656), (233, 197, 1661), (231, 198, 1665), (229, 199, 1670), (227, 200, 1674), (225, 201, 1679), (223, 202, 1683), (222, 203, 1686), (220, 204, 1691), (218, 205, 1695), (217, 206, 1697), (215, 207, 1700), (213, 208, 1704), (212, 209, 1706), (210, 210, 1709), (209, 211, 1711), (207, 212, 1715), (206, 213, 1717), (205, 214, 1719), (203, 215, 1722), (202, 216, 1724), (201, 217, 1726), (199, 218, 1729), (198, 219, 1730), (197, 220, 1732), (195, 221, 1735), (194, 222, 1737), (193, 223, 1739), (192, 224, 1741), (190, 225, 1744), (189, 226, 1746), (187, 227, 1749), (186, 228, 1751), (185, 229, 1753), (183, 230, 1756), (182, 231, 1758), (180, 232, 1762), (179, 233, 1764), (178, 234, 1766), (176, 235, 1769), (175, 236, 1771), (173, 237, 1774), (172, 238, 1776), (170, 239, 1780), (169, 240, 1782), (167, 241, 1785), (166, 242, 1787), (164, 243, 1791), (163, 244, 1793), (161, 245, 1796), (159, 246, 1799), (158, 247, 1802), (156, 248, 1805), (155, 249, 1808), (153, 250, 1811), (151, 251, 1814), (150, 252, 1817), (148, 253, 1820), (146, 254, 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311, 1937), (91, 312, 1938), (91, 313, 1938), (90, 314, 1940), (90, 315, 1940), (89, 316, 1942), (89, 317, 1942), (89, 318, 1943), (88, 319, 1944), (88, 320, 1945), (88, 321, 1945), (87, 322, 1947), (87, 323, 1948), (87, 324, 1948), (86, 325, 1950), (86, 326, 1950), (85, 327, 1952), (85, 328, 1952), (85, 329, 1953), (84, 330, 1954), (84, 331, 1955), (84, 332, 1955), (83, 333, 1957), (83, 334, 1957), (83, 335, 1958), (82, 336, 1959), (82, 337, 1960), (82, 338, 1961), (81, 339, 1962), (81, 340, 1963), (81, 341, 1963), (80, 342, 1965), (80, 343, 1965), (80, 344, 1966), (79, 345, 1967), (79, 346, 1968), (79, 347, 1968), (78, 348, 1970), (78, 349, 1971), (78, 350, 1971), (77, 351, 1973), (77, 352, 1973), (77, 353, 1974), (76, 354, 1975), (76, 355, 1976), (76, 356, 1977), (75, 357, 1978), (75, 358, 1979), (75, 359, 1979), (74, 360, 1981), (74, 361, 1981), (74, 362, 1982), (74, 363, 1983), (73, 364, 1984), (73, 365, 1985), (73, 366, 1985), (72, 367, 1987), (72, 368, 1988), (72, 369, 1988), (72, 370, 1989), (71, 371, 1991), (71, 372, 1992), (71, 373, 1993), (71, 374, 1993), (70, 375, 1995), (70, 376, 1996), (70, 377, 1997), (70, 378, 1998), (69, 379, 2000), (69, 380, 2001), (69, 381, 2002), (68, 382, 2004), (68, 383, 2005), (68, 384, 2006), (68, 385, 2007), (67, 386, 2009), (67, 387, 2011), (67, 388, 2012), (67, 389, 2013), (66, 390, 2015), (66, 391, 2016), (66, 392, 2017), (65, 393, 2019), (65, 394, 2020), (65, 395, 2021), (65, 396, 2022), (64, 397, 2023), (64, 398, 2024), (64, 399, 2025), (63, 400, 2027), (63, 401, 2028), (63, 402, 2029), (62, 403, 2030), (62, 404, 2031), (62, 405, 2032), (61, 406, 2034), (61, 407, 2034), (61, 408, 2035), (61, 409, 2036), (60, 410, 2037), (60, 411, 2038), (60, 412, 2039), (59, 413, 2040), (59, 414, 2041), (58, 415, 2043), (58, 416, 2043), (58, 417, 2044), (57, 418, 2046), (57, 419, 2046), (57, 420, 2047), (56, 421, 2048), (56, 422, 2049), (56, 423, 2049), (55, 424, 2051), (55, 425, 2051), (55, 426, 2052), (54, 427, 2053), (54, 428, 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663, 2147), (19, 664, 2147), (19, 665, 2148), (19, 666, 2148), (18, 667, 2149), (18, 668, 2149), (18, 669, 2149), (18, 670, 2149), (18, 671, 2149), (18, 672, 2149), (19, 673, 2148), (19, 674, 2148), (19, 675, 2148), (19, 676, 2148), (19, 677, 2148), (19, 678, 2148), (19, 679, 2148), (19, 680, 2148), (19, 681, 2147), (19, 682, 2147), (20, 683, 2146), (20, 684, 2146), (20, 685, 2146), (20, 686, 2146), (20, 687, 2146), (20, 688, 2146), (20, 689, 2146), (20, 690, 2146), (20, 691, 2146), (20, 692, 2146), (21, 693, 2144), (21, 694, 2144), (21, 695, 2144), (21, 696, 2144), (21, 697, 2144), (21, 698, 2144), (21, 699, 2144), (21, 700, 2144), (21, 701, 2144), (21, 702, 2144), (22, 703, 2143), (22, 704, 2143), (22, 705, 2142), (22, 706, 2142), (22, 707, 2142), (22, 708, 2142), (22, 709, 2142), (22, 710, 2142), (22, 711, 2142), (23, 712, 2141), (23, 713, 2141), (23, 714, 2141), (23, 715, 2141), (23, 716, 2140), (23, 717, 2140), (23, 718, 2140), (23, 719, 2140), (23, 720, 2140), (24, 721, 2139), 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['937,2141,855,2123,777,2093,647,2057,582,2029,367,1982,211,1966,122,1970,95,1928,51,1806,40,1658,40,1439,29,1277,30,910,26,739,19,682,29,525,47,444,99,291,223,202,430,131,525,113,990,113,1426,112,1589,137,1725,168,1832,180,1910,204,2018,292,2059,369,2114,443,2166,665,2152,822,2127,898,2119,974,2093,1050,2039,1123,2011,1192,1974,1242,1939,1350,1886,1426,1847,1658,1772,1887,1727,1962,1668,2011,1585,2014,1506,2049,1429,2054,1259,2080,1099,2136'])], 'temp/1759483272_3034557_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3678711 proportion of common points : 0.996320987353394 #&_# TEST SUCCEEDED #&_# : tests/mask_test #&_# #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : sam list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.3491196632385254 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:sam Fri Oct 3 11:21:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0019447803497314453 nb_pixel_total : 5614 time to create 1 rle with old method : 0.012547016143798828 time for calcul the mask position with numpy : 0.00139617919921875 nb_pixel_total : 13913 time to create 1 rle with old method : 0.030658960342407227 time for calcul the mask position with numpy : 0.0013897418975830078 nb_pixel_total : 4276 time to create 1 rle with old method : 0.009291887283325195 time for calcul the mask position with numpy : 0.0014083385467529297 nb_pixel_total : 16397 time to create 1 rle with old method : 0.036493778228759766 time for calcul the mask position with numpy : 0.0014989376068115234 nb_pixel_total : 10851 time to create 1 rle with old method : 0.023818492889404297 time for calcul the mask position with numpy : 0.001789093017578125 nb_pixel_total : 83925 time to create 1 rle with old method : 0.18766164779663086 time for calcul the mask position with numpy : 0.0014576911926269531 nb_pixel_total : 2940 time to create 1 rle with old method : 0.006816864013671875 time for calcul the mask position with numpy : 0.0014677047729492188 nb_pixel_total : 5353 time to create 1 rle with old method : 0.012411355972290039 time for calcul the mask position with numpy : 0.0015802383422851562 nb_pixel_total : 29359 time to create 1 rle with old method : 0.06921887397766113 time for calcul the mask position with numpy : 0.001562356948852539 nb_pixel_total : 14316 time to create 1 rle with old method : 0.0327906608581543 time for calcul the mask position with numpy : 0.0014481544494628906 nb_pixel_total : 3774 time to create 1 rle with old method : 0.008663654327392578 time for calcul the mask position with numpy : 0.0014941692352294922 nb_pixel_total : 9867 time to create 1 rle with old method : 0.02294015884399414 time for calcul the mask position with numpy : 0.0015094280242919922 nb_pixel_total : 13108 time to create 1 rle with old method : 0.029808998107910156 time for calcul the mask position with numpy : 0.0014739036560058594 nb_pixel_total : 6630 time to create 1 rle with old method : 0.015453338623046875 time for calcul the mask position with numpy : 0.0014486312866210938 nb_pixel_total : 4276 time to create 1 rle with old method : 0.010185718536376953 time for calcul the mask position with numpy : 0.001337289810180664 nb_pixel_total : 2731 time to create 1 rle with old method : 0.0061702728271484375 time for calcul the mask position with numpy : 0.001363515853881836 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0028471946716308594 time for calcul the mask position with numpy : 0.001338958740234375 nb_pixel_total : 2455 time to create 1 rle with old method : 0.005584239959716797 time for calcul the mask position with numpy : 0.0014460086822509766 nb_pixel_total : 3952 time to create 1 rle with old method : 0.008742094039916992 time for calcul the mask position with numpy : 0.0013477802276611328 nb_pixel_total : 2079 time to create 1 rle with old method : 0.0044209957122802734 time for calcul the mask position with numpy : 0.0014081001281738281 nb_pixel_total : 16339 time to create 1 rle with old method : 0.03485870361328125 time for calcul the mask position with numpy : 0.0013866424560546875 nb_pixel_total : 3898 time to create 1 rle with old method : 0.008732795715332031 time for calcul the mask position with numpy : 0.0013935565948486328 nb_pixel_total : 7635 time to create 1 rle with old method : 0.016963720321655273 time for calcul the mask position with numpy : 0.001462697982788086 nb_pixel_total : 1506 time to create 1 rle with old method : 0.003496408462524414 time for calcul the mask position with numpy : 0.0013527870178222656 nb_pixel_total : 3282 time to create 1 rle with old method : 0.0074863433837890625 time for calcul the mask position with numpy : 0.0015189647674560547 nb_pixel_total : 5488 time to create 1 rle with old method : 0.012431621551513672 time for calcul the mask position with numpy : 0.0013720989227294922 nb_pixel_total : 8600 time to create 1 rle with old method : 0.019207239151000977 time for calcul the mask position with numpy : 0.0014023780822753906 nb_pixel_total : 573 time to create 1 rle with old method : 0.001422882080078125 time for calcul the mask position with numpy : 0.0014176368713378906 nb_pixel_total : 931 time to create 1 rle with old method : 0.00229644775390625 time for calcul the mask position with numpy : 0.0014095306396484375 nb_pixel_total : 10560 time to create 1 rle with old method : 0.02383875846862793 time for calcul the mask position with numpy : 0.0013928413391113281 nb_pixel_total : 3528 time to create 1 rle with old method : 0.007949352264404297 time for calcul the mask position with numpy : 0.0014505386352539062 nb_pixel_total : 2448 time to create 1 rle with old method : 0.005483865737915039 time for calcul the mask position with numpy : 0.00139617919921875 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0026166439056396484 time for calcul the mask position with numpy : 0.0014505386352539062 nb_pixel_total : 2781 time to create 1 rle with old method : 0.006438255310058594 time for calcul the mask position with numpy : 0.001497030258178711 nb_pixel_total : 11896 time to create 1 rle with old method : 0.026708602905273438 time for calcul the mask position with numpy : 0.0015156269073486328 nb_pixel_total : 13011 time to create 1 rle with old method : 0.029881000518798828 time for calcul the mask position with numpy : 0.0014882087707519531 nb_pixel_total : 4130 time to create 1 rle with old method : 0.009812355041503906 time for calcul the mask position with numpy : 0.0016465187072753906 nb_pixel_total : 38744 time to create 1 rle with old method : 0.08968925476074219 time for calcul the mask position with numpy : 0.0014271736145019531 nb_pixel_total : 343 time to create 1 rle with old method : 0.0008513927459716797 time for calcul the mask position with numpy : 0.0014345645904541016 nb_pixel_total : 2378 time to create 1 rle with old method : 0.005614519119262695 time for calcul the mask position with numpy : 0.0013606548309326172 nb_pixel_total : 1012 time to create 1 rle with old method : 0.0024137496948242188 time for calcul the mask position with numpy : 0.001447439193725586 nb_pixel_total : 1648 time to create 1 rle with old method : 0.003874540328979492 time for calcul the mask position with numpy : 0.0013651847839355469 nb_pixel_total : 1254 time to create 1 rle with old method : 0.002881288528442383 time for calcul the mask position with numpy : 0.0014157295227050781 nb_pixel_total : 1614 time to create 1 rle with old method : 0.0038504600524902344 time for calcul the mask position with numpy : 0.0013263225555419922 nb_pixel_total : 839 time to create 1 rle with old method : 0.002031087875366211 time for calcul the mask position with numpy : 0.0013720989227294922 nb_pixel_total : 4172 time to create 1 rle with old method : 0.009915590286254883 time for calcul the mask position with numpy : 0.0014011859893798828 nb_pixel_total : 860 time to create 1 rle with old method : 0.002178192138671875 time for calcul the mask position with numpy : 0.0014328956604003906 nb_pixel_total : 1197 time to create 1 rle with old method : 0.0027997493743896484 time for calcul the mask position with numpy : 0.0014307498931884766 nb_pixel_total : 875 time to create 1 rle with old method : 0.002218961715698242 time for calcul the mask position with numpy : 0.0013556480407714844 nb_pixel_total : 2039 time to create 1 rle with old method : 0.004622459411621094 time for calcul the mask position with numpy : 0.001325845718383789 nb_pixel_total : 597 time to create 1 rle with old method : 0.0014519691467285156 time for calcul the mask position with numpy : 0.0014338493347167969 nb_pixel_total : 1545 time to create 1 rle with old method : 0.003672361373901367 time for calcul the mask position with numpy : 0.0014538764953613281 nb_pixel_total : 2323 time to create 1 rle with old method : 0.005292177200317383 time for calcul the mask position with numpy : 0.0013213157653808594 nb_pixel_total : 885 time to create 1 rle with old method : 0.0021207332611083984 time for calcul the mask position with numpy : 0.0014338493347167969 nb_pixel_total : 2408 time to create 1 rle with old method : 0.005767345428466797 time for calcul the mask position with numpy : 0.0014693737030029297 nb_pixel_total : 8442 time to create 1 rle with old method : 0.01901984214782715 time for calcul the mask position with numpy : 0.0014095306396484375 nb_pixel_total : 1799 time to create 1 rle with old method : 0.0041391849517822266 time for calcul the mask position with numpy : 0.001430511474609375 nb_pixel_total : 339 time to create 1 rle with old method : 0.0008683204650878906 time for calcul the mask position with numpy : 0.0013995170593261719 nb_pixel_total : 1706 time to create 1 rle with old method : 0.0039997100830078125 time for calcul the mask position with numpy : 0.0014100074768066406 nb_pixel_total : 488 time to create 1 rle with old method : 0.0012743473052978516 time for calcul the mask position with numpy : 0.0013387203216552734 nb_pixel_total : 586 time to create 1 rle with old method : 0.0014543533325195312 time for calcul the mask position with numpy : 0.0014333724975585938 nb_pixel_total : 2769 time to create 1 rle with old method : 0.00606846809387207 time for calcul the mask position with numpy : 0.0014333724975585938 nb_pixel_total : 693 time to create 1 rle with old method : 0.0017039775848388672 time for calcul the mask position with numpy : 0.0014405250549316406 nb_pixel_total : 1206 time to create 1 rle with old method : 0.002803802490234375 time for calcul the mask position with numpy : 0.0015101432800292969 nb_pixel_total : 27635 time to create 1 rle with old method : 0.06115460395812988 time for calcul the mask position with numpy : 0.0014269351959228516 nb_pixel_total : 1074 time to create 1 rle with old method : 0.002589702606201172 time for calcul the mask position with numpy : 0.001420736312866211 nb_pixel_total : 247 time to create 1 rle with old method : 0.0006511211395263672 time for calcul the mask position with numpy : 0.0014295578002929688 nb_pixel_total : 8594 time to create 1 rle with old method : 0.019072771072387695 time for calcul the mask position with numpy : 0.0013594627380371094 nb_pixel_total : 1429 time to create 1 rle with old method : 0.003357410430908203 time for calcul the mask position with numpy : 0.0013735294342041016 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0032508373260498047 time for calcul the mask position with numpy : 0.0014646053314208984 nb_pixel_total : 16680 time to create 1 rle with old method : 0.03965282440185547 time for calcul the mask position with numpy : 0.0014641284942626953 nb_pixel_total : 3104 time to create 1 rle with old method : 0.007215976715087891 time for calcul the mask position with numpy : 0.0015246868133544922 nb_pixel_total : 18422 time to create 1 rle with old method : 0.0399322509765625 time for calcul the mask position with numpy : 0.0015065670013427734 nb_pixel_total : 9658 time to create 1 rle with old method : 0.0221102237701416 time for calcul the mask position with numpy : 0.0015125274658203125 nb_pixel_total : 9077 time to create 1 rle with old method : 0.02059650421142578 time for calcul the mask position with numpy : 0.0014467239379882812 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006487369537353516 time for calcul the mask position with numpy : 0.0014255046844482422 nb_pixel_total : 1004 time to create 1 rle with old method : 0.0023636817932128906 time for calcul the mask position with numpy : 0.0014264583587646484 nb_pixel_total : 717 time to create 1 rle with old method : 0.001811981201171875 time for calcul the mask position with numpy : 0.0014367103576660156 nb_pixel_total : 620 time to create 1 rle with old method : 0.0015244483947753906 time for calcul the mask position with numpy : 0.0016279220581054688 nb_pixel_total : 1127 time to create 1 rle with old method : 0.0032176971435546875 time for calcul the mask position with numpy : 0.0018801689147949219 nb_pixel_total : 3171 time to create 1 rle with old method : 0.009947776794433594 time for calcul the mask position with numpy : 0.001474142074584961 nb_pixel_total : 968 time to create 1 rle with old method : 0.0023834705352783203 time for calcul the mask position with numpy : 0.0014717578887939453 nb_pixel_total : 222 time to create 1 rle with old method : 0.0005972385406494141 time for calcul the mask position with numpy : 0.0014379024505615234 nb_pixel_total : 1502 time to create 1 rle with old method : 0.003554821014404297 time for calcul the mask position with numpy : 0.0013947486877441406 nb_pixel_total : 735 time to create 1 rle with old method : 0.0017473697662353516 time for calcul the mask position with numpy : 0.0014274120330810547 nb_pixel_total : 595 time to create 1 rle with old method : 0.0015044212341308594 time for calcul the mask position with numpy : 0.001474618911743164 nb_pixel_total : 7525 time to create 1 rle with old method : 0.019662857055664062 time for calcul the mask position with numpy : 0.001737833023071289 nb_pixel_total : 298 time to create 1 rle with old method : 0.0008029937744140625 time for calcul the mask position with numpy : 0.0014307498931884766 nb_pixel_total : 5011 time to create 1 rle with old method : 0.011716842651367188 time for calcul the mask position with numpy : 0.0014319419860839844 nb_pixel_total : 890 time to create 1 rle with old method : 0.0021343231201171875 time for calcul the mask position with numpy : 0.0014235973358154297 nb_pixel_total : 2194 time to create 1 rle with old method : 0.00527644157409668 time for calcul the mask position with numpy : 0.001424551010131836 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0031235218048095703 time for calcul the mask position with numpy : 0.0014297962188720703 nb_pixel_total : 930 time to create 1 rle with old method : 0.0022623538970947266 time for calcul the mask position with numpy : 0.00140380859375 nb_pixel_total : 881 time to create 1 rle with old method : 0.002007722854614258 time for calcul the mask position with numpy : 0.0014083385467529297 nb_pixel_total : 1426 time to create 1 rle with old method : 0.003530263900756836 batch 1 Loaded 95 chid ids of type : 4677 Number RLEs to save : 8678 TO DO : save crop sub photo not yet done ! Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] Looping around the photos to save general results len do output : 1 /1189321094Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4573', None, None, None, None, None, None, None, None) ('4573', None, '1189321094', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.03831958770751953 save_final save missing photos in datou_result : time spend for datou_step_exec : 14.254041910171509 time spend to save output : 0.03878426551818848 total time spend for step 1 : 14.292826175689697 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1759483305_3034557_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 95 ############################### TEST frcnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : frcnn list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.18710660934448242 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:frcnn Fri Oct 3 11:22:00 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1759483319_3034557_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 len de result frcnn : 1 time spend for datou_step_exec : 2.707228422164917 time spend to save output : 8.082389831542969e-05 total time spend for step 1 : 2.7073092460632324 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False Inside saveFrcnn : final : True verbose : False threshold to save the result : 0.1 Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.03757667541503906 [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.035471200942993164 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, 0.06384172, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052212164, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271662, None)], 'temp/1759483319_3034557_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.12315607070922852 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Fri Oct 3 11:22:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 355 time to import caffe and check if the image exist : 0.012268543243408203 time to convert the images to numpy array : 0.0012514591217041016 total time to convert the images to numpy array : 0.013970375061035156 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 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'506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 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 : 969 wait 20 seconds l 3637 free memory gpu now : 969 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : car_360_1027 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 748 wait 20 seconds WARNING: Logging before InitGoogleLogging() is written to STDERR F1003 11:22:48.470537 3034557 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) No data to report. No data to report. ret : 34304 command : coverage3 html -i --omit=/usr/local/lib/python3.8/dist-packages/*,/home/admin/.local/lib/python3.8/site-packages/*,/usr/lib/python3/dist-packages/* -d htmlcov ret : 256 command : coverage3 report -i -m ret : 256 47.95user 28.94system 2:24.86elapsed 53%CPU (0avgtext+0avgdata 3953988maxresident)k 4275168inputs+4712outputs (7472major+3309435minor)pagefaults 0swaps