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/November/09112025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_4/data_log/job/2025/November/09112025/coverage/htmlcov new path : /data_4/data_log/job/2025/November/09112025/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 : 10998 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 : 0.1582043170928955 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 Sun Nov 9 05:20:29 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 : 10998 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 2025-11-09 05:20:33.266952: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-11-09 05:20:33.294472: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-11-09 05:20:33.296065: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5a28000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-11-09 05:20:33.296091: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-11-09 05:20:33.299311: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-11-09 05:20:33.435483: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d47de90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-11-09 05:20:33.435529: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-11-09 05:20:33.436983: 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 2025-11-09 05:20:33.437384: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 05:20:33.440358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 05:20:33.443163: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 05:20:33.443536: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 05:20:33.446124: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 05:20:33.447420: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 05:20:33.452577: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 05:20:33.454420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 05:20:33.454509: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 05:20:33.455391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-09 05:20:33.455407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-09 05:20:33.455417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-09 05:20:33.456742: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10049 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 2025-11-09 05:20:34.240698: 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 2025-11-09 05:20:34.240782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 05:20:34.240804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 05:20:34.240823: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 05:20:34.240841: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 05:20:34.240859: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 05:20:34.240877: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 05:20:34.240895: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 05:20:34.242509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 05:20:34.243706: 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 2025-11-09 05:20:34.243738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 05:20:34.243756: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 05:20:34.243772: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 05:20:34.243789: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 05:20:34.243805: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 05:20:34.243821: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 05:20:34.243838: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 05:20:34.245076: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 05:20:34.245101: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-09 05:20:34.245109: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-09 05:20:34.245116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-09 05:20:34.246431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10049 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 : [] 2025-11-09 05:20:43.859432: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 05:20:44.075092: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 05:20:46.135248: E tensorflow/stream_executor/cuda/cuda_driver.cc:910] failed to synchronize the stop event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-11-09 05:20:46.135324: E tensorflow/stream_executor/gpu/gpu_timer.cc:55] Internal: Error destroying CUDA event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-11-09 05:20:46.135348: E tensorflow/stream_executor/gpu/gpu_timer.cc:60] Internal: Error destroying CUDA event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-11-09 05:20:46.135366: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 8B (8 bytes) from device: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-11-09 05:20:46.135377: E tensorflow/stream_executor/stream.cc:5485] Internal: Failed to enqueue async memset operation: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-11-09 05:20:46.135389: W tensorflow/core/kernels/gpu_utils.cc:69] Failed to check cudnn convolutions for out-of-bounds reads and writes with an error message: 'Failed to load in-memory CUBIN: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered'; skipping this check. This only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-11-09 05:20:46.135398: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 8B (8 bytes) from device: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2025-11-09 05:20:46.135409: I tensorflow/stream_executor/stream.cc:4963] [stream=0x1e8ea690,impl=0x1e8e9680] did not memzero GPU location; source: 0x7f58bd7f8020 2025-11-09 05:20:46.135853: F ./tensorflow/core/kernels/reduction_gpu_kernels.cu.h:731] Non-OK-status: GpuLaunchKernel(RowReduceKernel, num_blocks, threads_per_block, 0, cu_stream, in, out, num_rows, num_cols, op, init) status: Internal: an illegal memory access was encountered max_time_sub_proc : 3600 Useless call to update_current_state in case -12 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! ERROR : mask output needs to be a dictionnary now ! No output to save, continue without doing anything ! save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : -12 free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4028 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False Catched exception ! Connect or reconnect ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better 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 : 0.39107823371887207 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 Sun Nov 9 06:20:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 4028 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-11-09 06:20:37.256236: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-11-09 06:20:37.286630: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-11-09 06:20:37.288425: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5a2c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-11-09 06:20:37.288471: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-11-09 06:20:37.292200: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-11-09 06:20:37.572683: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1de83950 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-11-09 06:20:37.572724: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-11-09 06:20:37.573720: 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 2025-11-09 06:20:37.574088: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 06:20:37.576972: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 06:20:37.579512: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 06:20:37.579987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 06:20:37.583125: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 06:20:37.584283: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 06:20:37.588144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 06:20:37.589107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 06:20:37.589160: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 06:20:37.589680: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-09 06:20:37.589694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-09 06:20:37.589702: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-09 06:20:37.590628: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3576 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 2025-11-09 06:20:38.325539: 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 2025-11-09 06:20:38.325620: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 06:20:38.325636: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 06:20:38.325650: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 06:20:38.325664: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 06:20:38.325677: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 06:20:38.325691: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 06:20:38.325704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 06:20:38.326562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 06:20:38.327476: 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 2025-11-09 06:20:38.327506: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 06:20:38.327527: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 06:20:38.327542: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 06:20:38.327556: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 06:20:38.327570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 06:20:38.327584: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 06:20:38.327598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 06:20:38.328442: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 06:20:38.328467: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-09 06:20:38.328475: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-09 06:20:38.328481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-09 06:20:38.329333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3576 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 : [] 2025-11-09 06:20:46.747398: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 06:20:46.905582: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 06:20:48.121807: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.122456: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.69G (2893322496 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.123063: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.42G (2603990272 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.123627: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.18G (2343591168 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.123648: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.124254: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.124269: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.131411: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.131433: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.132066: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.132081: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.138684: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.138709: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.139348: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.139364: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.170151: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.170178: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.170837: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.170855: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.176746: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.176768: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.177402: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.177418: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-09 06:20:48.211208: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.211814: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.213734: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.214391: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.258270: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.258946: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.261189: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.261784: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.269637: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.270239: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.275182: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.275827: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.287608: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.288251: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.289873: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.290540: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.296083: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.296725: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.298458: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.299100: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.304836: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.305480: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.307122: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.307765: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.334287: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.334963: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.335571: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.336179: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.339759: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.340383: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.355856: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.356516: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.357154: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.357790: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.370128: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.370823: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.371451: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.372043: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.376474: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.377119: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.381845: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.382536: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.394942: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.395596: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.399814: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.400456: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.401092: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.401729: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.422918: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.423569: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.424222: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.424859: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.425495: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.426130: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.426814: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.427454: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.442016: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.442669: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.460524: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.460566: 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. 2025-11-09 06:20:48.461218: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.461835: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.469320: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.469931: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.470609: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.471252: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.479832: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.480864: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.531825: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.532700: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.533576: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.534344: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.538746: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.539520: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.540392: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.541191: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.542636: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.552891: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.553685: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.564649: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.565604: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.566426: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.567316: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.568075: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:20:48.568783: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 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 3624458 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 2036 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 : 3229 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl454 Catched exception ! Connect or reconnect ! thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0004563331604003906 nb_pixel_total : 16902 time to create 1 rle with old method : 0.039980173110961914 length of segment : 107 time for calcul the mask position with numpy : 0.018488407135009766 nb_pixel_total : 480739 time to create 1 rle with new method : 0.03348278999328613 length of segment : 632 time for calcul the mask position with numpy : 0.0006229877471923828 nb_pixel_total : 36642 time to create 1 rle with old method : 0.08744001388549805 length of segment : 133 time for calcul the mask position with numpy : 0.00014472007751464844 nb_pixel_total : 4792 time to create 1 rle with old method : 0.011097192764282227 length of segment : 51 time spent for convertir_results : 1.2141904830932617 time spend for datou_step_exec : 20.30755352973938 time spend to save output : 5.340576171875e-05 total time spend for step 1 : 20.3076069355011 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 447 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 : 0.01731133460998535 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917855882': [[(917855882, 492601069, 445, 1092, 1280, 0, 108, 0.9988368, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.99774784, [(711, 22, 21), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (489, 47, 589), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 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859), (226, 150, 863), (220, 151, 869), (213, 152, 876), (207, 153, 882), (200, 154, 889), (193, 155, 896), (187, 156, 902), (184, 157, 905), (181, 158, 908), (178, 159, 911), (176, 160, 913), (174, 161, 915), (172, 162, 917), (170, 163, 919), (168, 164, 921), (167, 165, 922), (165, 166, 924), (164, 167, 925), (162, 168, 927), (161, 169, 928), (159, 170, 930), (157, 171, 932), (155, 172, 934), (153, 173, 935), (151, 174, 937), (149, 175, 939), (146, 176, 942), (144, 177, 944), (142, 178, 946), (140, 179, 948), (139, 180, 949), (137, 181, 951), (136, 182, 952), (134, 183, 954), (133, 184, 955), (132, 185, 956), (131, 186, 957), (130, 187, 958), (129, 188, 959), (128, 189, 960), (127, 190, 960), (126, 191, 961), (126, 192, 961), (125, 193, 962), (124, 194, 963), (123, 195, 964), (122, 196, 965), (122, 197, 965), (121, 198, 966), (120, 199, 967), (119, 200, 968), (118, 201, 969), (117, 202, 970), (116, 203, 971), (114, 204, 973), (113, 205, 973), (112, 206, 974), (111, 207, 975), (109, 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(378, 636, 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, 82), (565, 650, 9)], ['526,649,416,642,368,634,341,627,307,613,243,590,220,577,186,566,144,539,102,509,91,496,70,447,63,388,65,329,86,265,91,237,101,216,134,183,187,156,225,151,252,141,343,123,358,116,416,103,493,45,527,36,608,23,754,24,893,24,925,22,996,23,1032,27,1066,41,1082,52,1089,72,1088,172,1082,237,1064,267,1045,305,950,373,895,423,865,446,851,473,830,493,810,528,786,554,773,585,714,624,683,638,607,649']), (917855882, 492601069, 445, 0, 440, 0, 116, 0.9919451, [(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, 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(279, 85, 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, 0.93914914, [(414, 0, 6), (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)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,419,0,420,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1762665632_3527149_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 : 0.2179584503173828 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 Sun Nov 9 06:20:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3000 l 3632 free memory gpu now : 567 wait 20 seconds l 3637 free memory gpu now : 567 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-11-09 06:21:17.258143: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-11-09 06:21:17.286651: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-11-09 06:21:17.288870: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5a2c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-11-09 06:21:17.288944: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-11-09 06:21:17.293004: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-11-09 06:21:17.527849: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e95f1c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-11-09 06:21:17.527901: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-11-09 06:21:17.528950: 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 2025-11-09 06:21:17.529415: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 06:21:17.532689: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 06:21:17.538636: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 06:21:17.539062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 06:21:17.542256: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 06:21:17.543669: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 06:21:17.548832: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 06:21:17.549916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 06:21:17.550008: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 06:21:17.550571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-09 06:21:17.550587: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-09 06:21:17.550612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-09 06:21:17.551516: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3576 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. 2025-11-09 06:21:17.660692: 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 2025-11-09 06:21:17.660839: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 06:21:17.660868: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 06:21:17.660893: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 06:21:17.660917: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 06:21:17.660941: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 06:21:17.660964: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 06:21:17.660988: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 06:21:17.662040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 06:21:17.663224: 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 2025-11-09 06:21:17.663262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-09 06:21:17.663278: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 06:21:17.663293: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-09 06:21:17.663308: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-09 06:21:17.663322: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-09 06:21:17.663339: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-09 06:21:17.663355: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 06:21:17.664214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-09 06:21:17.664246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-09 06:21:17.664254: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-09 06:21:17.664261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-09 06:21:17.665105: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3576 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 : [] 2025-11-09 06:21:27.434383: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-09 06:21:27.631541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-09 06:21:28.980261: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3214802944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-09 06:21:29.767131: 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. 2025-11-09 06:21:29.767202: 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 3625792 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 75 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 : 4028 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 : 0.369157075881958 nb_pixel_total : 3693221 time to create 1 rle with new method : 0.4115481376647949 length of segment : 2041 time spent for convertir_results : 2.127328395843506 time spend for datou_step_exec : 42.1473491191864 time spend to save output : 4.9114227294921875e-05 total time spend for step 1 : 42.147398233413696 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 725 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 : 0.0164947509765625 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 7, 2268, 118, 2241, 0.98500174, [(675, 120, 112), (520, 121, 481), (1051, 121, 380), (502, 122, 948), (486, 123, 981), (470, 124, 1015), (455, 125, 1046), (442, 126, 1092), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1223), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (363, 135, 1267), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (323, 159, 1389), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (280, 182, 1508), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1681), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 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['1001,2150,936,2144,771,2092,694,2075,610,2037,364,1986,214,1963,127,1970,54,1825,39,1677,41,1538,29,1240,27,757,21,696,27,543,39,458,93,308,126,270,210,206,291,179,363,135,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2011,293,2098,420,2148,535,2168,614,2171,716,2164,839,2128,914,2112,994,2081,1068,2031,1132,1997,1214,1967,1255,1931,1368,1879,1444,1845,1674,1756,1920,1662,2015,1581,2015,1496,2039,1420,2046,1347,2068,1177,2101,1097,2141'])], 'temp/1762665653_3527149_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3689908 proportion of common points : 0.9993535186110536 #&_# TEST FAILED #&_# : 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 : 0.20646905899047852 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 Sun Nov 9 06:21:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0022325515747070312 nb_pixel_total : 6642 time to create 1 rle with old method : 0.01528310775756836 time for calcul the mask position with numpy : 0.0013794898986816406 nb_pixel_total : 6021 time to create 1 rle with old method : 0.013563394546508789 time for calcul the mask position with numpy : 0.0013892650604248047 nb_pixel_total : 3777 time to create 1 rle with old method : 0.00834345817565918 time for calcul the mask position with numpy : 0.0015099048614501953 nb_pixel_total : 5619 time to create 1 rle with old method : 0.012762308120727539 time for calcul the mask position with numpy : 0.0014867782592773438 nb_pixel_total : 16458 time to create 1 rle with old method : 0.03560924530029297 time for calcul the mask position with numpy : 0.0017595291137695312 nb_pixel_total : 84104 time to create 1 rle with old method : 0.17809176445007324 time for calcul the mask position with numpy : 0.001416921615600586 nb_pixel_total : 2949 time to create 1 rle with old method : 0.006467342376708984 time for calcul the mask position with numpy : 0.0014553070068359375 nb_pixel_total : 7675 time to create 1 rle with old method : 0.017043590545654297 time for calcul the mask position with numpy : 0.0015399456024169922 nb_pixel_total : 13916 time to create 1 rle with old method : 0.03072071075439453 time for calcul the mask position with numpy : 0.0015149116516113281 nb_pixel_total : 29433 time to create 1 rle with old method : 0.0627906322479248 time for calcul the mask position with numpy : 0.0013492107391357422 nb_pixel_total : 4244 time to create 1 rle with old method : 0.009331941604614258 time for calcul the mask position with numpy : 0.0013124942779541016 nb_pixel_total : 2736 time to create 1 rle with old method : 0.006003856658935547 time for calcul the mask position with numpy : 0.0013196468353271484 nb_pixel_total : 4275 time to create 1 rle with old method : 0.009013175964355469 time for calcul the mask position with numpy : 0.0013415813446044922 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0026679039001464844 time for calcul the mask position with numpy : 0.0013201236724853516 nb_pixel_total : 2453 time to create 1 rle with old method : 0.005283832550048828 time for calcul the mask position with numpy : 0.0013532638549804688 nb_pixel_total : 10819 time to create 1 rle with old method : 0.022431373596191406 time for calcul the mask position with numpy : 0.001318216323852539 nb_pixel_total : 3926 time to create 1 rle with old method : 0.008608341217041016 time for calcul the mask position with numpy : 0.0015082359313964844 nb_pixel_total : 16328 time to create 1 rle with old method : 0.04138970375061035 time for calcul the mask position with numpy : 0.0018606185913085938 nb_pixel_total : 2403 time to create 1 rle with old method : 0.007432699203491211 time for calcul the mask position with numpy : 0.0017230510711669922 nb_pixel_total : 2079 time to create 1 rle with old method : 0.005968332290649414 time for calcul the mask position with numpy : 0.001428842544555664 nb_pixel_total : 5486 time to create 1 rle with old method : 0.012410163879394531 time for calcul the mask position with numpy : 0.0014696121215820312 nb_pixel_total : 2502 time to create 1 rle with old method : 0.005957841873168945 time for calcul the mask position with numpy : 0.0015037059783935547 nb_pixel_total : 3097 time to create 1 rle with old method : 0.00735926628112793 time for calcul the mask position with numpy : 0.0018224716186523438 nb_pixel_total : 38460 time to create 1 rle with old method : 0.10019659996032715 time for calcul the mask position with numpy : 0.0015044212341308594 nb_pixel_total : 5327 time to create 1 rle with old method : 0.012067556381225586 time for calcul the mask position with numpy : 0.0014948844909667969 nb_pixel_total : 8606 time to create 1 rle with old method : 0.020088672637939453 time for calcul the mask position with numpy : 0.001718759536743164 nb_pixel_total : 11922 time to create 1 rle with old method : 0.02786707878112793 time for calcul the mask position with numpy : 0.0015263557434082031 nb_pixel_total : 3529 time to create 1 rle with old method : 0.011613130569458008 time for calcul the mask position with numpy : 0.0015053749084472656 nb_pixel_total : 2383 time to create 1 rle with old method : 0.005616903305053711 time for calcul the mask position with numpy : 0.0014615058898925781 nb_pixel_total : 2495 time to create 1 rle with old method : 0.0054781436920166016 time for calcul the mask position with numpy : 0.0013995170593261719 nb_pixel_total : 13014 time to create 1 rle with old method : 0.028582096099853516 time for calcul the mask position with numpy : 0.0013737678527832031 nb_pixel_total : 221 time to create 1 rle with old method : 0.0005445480346679688 time for calcul the mask position with numpy : 0.0013549327850341797 nb_pixel_total : 9849 time to create 1 rle with old method : 0.021675825119018555 time for calcul the mask position with numpy : 0.0013184547424316406 nb_pixel_total : 2781 time to create 1 rle with old method : 0.006045103073120117 time for calcul the mask position with numpy : 0.0013191699981689453 nb_pixel_total : 695 time to create 1 rle with old method : 0.0015943050384521484 time for calcul the mask position with numpy : 0.0014066696166992188 nb_pixel_total : 254 time to create 1 rle with old method : 0.0006012916564941406 time for calcul the mask position with numpy : 0.0013399124145507812 nb_pixel_total : 10629 time to create 1 rle with old method : 0.02282118797302246 time for calcul the mask position with numpy : 0.001310110092163086 nb_pixel_total : 1648 time to create 1 rle with old method : 0.0037109851837158203 time for calcul the mask position with numpy : 0.001325845718383789 nb_pixel_total : 3328 time to create 1 rle with old method : 0.007066249847412109 time for calcul the mask position with numpy : 0.0012865066528320312 nb_pixel_total : 1025 time to create 1 rle with old method : 0.0022118091583251953 time for calcul the mask position with numpy : 0.0013086795806884766 nb_pixel_total : 4169 time to create 1 rle with old method : 0.008931159973144531 time for calcul the mask position with numpy : 0.0013065338134765625 nb_pixel_total : 4127 time to create 1 rle with old method : 0.008996725082397461 time for calcul the mask position with numpy : 0.0013065338134765625 nb_pixel_total : 1257 time to create 1 rle with old method : 0.002694845199584961 time for calcul the mask position with numpy : 0.0013070106506347656 nb_pixel_total : 3857 time to create 1 rle with old method : 0.008599281311035156 time for calcul the mask position with numpy : 0.0012998580932617188 nb_pixel_total : 344 time to create 1 rle with old method : 0.0007808208465576172 time for calcul the mask position with numpy : 0.0012979507446289062 nb_pixel_total : 2045 time to create 1 rle with old method : 0.004398345947265625 time for calcul the mask position with numpy : 0.0014760494232177734 nb_pixel_total : 39181 time to create 1 rle with old method : 0.08730101585388184 time for calcul the mask position with numpy : 0.0024421215057373047 nb_pixel_total : 861 time to create 1 rle with old method : 0.0035965442657470703 time for calcul the mask position with numpy : 0.0025315284729003906 nb_pixel_total : 14634 time to create 1 rle with old method : 0.03308534622192383 time for calcul the mask position with numpy : 0.0012979507446289062 nb_pixel_total : 877 time to create 1 rle with old method : 0.0021011829376220703 time for calcul the mask position with numpy : 0.0012941360473632812 nb_pixel_total : 595 time to create 1 rle with old method : 0.0014736652374267578 time for calcul the mask position with numpy : 0.0013515949249267578 nb_pixel_total : 882 time to create 1 rle with old method : 0.0019445419311523438 time for calcul the mask position with numpy : 0.0014224052429199219 nb_pixel_total : 2324 time to create 1 rle with old method : 0.005170345306396484 time for calcul the mask position with numpy : 0.0013227462768554688 nb_pixel_total : 577 time to create 1 rle with old method : 0.001402139663696289 time for calcul the mask position with numpy : 0.0012981891632080078 nb_pixel_total : 337 time to create 1 rle with old method : 0.0007984638214111328 time for calcul the mask position with numpy : 0.0014431476593017578 nb_pixel_total : 27670 time to create 1 rle with old method : 0.05879926681518555 time for calcul the mask position with numpy : 0.0013179779052734375 nb_pixel_total : 1571 time to create 1 rle with old method : 0.003711700439453125 time for calcul the mask position with numpy : 0.0014042854309082031 nb_pixel_total : 2769 time to create 1 rle with old method : 0.0061380863189697266 time for calcul the mask position with numpy : 0.0014195442199707031 nb_pixel_total : 605 time to create 1 rle with old method : 0.0013835430145263672 time for calcul the mask position with numpy : 0.0014274120330810547 nb_pixel_total : 1075 time to create 1 rle with old method : 0.002541065216064453 time for calcul the mask position with numpy : 0.001435995101928711 nb_pixel_total : 1196 time to create 1 rle with old method : 0.0028514862060546875 time for calcul the mask position with numpy : 0.0015075206756591797 nb_pixel_total : 18620 time to create 1 rle with old method : 0.04073691368103027 time for calcul the mask position with numpy : 0.001474142074584961 nb_pixel_total : 16694 time to create 1 rle with old method : 0.0374758243560791 time for calcul the mask position with numpy : 0.0013515949249267578 nb_pixel_total : 8608 time to create 1 rle with old method : 0.018310070037841797 time for calcul the mask position with numpy : 0.0013861656188964844 nb_pixel_total : 13534 time to create 1 rle with old method : 0.02837395668029785 time for calcul the mask position with numpy : 0.0012938976287841797 nb_pixel_total : 1061 time to create 1 rle with old method : 0.0024390220642089844 time for calcul the mask position with numpy : 0.0013051033020019531 nb_pixel_total : 1740 time to create 1 rle with old method : 0.0037441253662109375 time for calcul the mask position with numpy : 0.0013544559478759766 nb_pixel_total : 1707 time to create 1 rle with old method : 0.0036966800689697266 time for calcul the mask position with numpy : 0.0013225078582763672 nb_pixel_total : 8443 time to create 1 rle with old method : 0.0176999568939209 time for calcul the mask position with numpy : 0.0013415813446044922 nb_pixel_total : 9081 time to create 1 rle with old method : 0.019081830978393555 time for calcul the mask position with numpy : 0.0012886524200439453 nb_pixel_total : 1528 time to create 1 rle with old method : 0.003242015838623047 time for calcul the mask position with numpy : 0.0012812614440917969 nb_pixel_total : 267 time to create 1 rle with old method : 0.0005929470062255859 time for calcul the mask position with numpy : 0.0012927055358886719 nb_pixel_total : 1334 time to create 1 rle with old method : 0.002927541732788086 time for calcul the mask position with numpy : 0.001318216323852539 nb_pixel_total : 9504 time to create 1 rle with old method : 0.0197446346282959 time for calcul the mask position with numpy : 0.0013470649719238281 nb_pixel_total : 712 time to create 1 rle with old method : 0.001631021499633789 time for calcul the mask position with numpy : 0.0012900829315185547 nb_pixel_total : 3166 time to create 1 rle with old method : 0.006739616394042969 time for calcul the mask position with numpy : 0.0013687610626220703 nb_pixel_total : 971 time to create 1 rle with old method : 0.0020945072174072266 time for calcul the mask position with numpy : 0.0013165473937988281 nb_pixel_total : 1502 time to create 1 rle with old method : 0.003256559371948242 time for calcul the mask position with numpy : 0.0013017654418945312 nb_pixel_total : 616 time to create 1 rle with old method : 0.0013856887817382812 time for calcul the mask position with numpy : 0.001318216323852539 nb_pixel_total : 973 time to create 1 rle with old method : 0.002239227294921875 time for calcul the mask position with numpy : 0.0014793872833251953 nb_pixel_total : 5010 time to create 1 rle with old method : 0.011403322219848633 time for calcul the mask position with numpy : 0.00141143798828125 nb_pixel_total : 735 time to create 1 rle with old method : 0.0019214153289794922 time for calcul the mask position with numpy : 0.0014431476593017578 nb_pixel_total : 595 time to create 1 rle with old method : 0.0013926029205322266 time for calcul the mask position with numpy : 0.0013403892517089844 nb_pixel_total : 1644 time to create 1 rle with old method : 0.003919124603271484 time for calcul the mask position with numpy : 0.0014803409576416016 nb_pixel_total : 7500 time to create 1 rle with old method : 0.016831636428833008 time for calcul the mask position with numpy : 0.0013339519500732422 nb_pixel_total : 1441 time to create 1 rle with old method : 0.003374814987182617 time for calcul the mask position with numpy : 0.0014352798461914062 nb_pixel_total : 1110 time to create 1 rle with old method : 0.0026123523712158203 time for calcul the mask position with numpy : 0.0014390945434570312 nb_pixel_total : 293 time to create 1 rle with old method : 0.0008058547973632812 time for calcul the mask position with numpy : 0.0014243125915527344 nb_pixel_total : 1121 time to create 1 rle with old method : 0.0025827884674072266 time for calcul the mask position with numpy : 0.0013537406921386719 nb_pixel_total : 914 time to create 1 rle with old method : 0.002420663833618164 time for calcul the mask position with numpy : 0.0013151168823242188 nb_pixel_total : 2198 time to create 1 rle with old method : 0.004991292953491211 time for calcul the mask position with numpy : 0.0014345645904541016 nb_pixel_total : 888 time to create 1 rle with old method : 0.0020036697387695312 time for calcul the mask position with numpy : 0.0013070106506347656 nb_pixel_total : 949 time to create 1 rle with old method : 0.0021393299102783203 time for calcul the mask position with numpy : 0.0012929439544677734 nb_pixel_total : 885 time to create 1 rle with old method : 0.0019593238830566406 time for calcul the mask position with numpy : 0.0012965202331542969 nb_pixel_total : 1321 time to create 1 rle with old method : 0.003194093704223633 time for calcul the mask position with numpy : 0.0012874603271484375 nb_pixel_total : 478 time to create 1 rle with old method : 0.0011110305786132812 time for calcul the mask position with numpy : 0.00130462646484375 nb_pixel_total : 1614 time to create 1 rle with old method : 0.003712177276611328 time for calcul the mask position with numpy : 0.001299142837524414 nb_pixel_total : 538 time to create 1 rle with old method : 0.0012960433959960938 time for calcul the mask position with numpy : 0.001302957534790039 nb_pixel_total : 1438 time to create 1 rle with old method : 0.003288745880126953 batch 1 Loaded 99 chid ids of type : 4677 Number RLEs to save : 9222 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 : 0.01612997055053711 save_final save missing photos in datou_result : time spend for datou_step_exec : 15.60373044013977 time spend to save output : 0.016440153121948242 total time spend for step 1 : 15.620170593261719 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1762665703_3527149_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 99 ############################### 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 : 0.14658045768737793 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 Sun Nov 9 06:21:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step 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 : [] WARNING: Logging before InitGoogleLogging() is written to STDERR F1109 06:22:01.205171 3527149 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 46.17user 26.21system 1:01:36elapsed 1%CPU (0avgtext+0avgdata 3562352maxresident)k 3775848inputs+4760outputs (21592major+3014387minor)pagefaults 0swaps