python /home/admin/mtr/script_for_cron.py -j python_test3 -m 12 -a ' --short_python3 -v ' -s python_test3 -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data list_job_run_as_list : ['mask_detection', 'datou', 'CacheModelData_queries', 'CachePhotoData_queries', 'test_fork', 'prepare_maskdata', 'portfolio_queries', 'sla_mensuel'] python version used : 3 liste_fichiers : [('tests/mask_test', True, 'Test mask-detection ', 'mask_detection'), ('tests/datou_test', True, 'Datou All Test', 'datou', 'all'), ('mtr/database_queries/CacheModelData_queries', True, 'Test Cache Model Data', 'CacheModelData_queries'), ('tests/cache_photo_data_test', True, 'Test local_cache_photo ', 'CachePhotoData_queries'), ('mtr/mask_rcnn/prepare_maskdata', True, 'test prepare mask data', 'prepare_maskdata', 'all'), ('mtr/database_queries/portfolio_queries', True, 'test portfolio queries', 'portfolio_queries'), ('prod/memo/memo', True, 'SLA Mensuel', 'sla_mensuel', 'all')] #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 3137 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 : 0.12749934196472168 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 Wed Feb 12 18:35:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3137 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 2025-02-12 18:35:31.281610: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-12 18:35:31.307140: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-12 18:35:31.309441: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f509c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-12 18:35:31.309519: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-12 18:35:31.313571: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-12 18:35:31.577112: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x28c6b930 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-12 18:35:31.577173: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-12 18:35:31.578251: 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 2025-02-12 18:35:31.578708: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:35:31.587547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:35:31.590595: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:35:31.591121: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:35:31.596801: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:35:31.597887: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:35:31.603018: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:35:31.604098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:35:31.604174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:35:31.604835: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 18:35:31.604854: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 18:35:31.604865: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 18:35:31.605963: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2685 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 2025-02-12 18:35:32.179160: 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 2025-02-12 18:35:32.179270: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:35:32.179306: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:35:32.179337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:35:32.179368: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:35:32.179396: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:35:32.179440: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:35:32.179472: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:35:32.180642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:35:32.184594: 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 2025-02-12 18:35:32.184771: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:35:32.184813: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:35:32.184851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:35:32.184887: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:35:32.184922: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:35:32.184956: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:35:32.184991: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:35:32.186650: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:35:32.186703: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 18:35:32.186722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 18:35:32.186739: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 18:35:32.188424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2685 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 : [] 2025-02-12 18:35:40.044245: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:35:40.244572: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:35:41.541041: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.541142: 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. 2025-02-12 18:35:41.541686: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.541703: 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. 2025-02-12 18:35:41.548752: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.548774: 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. 2025-02-12 18:35:41.549310: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.549325: 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. 2025-02-12 18:35:41.555913: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.555936: 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. 2025-02-12 18:35:41.556491: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.556506: 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. 2025-02-12 18:35:41.587447: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.587474: 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. 2025-02-12 18:35:41.588049: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.588064: 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. 2025-02-12 18:35:41.593901: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.593921: 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. 2025-02-12 18:35:41.594490: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.594506: 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. 2025-02-12 18:35:41.628475: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.629068: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.630975: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.631566: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.671506: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.672098: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.678581: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.679170: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.683544: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.684144: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.694794: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.695456: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.697001: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.697586: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.703127: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.703715: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.705368: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.705919: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.711520: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.712100: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.713649: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.714228: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.740563: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.741116: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.741680: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.742260: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.745765: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.746309: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.761661: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.762211: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.762750: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.763319: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.775547: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.776334: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.776910: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.777485: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.781754: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.782336: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.786878: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.787467: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.799306: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.799891: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.803956: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.804535: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.805102: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.805676: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.826526: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.827095: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.827690: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.828230: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.828769: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.829306: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.843807: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.844390: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.868390: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.868414: 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. 2025-02-12 18:35:41.868958: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.869499: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.877759: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.878339: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.887024: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.887606: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.903446: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.904035: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.904583: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.905132: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.909576: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.910162: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.910741: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.911311: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.912361: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.923583: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.924118: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.945606: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.946196: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.946783: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.947364: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.947948: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:35:41.948522: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 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: (480, 640, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 372453 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1944 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 : 3137 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 : 0.0007929801940917969 nb_pixel_total : 15550 time to create 1 rle with old method : 0.03354692459106445 length of segment : 256 time for calcul the mask position with numpy : 0.0037245750427246094 nb_pixel_total : 145330 time to create 1 rle with old method : 0.16524624824523926 length of segment : 371 time for calcul the mask position with numpy : 0.00023984909057617188 nb_pixel_total : 14256 time to create 1 rle with old method : 0.02170538902282715 length of segment : 151 time for calcul the mask position with numpy : 0.00014019012451171875 nb_pixel_total : 5613 time to create 1 rle with old method : 0.009772062301635742 length of segment : 48 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1823 time to create 1 rle with old method : 0.0033195018768310547 length of segment : 39 time spent for convertir_results : 1.0940542221069336 time spend for datou_step_exec : 17.705400466918945 time spend to save output : 4.649162292480469e-05 total time spend for step 1 : 17.70544695854187 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 3265 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 : 0.0351560115814209 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[(957285035, 492601069, 445, 0, 186, 22, 282, 0.99552625, [(140, 26, 6), (135, 27, 15), (134, 28, 17), (131, 29, 22), (127, 30, 27), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,126,31,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.992378, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (239, 40, 150), (200, 41, 195), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 258), (169, 46, 266), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 311), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), (89, 111, 449), (89, 112, 450), (89, 113, 451), (89, 114, 453), (89, 115, 454), (89, 116, 455), (88, 117, 456), (88, 118, 457), (87, 119, 459), (87, 120, 459), (86, 121, 461), (86, 122, 461), (85, 123, 463), (84, 124, 464), (84, 125, 465), (83, 126, 466), (82, 127, 468), (82, 128, 468), (81, 129, 470), (80, 130, 471), (78, 131, 473), (77, 132, 475), (75, 133, 477), (73, 134, 480), (71, 135, 482), (70, 136, 484), (68, 137, 486), (67, 138, 488), (65, 139, 490), (64, 140, 492), (63, 141, 493), (61, 142, 496), (60, 143, 497), (59, 144, 499), (58, 145, 501), (58, 146, 501), (57, 147, 503), (57, 148, 504), (57, 149, 505), (56, 150, 507), (56, 151, 507), (55, 152, 509), (55, 153, 510), (54, 154, 511), (54, 155, 512), (54, 156, 513), (53, 157, 514), (53, 158, 514), (52, 159, 516), (52, 160, 516), (52, 161, 516), (51, 162, 517), (51, 163, 517), (50, 164, 518), (50, 165, 518), (49, 166, 519), (49, 167, 520), (48, 168, 521), (48, 169, 521), (47, 170, 522), (47, 171, 522), (46, 172, 523), (46, 173, 523), (46, 174, 523), (45, 175, 524), (45, 176, 523), (44, 177, 524), (44, 178, 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33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,499,8,500,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1739381728_372220_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 3137 ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.17292165756225586 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 Wed Feb 12 18:35:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 3137 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-12 18:35:50.789055: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-12 18:35:50.815143: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-12 18:35:50.817302: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f50a0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-12 18:35:50.817333: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-12 18:35:50.821792: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-12 18:35:51.077621: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2859e400 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-12 18:35:51.077680: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-12 18:35:51.078561: 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 2025-02-12 18:35:51.078941: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:35:51.081160: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:35:51.083432: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:35:51.083786: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:35:51.086379: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:35:51.087639: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:35:51.092586: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:35:51.093731: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:35:51.093827: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:35:51.094505: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 18:35:51.094527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 18:35:51.094540: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 18:35:51.095805: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2685 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. 2025-02-12 18:35:51.183610: 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 2025-02-12 18:35:51.183765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:35:51.183788: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:35:51.183807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:35:51.183823: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:35:51.183839: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:35:51.183855: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:35:51.183873: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:35:51.184723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:35:51.185737: 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 2025-02-12 18:35:51.185791: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:35:51.185809: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:35:51.185825: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:35:51.185841: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:35:51.185857: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:35:51.185872: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:35:51.185889: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:35:51.186721: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:35:51.186760: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 18:35:51.186769: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 18:35:51.186777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 18:35:51.187702: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2685 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 : [] 2025-02-12 18:35:58.525171: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:35:58.715658: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:36:00.054213: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.054301: 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. 2025-02-12 18:36:00.054838: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.054853: 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. 2025-02-12 18:36:00.061855: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.061875: 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. 2025-02-12 18:36:00.062402: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.062417: 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. 2025-02-12 18:36:00.068923: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.068944: 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. 2025-02-12 18:36:00.069513: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.069529: 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. 2025-02-12 18:36:00.099934: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.099957: 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. 2025-02-12 18:36:00.100481: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.100495: 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. 2025-02-12 18:36:00.106232: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.106251: 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. 2025-02-12 18:36:00.106786: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.106801: 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. 2025-02-12 18:36:00.140241: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.140823: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.142675: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.143251: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.187850: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.188397: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.196180: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.196724: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.201489: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.202029: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.214407: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.214959: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.216628: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.217165: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.223601: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.224176: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.226026: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.226561: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.233067: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.233674: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.235408: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.235988: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.266702: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.267305: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.267845: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.268382: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.272376: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.272914: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.291173: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.291776: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.292331: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.292859: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.308191: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.308727: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.309256: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.309784: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.314612: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.315197: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.320368: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.320906: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.333761: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.334300: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.338578: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.339146: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.339702: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.340264: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.361699: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.362238: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.362783: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.363329: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.363861: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.364390: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.378704: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.379247: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.397449: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.397509: 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. 2025-02-12 18:36:00.398420: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.399219: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.406714: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.407499: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.415874: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.416561: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.431954: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.432747: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.433480: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.434207: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.438446: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.439237: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.439957: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.440573: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.441536: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.451725: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.452302: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.462472: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.463040: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.463636: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.464208: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.464789: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:00.465361: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2280521728 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 373505 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1944 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 : 3137 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 : 0.00045680999755859375 nb_pixel_total : 16902 time to create 1 rle with old method : 0.01879262924194336 length of segment : 107 time for calcul the mask position with numpy : 0.026282548904418945 nb_pixel_total : 480724 time to create 1 rle with new method : 0.03537106513977051 length of segment : 632 time for calcul the mask position with numpy : 0.00044918060302734375 nb_pixel_total : 36639 time to create 1 rle with old method : 0.041803836822509766 length of segment : 133 time for calcul the mask position with numpy : 0.00011706352233886719 nb_pixel_total : 4794 time to create 1 rle with old method : 0.005883693695068359 length of segment : 51 time spent for convertir_results : 0.30786967277526855 time spend for datou_step_exec : 15.932025909423828 time spend to save output : 4.410743713378906e-05 total time spend for step 1 : 15.932070016860962 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 397 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 : 0.020998001098632812 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, 0.99883634, [(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, 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['450,47,449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,419,25,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46,451,46'])], 'temp/1739381747_372220_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 : 0.14030790328979492 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 Wed Feb 12 18:36:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2786 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-12 18:36:08.052993: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-12 18:36:08.079151: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-12 18:36:08.080739: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f50a8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-12 18:36:08.080759: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-12 18:36:08.083763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-12 18:36:08.255734: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x26bb3880 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-12 18:36:08.255808: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-12 18:36:08.256693: 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 2025-02-12 18:36:08.257084: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:36:08.259661: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:36:08.262128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:36:08.262490: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:36:08.265049: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:36:08.266349: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:36:08.271826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:36:08.272922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:36:08.273020: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:36:08.273615: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 18:36:08.273633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 18:36:08.273641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 18:36:08.274579: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1886 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. 2025-02-12 18:36:08.355674: 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 2025-02-12 18:36:08.355797: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:36:08.355832: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:36:08.355850: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:36:08.355868: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:36:08.355885: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:36:08.355901: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:36:08.355919: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:36:08.356725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:36:08.357662: 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 2025-02-12 18:36:08.357701: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:36:08.357719: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:36:08.357735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:36:08.357751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:36:08.357767: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:36:08.357783: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:36:08.357799: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:36:08.358566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:36:08.358603: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 18:36:08.358611: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 18:36:08.358619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 18:36:08.359436: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1886 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 : [] 2025-02-12 18:36:17.570102: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:36:17.769198: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:36:19.252822: 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. 2025-02-12 18:36:19.252888: 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. 2025-02-12 18:36:19.259150: 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. 2025-02-12 18:36:19.259174: 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. 2025-02-12 18:36:19.265661: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.265683: 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. 2025-02-12 18:36:19.266162: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.266177: 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. 2025-02-12 18:36:19.295389: 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. 2025-02-12 18:36:19.295421: 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. 2025-02-12 18:36:19.301099: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.301120: 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. 2025-02-12 18:36:19.301612: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.301627: 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. 2025-02-12 18:36:19.335538: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.336071: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.426608: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.427125: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.433708: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.434215: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.443645: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.444158: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.477101: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.477679: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.479299: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.479827: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.485323: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.485854: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.487463: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.487973: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.493529: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.494036: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.495497: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.496004: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.522573: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.523120: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.523635: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.524148: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.527671: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.528190: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.583605: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.584123: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.584625: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.585107: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.597649: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.598125: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.598595: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.600905: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.605313: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.605898: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.625090: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.625620: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.649185: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.649663: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.653685: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.654191: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.675494: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.676002: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.676488: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.676958: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.677428: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.677897: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.715904: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.715954: 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. 2025-02-12 18:36:19.716472: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.716983: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.724837: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.725466: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.733590: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.734099: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.749004: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.749526: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.750052: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.750554: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.754559: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.755084: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.755591: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.756095: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.756953: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.767230: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.767741: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.778015: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.778533: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.779173: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.779687: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.780213: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:36:19.780717: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.34G (1442709504 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: (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 374600 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 532 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 : 1725 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 : 1.049274206161499 nb_pixel_total : 3696994 time to create 1 rle with new method : 0.5084660053253174 length of segment : 2044 time spent for convertir_results : 2.883514165878296 time spend for datou_step_exec : 20.73599147796631 time spend to save output : 7.104873657226562e-05 total time spend for step 1 : 20.73606252670288 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 718 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 : 0.014594793319702148 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 0, 2279, 103, 2222, 0.98186904, [(1250, 110, 27), (653, 111, 288), (1202, 111, 135), (614, 112, 376), (1081, 112, 346), (525, 113, 909), (518, 114, 922), (511, 115, 936), (505, 116, 948), (499, 117, 960), (493, 118, 971), (487, 119, 983), (481, 120, 994), (476, 121, 1004), (470, 122, 1015), (465, 123, 1025), (461, 124, 1033), (456, 125, 1043), (451, 126, 1052), (447, 127, 1062), (443, 128, 1075), (438, 129, 1089), (434, 130, 1102), (431, 131, 1114), (427, 132, 1127), (423, 133, 1139), (420, 134, 1150), (416, 135, 1162), (413, 136, 1172), (410, 137, 1180), (406, 138, 1187), (403, 139, 1194), (400, 140, 1200), (397, 141, 1206), (395, 142, 1211), (392, 143, 1218), (389, 144, 1224), (385, 145, 1231), (382, 146, 1238), (379, 147, 1245), (375, 148, 1253), (372, 149, 1260), (368, 150, 1268), (365, 151, 1275), (363, 152, 1281), (361, 153, 1288), (358, 154, 1295), (356, 155, 1302), (353, 156, 1310), (350, 157, 1318), (348, 158, 1325), (345, 159, 1333), (342, 160, 1341), (339, 161, 1350), (336, 162, 1358), (333, 163, 1367), (330, 164, 1376), (327, 165, 1385), (324, 166, 1395), (321, 167, 1404), (318, 168, 1414), (314, 169, 1426), (311, 170, 1436), (307, 171, 1448), (303, 172, 1461), (300, 173, 1472), (296, 174, 1485), (292, 175, 1497), (288, 176, 1510), (284, 177, 1523), (281, 178, 1535), (277, 179, 1548), (274, 180, 1559), (271, 181, 1567), (268, 182, 1574), (266, 183, 1580), (263, 184, 1588), (260, 185, 1595), (258, 186, 1600), (255, 187, 1607), (253, 188, 1613), (250, 189, 1619), (248, 190, 1625), (246, 191, 1630), (243, 192, 1636), (241, 193, 1641), (239, 194, 1646), (237, 195, 1651), (235, 196, 1656), (233, 197, 1661), (231, 198, 1665), (229, 199, 1670), (227, 200, 1674), (225, 201, 1679), (223, 202, 1683), (222, 203, 1686), (220, 204, 1691), (218, 205, 1695), (217, 206, 1697), (215, 207, 1700), (214, 208, 1703), (212, 209, 1706), (210, 210, 1709), (209, 211, 1711), (207, 212, 1715), (206, 213, 1717), (205, 214, 1719), (203, 215, 1722), (202, 216, 1724), (201, 217, 1726), (199, 218, 1729), (198, 219, 1731), (197, 220, 1732), (196, 221, 1734), (194, 222, 1737), (193, 223, 1739), (192, 224, 1741), (190, 225, 1744), (189, 226, 1746), (188, 227, 1748), (186, 228, 1751), (185, 229, 1753), (183, 230, 1756), (182, 231, 1758), (181, 232, 1761), (179, 233, 1764), (178, 234, 1766), (176, 235, 1769), (175, 236, 1771), (173, 237, 1774), (172, 238, 1776), (170, 239, 1780), (169, 240, 1782), (167, 241, 1785), (166, 242, 1787), (164, 243, 1791), (163, 244, 1793), (161, 245, 1796), (160, 246, 1799), (158, 247, 1802), (156, 248, 1805), (155, 249, 1808), (153, 250, 1811), (151, 251, 1814), (150, 252, 1817), (148, 253, 1820), (146, 254, 1824), (145, 255, 1826), (143, 256, 1830), (141, 257, 1834), (140, 258, 1836), (138, 259, 1840), (136, 260, 1843), (134, 261, 1847), (132, 262, 1851), (131, 263, 1854), (129, 264, 1857), (127, 265, 1861), (125, 266, 1865), (123, 267, 1869), (122, 268, 1872), (120, 269, 1875), (119, 270, 1878), (118, 271, 1880), (117, 272, 1882), (115, 273, 1886), (114, 274, 1888), (113, 275, 1890), (112, 276, 1892), (111, 277, 1894), (110, 278, 1896), (109, 279, 1899), (108, 280, 1901), (107, 281, 1903), (106, 282, 1905), (106, 283, 1905), (105, 284, 1907), (104, 285, 1909), (103, 286, 1911), (102, 287, 1913), (101, 288, 1915), (101, 289, 1916), (100, 290, 1918), (99, 291, 1919), (99, 292, 1920), (98, 293, 1921), (98, 294, 1922), (97, 295, 1923), (97, 296, 1924), (97, 297, 1924), (96, 298, 1926), (96, 299, 1926), (95, 300, 1928), (95, 301, 1928), (95, 302, 1929), (94, 303, 1930), (94, 304, 1931), (94, 305, 1931), (93, 306, 1933), (93, 307, 1933), (92, 308, 1935), (92, 309, 1935), (92, 310, 1936), (91, 311, 1937), (91, 312, 1938), (91, 313, 1938), (90, 314, 1940), (90, 315, 1940), (89, 316, 1942), (89, 317, 1942), (89, 318, 1943), (88, 319, 1944), (88, 320, 1945), (88, 321, 1945), (87, 322, 1947), (87, 323, 1948), (87, 324, 1948), (86, 325, 1950), (86, 326, 1950), (86, 327, 1951), (85, 328, 1952), (85, 329, 1953), (84, 330, 1954), (84, 331, 1955), (84, 332, 1955), (83, 333, 1957), (83, 334, 1957), (83, 335, 1958), (82, 336, 1959), (82, 337, 1960), (82, 338, 1961), (81, 339, 1962), (81, 340, 1963), (81, 341, 1963), (80, 342, 1965), (80, 343, 1965), (80, 344, 1966), (79, 345, 1967), (79, 346, 1968), (79, 347, 1968), (78, 348, 1970), (78, 349, 1971), (78, 350, 1971), (77, 351, 1973), (77, 352, 1973), (77, 353, 1974), (76, 354, 1975), (76, 355, 1976), (76, 356, 1977), (75, 357, 1978), (75, 358, 1979), (75, 359, 1979), (74, 360, 1981), (74, 361, 1981), (74, 362, 1982), (74, 363, 1983), (73, 364, 1984), (73, 365, 1985), (73, 366, 1985), (72, 367, 1987), (72, 368, 1988), (72, 369, 1988), (72, 370, 1989), (71, 371, 1991), (71, 372, 1992), (71, 373, 1993), (71, 374, 1993), (70, 375, 1995), (70, 376, 1996), (70, 377, 1997), (70, 378, 1998), (69, 379, 2000), (69, 380, 2001), (69, 381, 2002), (69, 382, 2003), (68, 383, 2005), (68, 384, 2006), (68, 385, 2007), (67, 386, 2009), (67, 387, 2011), (67, 388, 2012), (67, 389, 2013), (66, 390, 2015), (66, 391, 2016), (66, 392, 2017), (65, 393, 2019), (65, 394, 2020), (65, 395, 2021), (65, 396, 2022), (64, 397, 2023), (64, 398, 2024), (64, 399, 2025), (63, 400, 2027), (63, 401, 2028), (63, 402, 2029), (62, 403, 2030), (62, 404, 2031), (62, 405, 2032), (62, 406, 2033), (61, 407, 2034), (61, 408, 2035), (61, 409, 2036), (60, 410, 2037), (60, 411, 2038), (60, 412, 2039), (59, 413, 2040), (59, 414, 2041), (59, 415, 2042), (58, 416, 2043), (58, 417, 2044), (57, 418, 2046), (57, 419, 2046), (57, 420, 2047), (56, 421, 2048), (56, 422, 2049), (56, 423, 2049), (55, 424, 2051), (55, 425, 2051), (55, 426, 2052), (54, 427, 2053), (54, 428, 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['939,2141,833,2116,780,2094,650,2058,543,2017,367,1982,204,1964,122,1970,88,1911,51,1806,51,1724,40,1658,40,1436,29,1276,30,889,19,682,29,525,47,444,97,295,120,269,250,189,416,135,525,113,653,111,1426,112,1584,136,1747,171,1832,180,1910,204,2017,290,2059,369,2114,443,2166,665,2152,822,2127,898,2119,974,2093,1050,2062,1084,1959,1275,1939,1350,1886,1426,1852,1634,1784,1856,1727,1962,1668,2011,1585,2014,1506,2049,1428,2054,1263,2079,1093,2137'])], 'temp/1739381765_372220_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3678118 proportion of common points : 0.9961603826346487 [('test release memory', 'SUCCESS', True), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)] res_total : True #&_# TEST SUCCEEDED #&_# : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_a322f3f957fb93d8dde36d0c765e11c11a7c8a94 SQL :INSERT INTO MTRAdmin.monitor_sys (name, type, server, version_code, result_str, result_bool, lien , test_group ,test_name) VALUES ('python_test3','1','marlene','refs/heads/master_a322f3f957fb93d8dde36d0c765e11c11a7c8a94','{"mask_detection": "success"}','1','http://marlene.fotonower-preprod.com/job/2025/February/12022025/python_test3//data_2/data_log/job/2025/February/12022025/python_test3/log-python3----short_python3--v--marlene-18:35:01.txt','mask_detection','unknown'); #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4573 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4573 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4573 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4573 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : sam list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1189321094) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 1189321094 download finish for photo 1189321094 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.20864605903625488 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:sam Wed Feb 12 18:36:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739381791_372220_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1739381791_372220_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png', 'extension': 'png'}} map_subphoto_mainphoto : {} Beginning of datou step sam ! pht : 4677 Inside sam : nb paths : 1 (640, 960, 3) ERROR in datou_step_exec, will save and exit ! CUDA out of memory. Tried to allocate 768.00 MiB (GPU 0; 10.76 GiB total capacity; 443.59 MiB already allocated; 207.88 MiB free; 530.00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2430, in datou_step_exec return lib_process.datou_step_sam(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 396, in datou_step_sam masks = mask_generator.generate(image) File "/home/admin/.local/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/home/admin/workarea/install/segment-anything/segment_anything/automatic_mask_generator.py", line 163, in generate mask_data = self._generate_masks(image) File "/home/admin/workarea/install/segment-anything/segment_anything/automatic_mask_generator.py", line 206, in _generate_masks crop_data = self._process_crop(image, crop_box, layer_idx, orig_size) File "/home/admin/workarea/install/segment-anything/segment_anything/automatic_mask_generator.py", line 236, in _process_crop self.predictor.set_image(cropped_im) File "/home/admin/workarea/install/segment-anything/segment_anything/predictor.py", line 60, in set_image self.set_torch_image(input_image_torch, image.shape[:2]) File "/home/admin/.local/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/home/admin/workarea/install/segment-anything/segment_anything/predictor.py", line 89, in set_torch_image self.features = self.model.image_encoder(input_image) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl return forward_call(*input, **kwargs) File "/home/admin/workarea/install/segment-anything/segment_anything/modeling/image_encoder.py", line 112, in forward x = blk(x) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl return forward_call(*input, **kwargs) File "/home/admin/workarea/install/segment-anything/segment_anything/modeling/image_encoder.py", line 174, in forward x = self.attn(x) File "/home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl return forward_call(*input, **kwargs) File "/home/admin/workarea/install/segment-anything/segment_anything/modeling/image_encoder.py", line 231, in forward attn = (q * self.scale) @ k.transpose(-2, -1) [1189321094] map_info['map_portfolio_photo'] : {} final : True mtd_id 4573 list_pids : [1189321094] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4573', None, '1189321094', "[>, , , , , 'CUDA out of memory. Tried to allocate 768.00 MiB (GPU 0; 10.76 GiB total capacity; 443.59 MiB already allocated; 207.88 MiB free; 530.00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF']", '-1', '-1.0', '501120777', '1.0', None)] time used for this insertion : 0.013017654418945312 save_final ERROR in last step sam, CUDA out of memory. Tried to allocate 768.00 MiB (GPU 0; 10.76 GiB total capacity; 443.59 MiB already allocated; 207.88 MiB free; 530.00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF time spend for datou_step_exec : 6.83764386177063 time spend to save output : 0.0679931640625 total time spend for step 0 : 6.90563702583313 need to delete datou_research and reload, so keep current state 1 need to delete datou_research and reload, so keep current state 1 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : None ERROR nb objects espect : 98 nb_objects detect : 0 ERROR sam FAILED ############################### TEST frcnn ################################ test frcnn Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4184 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4184 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4184 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4184 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : frcnn list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917754606) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917754606 download finish for photo 917754606 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1594698429107666 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:frcnn Wed Feb 12 18:36:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739381798_372220_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1739381798_372220_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Faster rcnn ! classes : ['background', 'plaque'] pht : 4370 caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 3375, 'mtr_user_id': 31, 'name': 'detection_plaque_valcor_010622', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,plaque', 'svm_portfolios_learning': '0,0', 'photo_hashtag_type': 4370, 'photo_desc_type': 5676, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0'} To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1739381798_372220_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg image_size (600, 800, 3) [[[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] ... [[ 14 16 16] [ 13 15 15] [ 11 13 13] ... [198 206 205] [198 206 205] [198 206 205]] [[ 16 18 18] [ 14 16 16] [ 11 13 13] ... [206 214 213] [206 214 213] [206 214 213]] [[ 13 15 15] [ 12 14 14] [ 9 11 11] ... [210 218 217] [210 218 217] [210 218 217]]] Detection took 0.086s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.06384062 (374.1269, 293.9192, 430.81012, 317.80853) proba : 0.052220896 (382.17758, 297.18884, 552.3578, 344.65814) proba : 0.0122712925 (345.3569, 272.42984, 468.85757, 320.72427) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 2.6124348640441895 time spend to save output : 0.00013756752014160156 total time spend for step 1 : 2.612572431564331 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True Inside saveFrcnn : final : True verbose : True threshold to save the result : 0.1 output flattener : [(0, 493029425, 4370, 374, 430, 293, 317, 0.06384062, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052220896, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.0122712925, None)] Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4184', None, '917754606', '0', 0, '0', 493061979, '0', None)] time used for this insertion : 0.01308298110961914 [917754606] map_info['map_portfolio_photo'] : {} final : True mtd_id 4184 list_pids : [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4184', None, '917754606', None, None, None, None, None, None)] time used for this insertion : 0.011218070983886719 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.06384062, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052220896, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.0122712925, None)], 'temp/1739381798_372220_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=2 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 2 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=2 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (916235064) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 916235064 download finish for photo 916235064 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.10353827476501465 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : True number of steps : 2 step1:thcl Wed Feb 12 18:36:41 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739381801_372220_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1739381801_372220_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'355': 1} we are using the classfication for only one thcl 355 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (66, 66, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0043833255767822266 time to convert the images to numpy array : 0.0005695819854736328 total time to convert the images to numpy array : 0.005146980285644531 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (355) thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 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'svm_portfolios_learning': 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'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 1389 wait 20 seconds l 3637 free memory gpu now : 1389 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) param : , param.caffemodel : car_360_1027 None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : car_360_1027 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 194 wait 20 seconds l 3637 free memory gpu now : 194 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.015129566192626953 time used to do the prediction : 0.07991290092468262 save descriptor for thcl : 355 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.05049395561218262 Testing : ['916235064'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (916235064) Catched exception ! Connect or reconnect ! result : {916235064: {'photo_id': 916235064, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2017/10/14/6293d1bb790dc6902450e7c572b7d10b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': None}} list_photo_exists : [916235064] storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.7102513313293457 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True time used to find the portfolios of the photos select button_legend_list from MTRDatou.classification_theme where id = 355 SAVE THCL, output : {'916235064': [[('916235064', 'c_elysee_1027_gao__port_506302', 0.0018817235, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011633587, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.00081577536, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.001177382, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.0025849023, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.00417041, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.003481643, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.007366898, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021885221, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.0005798293, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.0045914296, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031588727, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.0022506525, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.0053206556, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0010998052, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.0054018805, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.0039190133, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014788725, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019777825, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.001244127, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.001504888, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021690254, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.0027082108, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.004702088, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019415938, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.0009585264, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.0007699619, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019469456, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013726263, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.0016200843, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013924837, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010045076, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010793393, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.0008447073, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.0012519264, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.0025843636, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012429471, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.0028648365, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.001294903, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010340928, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018764988, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011343808, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015758971, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.003980247, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.0028738556, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023356653, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012106723, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.001144855, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.0022338142, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017464807, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.004700075, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021914544, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022628882, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018173557, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.00090578245, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.0020404144, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025338219, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.004489287, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034251993, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031350001, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010253468, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.0015355888, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.00059150666, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.0040302277, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011585251, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012159442, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014966642, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.0011319944, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010545949, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011984004, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.00418537, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.002363706, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016325795, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.001556577, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013958989, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.0021977385, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.00882772, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.0014617655, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.0008826669, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.00247155, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.001009304, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015963122, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.0015329133, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.0014918036, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014585035, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.005645529, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.0011166077, 332, '355'), 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('916235064', 'viano_1027_gao__port_506211', 0.0026946052, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.0008319334, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037377768, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.00079201325, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.003053244, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.0037230288, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017738711, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015305998, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004086935, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.00118612, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.002695683, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011407274, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012531207, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.001515037, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031654371, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017348223, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.016724039, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018254691, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.002145813, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014151236, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.002055099, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.0022967474, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013944935, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.011968281, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010548712, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020712584, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017868136, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.00572165, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.0027661703, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017142505, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.004825274, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018687895, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012581918, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012626232, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.0009243956, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.0074667213, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.0007290661, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.0010386982, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.00069107395, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.002141875, 332, '355')]]} begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 7.152557373046875e-06 save missing photos in datou_result : time spend for datou_step_exec : 46.46305298805237 time spend to save output : 1.5253245830535889 total time spend for step 1 : 47.98837757110596 step2:argmax Wed Feb 12 18:37:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739381801_372220_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1739381801_372220_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 355 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.017713634, 332, '355'), 'temp/1739381801_372220_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('916235064', '2049863950', '332') ... last line : ('916235064', '2049863950', '332') time used for this insertion : 0.008800268173217773 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.011662721633911133 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('2', None, '916235064', 'c15_1027_gao__port_506055', None, None, '2049863950', '0.017713634', None)] time used for this insertion : 0.012067317962646484 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 4.5299530029296875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0005350112915039062 time spend to save output : 0.033144235610961914 total time spend for step 2 : 0.03367924690246582 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.017713634, 332, '355'), 'temp/1739381801_372220_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4567 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4567 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4567 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4567 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12835 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12836 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171252784,1171252764,1171252487) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171252487 begin to download photo : 1171252764 begin to download photo : 1171252784 download finish for photo 1171252487 download finish for photo 1171252764 download finish for photo 1171252784 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.7122743129730225 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : True number of steps : 2 step1:tfhub_classification2 Wed Feb 12 18:37:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739381849_372220_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1739381849_372220_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1739381849_372220_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784} map_photo_id_path_extension : {1171252487: {'path': 'temp/1739381849_372220_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1739381849_372220_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252784: {'path': 'temp/1739381849_372220_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3609': 1} we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1389 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1389 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1170 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1168 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1166 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 1168 wait 20 seconds 2025-02-12 18:39:41.191816: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-12 18:39:41.192471: 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 2025-02-12 18:39:41.192568: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:39:41.192616: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:39:41.195678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:39:41.195770: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:39:41.199756: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:39:41.201242: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:39:41.207711: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:39:41.208730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:39:41.209378: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-12 18:39:41.243388: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-12 18:39:41.244899: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f4eb0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-12 18:39:41.244919: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-12 18:39:41.247609: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x31f65ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-12 18:39:41.247629: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-12 18:39:41.248449: 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 2025-02-12 18:39:41.248607: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:39:41.248629: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 18:39:41.248749: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 18:39:41.248776: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 18:39:41.248811: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 18:39:41.248850: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 18:39:41.248889: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 18:39:41.249616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 18:39:41.249684: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 18:39:41.249731: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 18:39:41.249741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 18:39:41.249749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 18:39:41.250558: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) l 3637 free memory gpu now : 1168 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3609) thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (5832) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] 2025-02-12 18:39:50.050100: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.02G (3246391296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.050716: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.72G (2921752064 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.051364: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.45G (2629576704 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.051950: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2366618880 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.052532: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.98G (2129957120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.053132: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.79G (1916961536 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.053716: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.61G (1725265408 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.054300: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.45G (1552738816 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.054880: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.30G (1397465088 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 18:39:50.055492: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.17G (1257718528 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update ERROR in datou_step_exec, will save and exit ! assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3138, in datou_step_tfhub2 this_model = model_evaluator(model_name, model_type=model_type, fc_size=fc_size,use_multi_inputs=use_multi_inputs) File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 156, in __init__ self.model, _, _ = create_tfhub_model(module_handle=self.tfhub_module, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 77, in create_tfhub_model hub.KerasLayer(module_handle, trainable=do_fine_tuning, name="module"), File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 152, in __init__ self._func = load_module(handle, tags, self._load_options) File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 421, in load_module return module_v2.load(handle, tags=tags, options=set_load_options) File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_v2.py", line 106, in load obj = tf.compat.v1.saved_model.load_v2(module_path, tags=tags) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 578, in load return load_internal(export_dir, tags) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 602, in load_internal loader = loader_cls(object_graph_proto, File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 123, in __init__ self._load_all() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 134, in _load_all self._load_nodes() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 264, in _load_nodes node, setter = self._recreate(proto, node_id) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 370, in _recreate return factory[kind]() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 363, in "variable": lambda: self._recreate_variable(proto.variable), File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 426, in _recreate_variable return variables.Variable( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 261, in __call__ return cls._variable_v2_call(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 243, in _variable_v2_call return previous_getter( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 66, in getter return captured_getter(captured_previous, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 418, in uninitialized_variable_creator return resource_variable_ops.UninitializedVariable(**kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 263, in __call__ return super(VariableMetaclass, cls).__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 1795, in __init__ handle = _variable_handle_from_shape_and_dtype( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 174, in _variable_handle_from_shape_and_dtype gen_logging_ops._assert( # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py", line 55, in _assert _ops.raise_from_not_ok_status(e, name) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 6653, in raise_from_not_ok_status six.raise_from(core._status_to_exception(e.code, message), None) File "", line 3, in raise_from [1171252487, 1171252764, 1171252784] map_info['map_portfolio_photo'] : {} final : True mtd_id 4567 list_pids : [1171252487, 1171252764, 1171252784] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4567', None, '1171252487', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4567', None, '1171252764', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4567', None, '1171252784', "[>, , , , , 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None)] time used for this insertion : 0.013313531875610352 save_final ERROR in last step tfhub_classification2, assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse time spend for datou_step_exec : 140.31497049331665 time spend to save output : 0.02022576332092285 total time spend for step 0 : 140.33519625663757 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171252784': [(1171252784, 'jrm', 0.9677492, 4674, '3609'), 'temp/1687511175_1882837_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853587, 4674, '3609'), 'temp/1687511175_1882837_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262757, 4674, '3609'), 'temp/1687511175_1882837_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} got : None ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4621 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4621 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4621 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4621 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171291875,1171275372,1171275314) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171275314 begin to download photo : 1171275372 begin to download photo : 1171291875 download finish for photo 1171275314 download finish for photo 1171291875 download finish for photo 1171275372 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.2033224105834961 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : True number of steps : 2 step1:tfhub_classification2 Wed Feb 12 18:39:50 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739381990_372220_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1739381990_372220_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1739381990_372220_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171275314: {'path': 'temp/1739381990_372220_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171291875: {'path': 'temp/1739381990_372220_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1739381990_372220_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3655': 1} we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 30 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 32 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 251 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 693 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 88 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 693 wait 20 seconds l 3637 free memory gpu now : 693 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3655) thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (5862) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] 2025-02-12 18:42:08.930642: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_2 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_3 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_2[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_3[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 8.585910320281982 time used to load_weights : 0.1300492286682129 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 3.135885238647461 ['temp/1739381990_372220_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'temp/1739381990_372220_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1739381990_372220_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 (3, 1280) Got the blobs of the net to insert : [0, 0, 0, 0, 8, 0, 0, 0, 3, 0] code_as_byte_string:b'0000000008'| Got the blobs of the net to insert : [0, 1, 0, 0, 11, 0, 2, 2, 0, 0] code_as_byte_string:b'000100000b'| Got the blobs of the net to insert : [0, 0, 0, 0, 14, 0, 1, 4, 0, 0] code_as_byte_string:b'000000000e'| time to traite the descriptors : 0.040170907974243164 Testing : ['1171275314', '1171291875', '1171275372'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171275314,1171291875,1171275372) result : {1171275314: {'photo_id': 1171275314, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/6e0a72c8fa00d5e4b018bd689b547133.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_54_22_6187.jpg'}, 1171275372: {'photo_id': 1171275372, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/76d81364ff7df843bff095f45c07ba35.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_56_46_6098.jpg'}, 1171291875: {'photo_id': 1171291875, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/b62cd9e0d976b143f86fe82d072798c0.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_59_04_5803.jpg'}} list_photo_exists : [1171275314, 1171275372, 1171291875] storage_type for insertDescriptorsMulti : 3 To insert : 1171275314 To insert : 1171291875 To insert : 1171275372 time to insert the descriptors : 1.383350133895874 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171275314, 1171291875, 1171275372] map_info['map_portfolio_photo'] : {} final : False mtd_id 4621 list_pids : [1171275314, 1171291875, 1171275372] Looping around the photos to save general results len do output : 3 /1171275314Didn't retrieve data . /1171291875Didn't retrieve data . /1171275372Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4621', None, '1171275314', 'None', None, None, None, None, None), ('4621', None, '1171291875', 'None', None, None, None, None, None), ('4621', None, '1171275372', 'None', None, None, None, None, None)] time used for this insertion : 0.013411760330200195 save_final save missing photos in datou_result : time spend for datou_step_exec : 143.01366209983826 time spend to save output : 0.013789176940917969 total time spend for step 1 : 143.02745127677917 step2:argmax Wed Feb 12 18:42:13 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739381990_372220_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1739381990_372220_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1739381990_372220_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171275314: {'path': 'temp/1739381990_372220_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171291875: {'path': 'temp/1739381990_372220_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1739381990_372220_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.9652851, 4723, '3655'), 'temp/1739381990_372220_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.97081214, 4723, '3655'), 'temp/1739381990_372220_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96732384, 4723, '3655'), 'temp/1739381990_372220_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('1171275314', '2107748999', '4723') ... last line : ('1171275372', '2107748999', '4723') time used for this insertion : 0.007884740829467773 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.011989593505859375 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.9652851', None), ('4621', None, '1171291875', 'tapis_vide', None, None, '2107748999', '0.97081214', None), ('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.96732384', None)] time used for this insertion : 0.012892007827758789 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 3.337860107421875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0001773834228515625 time spend to save output : 0.03770852088928223 total time spend for step 2 : 0.03788590431213379 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171275314': [(1171275314, 'tapis_vide', 0.9652851, 4723, '3655'), 'temp/1739381990_372220_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171291875': [(1171291875, 'tapis_vide', 0.97081214, 4723, '3655'), 'temp/1739381990_372220_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.96732384, 4723, '3655'), 'temp/1739381990_372220_1171275372_76d81364ff7df843bff095f45c07ba35.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ERROR tfhub2 FAILED ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (358) thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 SELECT hashtag_id,hashtag FROM MTRBack.hashtags where hashtag in ('FirstUploadExperveo_vignette__port_505674','CAR_EXTERIEUR_Roue__port_503398','FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486','FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485','CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465','CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198','CAR_EXTERIEUR_Face_avant_axe_droit__port_504451','CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235','FirstUploadExperveo_vin__port_505675','CAR_EXTERIEUR_cote_droite__port_504108','CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565','FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483','CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201','cartegrise_orientation__port_505064','CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217','CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531','CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218','CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214','CAR_EXTERIEUR_Angle_avant_droit__port_504087','FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484','CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563','CAR_EXTERIEUR_Angle_arriere_droit__port_504160','CAR_EXTERIEUR_arriere__port_504184','CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562','INTERIEUR_Compteur_kilometrique__port_503644','CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494','CAR_EXTERIEUR_Angle_arriere_gauche__port_504170','CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226','CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202','CAR_EXTERIEUR_moteur__port_503704','FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487','CAR_INTERIEUR_siege_arriere_class_1__port_506551','CAR_EXTERIEUR_avant__port_504146','CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215','CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225','CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564','FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482','CAR_INTERIEUR_coffre__port_503412','FirstUploadExperveo_rouetranche__port_505677','UploadPhotoImmatBest_class_1__port_505051','CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532','CAR_EXTERIEUR_angle_avant_gauche__port_504098','CAR_EXTERIEUR_face_avant_axe_gauche__port_504236','CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540','CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233','CAR_EXTERIEUR_roue_de_secour__port_503763','CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199','CAR_EXTERIEUR_cote_gauche__port_504017','CAR_INTERIEUR_avant_volant_class_1__port_506503','CAR_INTERIEUR_avant_volant_class_2__port_506504','CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'); 51 dict_keys(['cartegrise_orientation__port_505064', 'car_exterieur_angle_arriere_droit_axe_arriere__port_504217', 'car_exterieur_angle_arriere_droit_axe_droit__port_504215', 'car_exterieur_angle_arriere_droit__port_504160', 'car_exterieur_angle_arriere_gauche_axe_arriere__port_504201', 'car_exterieur_angle_arriere_gauche_axe_gauche__port_504199', 'car_exterieur_angle_arriere_gauche__port_504170', 'car_exterieur_angle_avant_droit_axe_arriere__port_504226', 'car_exterieur_angle_avant_droit_axe_droit__port_504225', 'car_exterieur_angle_avant_droit__port_504087', 'car_exterieur_angle_avant_gauche_axe_avant__port_504235', 'car_exterieur_angle_avant_gauche_axe_gauche__port_504234', 'car_exterieur_angle_avant_gauche__port_504098', 'car_exterieur_arriere__port_504184', 'car_exterieur_avant__port_504146', 'car_exterieur_cote_droite__port_504108', 'car_exterieur_cote_droit_axe_arriere__port_504214', 'car_exterieur_cote_droit_axe_avant__port_504465', 'car_exterieur_cote_gauche_axe_arriere__port_504198', 'car_exterieur_cote_gauche_axe_avant__port_504233', 'car_exterieur_cote_gauche__port_504017', 'car_exterieur_face_arriere_axe_droit__port_504218', 'car_exterieur_face_arriere_axe_gauche__port_504202', 'car_exterieur_face_avant_axe_droit__port_504451', 'car_exterieur_face_avant_axe_gauche__port_504236', 'car_exterieur_moteur__port_503704', 'car_exterieur_roue_de_secour__port_503763', 'car_exterieur_roue__port_503398', 'car_interieur_avant_volant_class_1__port_506503', 'car_interieur_avant_volant_class_2__port_506504', 'car_interieur_avant_volant_class_6_boutonrond__port_506562', 'car_interieur_avant_volant_class_6_class_2__port_506563', 'car_interieur_avant_volant_class_6_ecrangrosplan__port_506564', 'car_interieur_avant_volant_class_6_levierdevitesse__port_506565', 'car_interieur_avant_vue-arriere_class_1__port_506531', 'car_interieur_avant_vue-arriere_class_2__port_506532', 'car_interieur_avant_vue_droite_habitacle_class_1__port_506540', 'car_interieur_avant_vue_gauche_habitacle_class_1__port_506494', 'car_interieur_coffre__port_503412', 'car_interieur_siege_arriere_class_1__port_506551', 'firstuploadexperveo_carrosseriegrosplan_carrosserie__port_506483', 'firstuploadexperveo_carrosseriegrosplan_class_6__port_506487', 'firstuploadexperveo_carrosseriegrosplan_morceauderoue__port_506484', 'firstuploadexperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'firstuploadexperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'firstuploadexperveo_carrosseriegrosplan_vindanslamoquette__port_506486', 'firstuploadexperveo_rouetranche__port_505677', 'firstuploadexperveo_vignette__port_505674', 'firstuploadexperveo_vin__port_505675', 'interieur_compteur_kilometrique__port_503644', 'uploadphotoimmatbest_class_1__port_505051']) select photo_hashtag_type from MTRDatou.classification_theme where id = 358 thcl : 358 photo_hashtag_type : 337 SELECT phi.hashtag_id , phi.photo_id FROM MTRBack.photo_hashtag_ids phi, MTRUser.mtr_portfolio_photos mp where phi.type = 337 and phi.photo_id = mp.mtr_photo_id and mp.mtr_portfolio_id =510365; {510365: [(917973295, 1), (917973297, 1), (917973302, 1), (917973293, 7), (917973296, 11), (917973300, 11), (917973286, 13), (917973289, 13), (917973301, 24), (917973285, 29), (917973290, 29), (917973299, 29), (917973304, 35), (917973287, 36), (917973298, 36), (917973305, 36), (917973292, 37), (917973291, 41), (917973303, 41), (917973294, 42), (917973288, 46)]} ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=230 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=230 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 230 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=230 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1182088851928711 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:rotate Wed Feb 12 18:42:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 90 degree temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 90 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : False image_rotate : image_path : temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 180 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : False image_rotate : image_path : temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 270 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : False image_rotate : image_path : temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg image_rotate.mode : RGB About to upload 3 photos upload in portfolio : 551782 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739382135_372220 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.297990322113037 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1337159086, 'temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1337159088, 'temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1337159089} Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] After datou_step_exec type output : time spend for datou_step_exec : 1.5279159545898438 time spend to save output : 4.57763671875e-05 total time spend for step 1 : 1.5279617309570312 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 230 list_pids : [917849322] Looping around the photos to save general results len do output : 3 /1337159086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337159088Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337159089Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('230', None, None, None, None, None, None, None, None) ('230', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('230', None, '1337159086', 'None', None, None, None, None, None), ('230', None, '1337159088', 'None', None, None, None, None, None), ('230', None, '1337159089', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.012602090835571289 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1337159086: ['917849322', 'temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1337159088: ['917849322', 'temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1337159089: ['917849322', 'temp/1739382134_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=233 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=233 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 233 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=233 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.10532116889953613 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : True number of steps : 3 step1:thcl Wed Feb 12 18:42:16 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1739382136_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1739382136_372220_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'500': 1} we are using the classfication for only one thcl 500 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (2448, 3264, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0002491474151611328 time to convert the images to numpy array : 2.89717698097229 total time to convert the images to numpy array : 2.8978214263916016 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (500) thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3517) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 237 wait 20 seconds l 3637 free memory gpu now : 237 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3517) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) param : , param.caffemodel : orientation_carte_grise_all_2 None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : orientation_carte_grise_all_2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 235 wait 20 seconds WARNING: Logging before InitGoogleLogging() is written to STDERR F0212 18:43:06.937943 372220 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Command terminated by signal 6 59.10user 42.55system 7:41.65elapsed 22%CPU (0avgtext+0avgdata 6104496maxresident)k 5678112inputs+23368outputs (11435major+5209517minor)pagefaults 0swaps