python /home/admin/mtr/script_for_cron.py -j coverage -m 9 -a '' -s coverage -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded root_folder /data_4/data_log/job/2026/March/01032026/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_4/data_log/job/2026/March/01032026/coverage/htmlcov new path : /data_4/data_log/job/2026/March/01032026/coverage/ command : coverage3 run /home/admin/workarea/git/Velours/python/tests/python_tests.py --short_python3 `cat ~/.fotonower_pass/bdd.py.pass` cat: /home/admin/.fotonower_pass/bdd.py.pass: Aucun fichier ou dossier de ce type import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data python version used : 3 #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10996 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 : 0.12899136543273926 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Sun Mar 1 05:20:28 2026 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 : 10996 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 2026-03-01 05:20:31.628091: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-03-01 05:20:31.654610: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-03-01 05:20:31.656146: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9bb0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-03-01 05:20:31.656195: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-03-01 05:20:31.658668: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-03-01 05:20:31.937325: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2bccad20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-03-01 05:20:31.937392: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-03-01 05:20:31.938882: 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 2026-03-01 05:20:31.939320: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:20:31.942574: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:20:31.970391: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:20:31.970927: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:20:31.974604: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:20:31.976415: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:20:31.981746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:20:31.983199: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:20:31.983273: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:20:31.984017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-03-01 05:20:31.984031: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-03-01 05:20:31.984040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-03-01 05:20:31.985336: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10191 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 2026-03-01 05:20:32.822504: 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 2026-03-01 05:20:32.822623: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:20:32.822646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:20:32.822667: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:20:32.822688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:20:32.822708: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:20:32.822727: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:20:32.822747: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:20:32.824207: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:20:32.825320: 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 2026-03-01 05:20:32.825351: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:20:32.825367: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:20:32.825382: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:20:32.825397: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:20:32.825412: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:20:32.825427: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:20:32.825442: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:20:32.826603: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:20:32.826637: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-03-01 05:20:32.826644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-03-01 05:20:32.826651: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-03-01 05:20:32.827852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10191 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 : [] file manque in s3 : ['mask_model.h5'] 2026-03-01 05:20:41.317924: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:20:41.478773: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin 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 3257178 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5704 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 : 10996 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'] DEBUG bbox = [22, 0, 282, 186] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 0.0005276203155517578 nb_pixel_total : 15553 time to create 1 rle with old method : 0.03502368927001953 length of segment : 256 DEBUG bbox = [24, 29, 419, 591] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 0.0024836063385009766 nb_pixel_total : 145331 time to create 1 rle with old method : 0.3128080368041992 length of segment : 371 DEBUG bbox = [23, 485, 174, 636] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 0.0002338886260986328 nb_pixel_total : 14256 time to create 1 rle with old method : 0.03168940544128418 length of segment : 151 DEBUG bbox = [2, 280, 55, 481] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 0.0001125335693359375 nb_pixel_total : 5613 time to create 1 rle with old method : 0.013305187225341797 length of segment : 48 DEBUG bbox = [6, 456, 45, 547] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 1825 time to create 1 rle with old method : 0.004670381546020508 length of segment : 39 time spent for convertir_results : 1.2396771907806396 time spend for datou_step_exec : 19.742902755737305 time spend to save output : 4.1484832763671875e-05 total time spend for step 1 : 19.74294424057007 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 3424 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 : 0.01812148094177246 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, 0.9955024, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (126, 30, 28), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 137), (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)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,149,56,138,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923728, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 310), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 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), (85, 122, 462), (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, 508), (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, 524), (44, 179, 524), (43, 180, 525), (43, 181, 525), (42, 182, 525), (42, 183, 525), (42, 184, 525), (41, 185, 526), (41, 186, 526), (40, 187, 526), (39, 188, 526), (39, 189, 525), (38, 190, 526), (38, 191, 525), (37, 192, 525), (37, 193, 524), (36, 194, 523), (36, 195, 523), (36, 196, 522), (35, 197, 522), (35, 198, 521), (34, 199, 521), (34, 200, 521), (34, 201, 520), (34, 202, 520), (34, 203, 520), (34, 204, 519), (34, 205, 519), (33, 206, 520), (33, 207, 519), (33, 208, 519), (33, 209, 519), (33, 210, 518), (33, 211, 518), (33, 212, 518), (33, 213, 517), (32, 214, 518), (32, 215, 517), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 512), (32, 222, 511), (32, 223, 510), (32, 224, 508), (32, 225, 507), (32, 226, 505), (32, 227, 504), (32, 228, 503), (32, 229, 502), (32, 230, 502), (32, 231, 501), (32, 232, 500), (32, 233, 499), (32, 234, 498), (32, 235, 497), (31, 236, 496), (31, 237, 495), (31, 238, 494), (31, 239, 493), (31, 240, 491), (31, 241, 490), (31, 242, 488), (31, 243, 487), (31, 244, 486), (31, 245, 485), (31, 246, 483), (31, 247, 482), (31, 248, 480), (31, 249, 479), (31, 250, 477), (31, 251, 475), (31, 252, 474), (31, 253, 472), (31, 254, 470), (31, 255, 468), (31, 256, 467), (31, 257, 465), (31, 258, 464), (31, 259, 463), (31, 260, 462), (31, 261, 461), (31, 262, 459), (31, 263, 458), (31, 264, 456), (31, 265, 455), (31, 266, 453), (31, 267, 451), (31, 268, 449), (31, 269, 448), (31, 270, 447), (31, 271, 445), (31, 272, 444), (31, 273, 443), (32, 274, 441), (32, 275, 440), (32, 276, 438), (32, 277, 437), (32, 278, 435), (32, 279, 434), (32, 280, 432), (33, 281, 429), (33, 282, 427), (33, 283, 426), (33, 284, 424), (33, 285, 423), (34, 286, 421), (34, 287, 420), (34, 288, 419), (35, 289, 416), (35, 290, 415), (35, 291, 414), (36, 292, 411), (36, 293, 410), (37, 294, 407), (37, 295, 406), (38, 296, 403), (38, 297, 401), (39, 298, 399), (39, 299, 397), (41, 300, 394), (42, 301, 392), (43, 302, 389), (44, 303, 387), (45, 304, 385), (46, 305, 382), (47, 306, 380), (47, 307, 378), (48, 308, 376), (49, 309, 373), (50, 310, 370), (51, 311, 368), (51, 312, 367), (52, 313, 365), (54, 314, 362), (55, 315, 360), (56, 316, 359), (58, 317, 356), (61, 318, 352), (64, 319, 349), (67, 320, 345), (70, 321, 341), (73, 322, 338), (75, 323, 335), (78, 324, 332), (80, 325, 329), (82, 326, 327), (84, 327, 324), (86, 328, 322), (88, 329, 320), (90, 330, 317), (93, 331, 314), (96, 332, 311), (99, 333, 307), (102, 334, 304), (105, 335, 300), (108, 336, 297), (111, 337, 294), (113, 338, 291), (115, 339, 289), (117, 340, 286), (119, 341, 283), (121, 342, 281), (123, 343, 278), (125, 344, 275), (127, 345, 272), (129, 346, 269), (132, 347, 266), (135, 348, 262), (138, 349, 258), (141, 350, 255), (143, 351, 252), (146, 352, 249), (147, 353, 247), (149, 354, 245), (151, 355, 242), (152, 356, 241), (154, 357, 239), (156, 358, 237), (159, 359, 233), (161, 360, 231), (163, 361, 229), (165, 362, 227), (167, 363, 224), (169, 364, 222), (170, 365, 221), (172, 366, 219), (173, 367, 218), (174, 368, 216), (176, 369, 214), (177, 370, 213), (178, 371, 212), (180, 372, 209), (183, 373, 206), (185, 374, 204), (188, 375, 200), (191, 376, 197), (194, 377, 193), (196, 378, 191), (199, 379, 188), (201, 380, 185), (203, 381, 183), (205, 382, 180), (207, 383, 178), (208, 384, 176), (210, 385, 174), (212, 386, 171), (213, 387, 169), (215, 388, 166), (218, 389, 162), (221, 390, 158), (225, 391, 153), (228, 392, 149), (232, 393, 144), (235, 394, 140), (238, 395, 136), (241, 396, 133), (245, 397, 128), (248, 398, 124), (252, 399, 119), (257, 400, 113), (263, 401, 105), (272, 402, 94), (283, 403, 82), (296, 404, 66), (306, 405, 53), (313, 406, 38), (321, 407, 23)], ['321,407,305,404,263,401,248,398,215,388,178,371,168,363,145,351,129,346,110,336,90,330,72,321,56,316,39,299,31,273,31,236,34,199,58,145,82,128,89,116,89,101,104,88,115,72,159,49,180,43,199,41,237,41,272,38,339,37,382,39,402,43,417,43,481,55,543,116,556,143,567,159,566,186,554,199,548,216,528,235,496,256,471,275,420,309,407,327,403,339,392,355,389,371,383,385,369,400,358,405']), (957285035, 492601069, 445, 485, 636, 23, 174, 0.97112346, [(540, 24, 21), (626, 24, 3), (531, 25, 50), (594, 25, 40), (527, 26, 107), (523, 27, 111), (520, 28, 114), (517, 29, 118), (516, 30, 119), (515, 31, 120), (513, 32, 122), (512, 33, 123), (510, 34, 125), (509, 35, 126), (507, 36, 128), (506, 37, 129), (504, 38, 131), (503, 39, 132), (501, 40, 134), (500, 41, 135), (499, 42, 136), (498, 43, 137), (497, 44, 138), (496, 45, 139), (496, 46, 139), (495, 47, 140), (495, 48, 140), (494, 49, 141), (493, 50, 142), (492, 51, 143), (491, 52, 144), (491, 53, 144), (490, 54, 145), (490, 55, 145), (490, 56, 145), (490, 57, 146), (490, 58, 146), (490, 59, 146), (491, 60, 145), (491, 61, 145), (491, 62, 145), (492, 63, 144), (493, 64, 143), (494, 65, 142), (495, 66, 141), (496, 67, 140), (497, 68, 138), (498, 69, 138), (499, 70, 137), (500, 71, 136), (501, 72, 135), (503, 73, 133), (503, 74, 133), (505, 75, 131), (506, 76, 130), (507, 77, 129), (508, 78, 128), (509, 79, 127), (510, 80, 126), (511, 81, 125), (512, 82, 124), (513, 83, 123), (514, 84, 122), (515, 85, 121), (516, 86, 120), (517, 87, 119), (518, 88, 118), (519, 89, 117), (521, 90, 115), (521, 91, 115), (522, 92, 114), (523, 93, 113), (524, 94, 112), (525, 95, 111), (526, 96, 110), (527, 97, 109), (529, 98, 107), (530, 99, 106), (532, 100, 104), (533, 101, 103), (534, 102, 102), (535, 103, 101), (536, 104, 100), (538, 105, 98), (540, 106, 96), (541, 107, 95), (543, 108, 93), (546, 109, 90), (548, 110, 88), (549, 111, 87), (551, 112, 84), (552, 113, 83), (553, 114, 82), (555, 115, 80), (556, 116, 79), (556, 117, 79), (557, 118, 78), (558, 119, 77), (559, 120, 76), (560, 121, 75), (560, 122, 75), (561, 123, 74), (561, 124, 74), (561, 125, 74), (562, 126, 73), (562, 127, 73), (563, 128, 72), (563, 129, 72), (564, 130, 70), (564, 131, 70), (565, 132, 69), (565, 133, 68), (565, 134, 68), (565, 135, 67), (566, 136, 65), (566, 137, 64), (566, 138, 64), (566, 139, 62), (566, 140, 61), (566, 141, 59), (566, 142, 57), (566, 143, 56), (566, 144, 55), (566, 145, 54), (567, 146, 53), (567, 147, 52), (567, 148, 51), (568, 149, 50), (568, 150, 49), (568, 151, 48), (568, 152, 47), (569, 153, 45), (569, 154, 44), (570, 155, 42), (570, 156, 42), (570, 157, 41), (571, 158, 39), (571, 159, 39), (572, 160, 37), (572, 161, 37), (573, 162, 35), (573, 163, 34), (573, 164, 34), (574, 165, 32), (575, 166, 30), (577, 167, 28), (578, 168, 26), (581, 169, 22), (584, 170, 19), (587, 171, 15), (591, 172, 8)], ['598,172,591,172,590,171,578,168,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,501,40,514,32,517,29,531,25,539,25,540,24,560,24,561,25,580,25,581,26,593,26,594,25,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161,604,166']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8295239, [(292, 3, 128), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 44), (432, 38, 41), (434, 39, 40), (436, 40, 39), (438, 41, 37), (441, 42, 35), (444, 43, 32), (448, 44, 29), (452, 45, 25), (454, 46, 23), (459, 47, 17), (463, 48, 12), (468, 49, 5)], ['472,49,468,49,467,48,459,47,458,46,454,46,451,44,448,44,447,43,444,43,440,41,438,41,428,36,424,36,423,35,419,35,418,34,409,34,408,33,401,33,400,32,371,32,370,31,337,31,336,30,283,31,281,29,281,6,284,4,291,4,292,3,419,3,420,4,429,4,430,5,432,5,436,7,441,11,445,12,453,16,456,19,457,19,465,27,465,29,472,37,476,44,476,46']), (957285035, 492601069, 445, 456, 547, 6, 45, 0.74141634, [(482, 8, 19), (463, 9, 4), (481, 9, 44), (457, 10, 12), (479, 10, 50), (457, 11, 13), (476, 11, 56), (457, 12, 15), (475, 12, 65), (457, 13, 84), (457, 14, 85), (457, 15, 89), (457, 16, 89), (458, 17, 88), (459, 18, 87), (460, 19, 86), (461, 20, 80), (464, 21, 71), (466, 22, 63), (467, 23, 59), (468, 24, 55), (469, 25, 52), (469, 26, 51), (470, 27, 48), (471, 28, 46), (471, 29, 44), (472, 30, 42), (473, 31, 39), (473, 32, 38), (474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1772338828_3257054_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10996 ############################### 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 : 0.16899418830871582 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Sun Mar 1 05:20:51 2026 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 : 10996 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-03-01 05:20:54.814295: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-03-01 05:20:54.838673: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-03-01 05:20:54.840392: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9bb4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-03-01 05:20:54.840427: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-03-01 05:20:54.843128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-03-01 05:20:55.131917: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x291bb270 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-03-01 05:20:55.131965: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-03-01 05:20:55.133373: 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 2026-03-01 05:20:55.133760: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:20:55.136592: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:20:55.139133: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:20:55.139549: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:20:55.141981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:20:55.143262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:20:55.148088: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:20:55.149853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:20:55.149934: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:20:55.150974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-03-01 05:20:55.150995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-03-01 05:20:55.151005: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-03-01 05:20:55.152378: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10191 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. 2026-03-01 05:20:55.262937: 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 2026-03-01 05:20:55.263043: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:20:55.263070: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:20:55.263094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:20:55.263117: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:20:55.263140: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:20:55.263163: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:20:55.263186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:20:55.264803: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:20:55.266244: 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 2026-03-01 05:20:55.266302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:20:55.266332: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:20:55.266353: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:20:55.266373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:20:55.266393: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:20:55.266412: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:20:55.266432: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:20:55.268028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:20:55.268064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-03-01 05:20:55.268075: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-03-01 05:20:55.268084: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-03-01 05:20:55.269745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10191 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 : [] file manque in s3 : ['mask_model.h5'] 2026-03-01 05:21:03.419513: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:21:03.593176: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin 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 3257472 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5704 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 : 10996 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'] DEBUG bbox = [0, 1092, 108, 1280] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.0006709098815917969 nb_pixel_total : 16902 time to create 1 rle with old method : 0.04099297523498535 length of segment : 107 DEBUG bbox = [16, 52, 668, 1128] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.02087259292602539 nb_pixel_total : 480680 time to create 1 rle with new method : 0.03165388107299805 length of segment : 632 DEBUG bbox = [0, 0, 116, 440] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.00042438507080078125 nb_pixel_total : 36642 time to create 1 rle with old method : 0.07892537117004395 length of segment : 133 DEBUG bbox = [0, 390, 54, 550] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.00012087821960449219 nb_pixel_total : 4791 time to create 1 rle with old method : 0.011564970016479492 length of segment : 51 time spent for convertir_results : 0.4288978576660156 time spend for datou_step_exec : 17.81405258178711 time spend to save output : 3.123283386230469e-05 total time spend for step 1 : 17.81408381462097 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 447 chid ids of type : 445 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.015505790710449219 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, 0.9988386, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.9977584, [(711, 22, 22), (925, 22, 48), (608, 23, 146), (893, 23, 104), (598, 24, 234), (849, 24, 159), (589, 25, 428), (582, 26, 444), (574, 27, 459), (569, 28, 466), (565, 29, 472), (560, 30, 480), (555, 31, 487), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (490, 46, 586), (488, 47, 590), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (414, 103, 676), (410, 104, 680), (406, 105, 684), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (369, 113, 721), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (349, 120, 741), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (286, 134, 803), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (233, 148, 856), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 876), (207, 153, 882), (200, 154, 889), (193, 155, 896), (187, 156, 902), (183, 157, 906), (181, 158, 908), (178, 159, 911), (176, 160, 913), (174, 161, 915), (172, 162, 917), (170, 163, 919), (168, 164, 921), (167, 165, 922), (165, 166, 924), (164, 167, 925), (162, 168, 927), (161, 169, 928), (159, 170, 930), (157, 171, 932), (155, 172, 934), (153, 173, 935), (151, 174, 937), (148, 175, 940), (146, 176, 942), (144, 177, 944), (142, 178, 946), (140, 179, 948), (139, 180, 949), (137, 181, 951), (136, 182, 952), (134, 183, 954), (133, 184, 955), (132, 185, 956), (131, 186, 957), (130, 187, 958), (129, 188, 959), (128, 189, 960), (127, 190, 960), (126, 191, 961), (126, 192, 961), (125, 193, 962), (124, 194, 963), (123, 195, 964), (122, 196, 965), (122, 197, 965), (121, 198, 966), (120, 199, 967), (119, 200, 968), (118, 201, 969), (117, 202, 970), (116, 203, 971), (114, 204, 973), (113, 205, 973), (112, 206, 974), (111, 207, 975), (109, 208, 977), (108, 209, 978), (107, 210, 979), (106, 211, 980), (105, 212, 981), (104, 213, 982), (103, 214, 983), (102, 215, 984), (101, 216, 985), (101, 217, 984), (100, 218, 985), (99, 219, 986), (99, 220, 986), (98, 221, 987), (98, 222, 987), (97, 223, 988), (97, 224, 987), (96, 225, 988), (96, 226, 988), (95, 227, 989), (95, 228, 989), (94, 229, 990), (94, 230, 990), (94, 231, 990), (93, 232, 990), (93, 233, 990), (92, 234, 991), (92, 235, 991), (92, 236, 991), (91, 237, 992), (91, 238, 991), (91, 239, 991), (91, 240, 991), (91, 241, 990), (90, 242, 991), (90, 243, 990), (90, 244, 990), (90, 245, 989), (90, 246, 989), (89, 247, 990), (89, 248, 989), (89, 249, 989), (89, 250, 988), (89, 251, 988), (88, 252, 988), (88, 253, 988), (88, 254, 987), (88, 255, 986), (88, 256, 986), (87, 257, 986), (87, 258, 985), (87, 259, 985), (87, 260, 984), (87, 261, 983), (86, 262, 983), (86, 263, 983), (86, 264, 982), (86, 265, 981), (85, 266, 981), (85, 267, 980), (85, 268, 980), (84, 269, 980), (84, 270, 979), (84, 271, 979), (84, 272, 978), (83, 273, 979), (83, 274, 978), (83, 275, 978), (82, 276, 978), (82, 277, 977), (82, 278, 977), (81, 279, 978), (81, 280, 977), (81, 281, 977), (80, 282, 977), (80, 283, 977), (80, 284, 976), (79, 285, 977), (79, 286, 976), (79, 287, 976), (78, 288, 976), (78, 289, 976), (78, 290, 975), (77, 291, 976), (77, 292, 975), (77, 293, 975), (76, 294, 975), (76, 295, 975), (76, 296, 974), (75, 297, 975), (75, 298, 974), (74, 299, 975), (74, 300, 974), (74, 301, 974), (73, 302, 974), (73, 303, 974), (72, 304, 974), (72, 305, 974), (71, 306, 974), (71, 307, 973), (71, 308, 972), (70, 309, 972), (70, 310, 971), (70, 311, 970), (70, 312, 968), (69, 313, 968), (69, 314, 966), (69, 315, 965), (69, 316, 963), (68, 317, 961), (68, 318, 960), (68, 319, 958), (68, 320, 956), (67, 321, 955), (67, 322, 954), (67, 323, 952), (67, 324, 951), (66, 325, 951), (66, 326, 950), (66, 327, 949), (66, 328, 947), (65, 329, 947), (65, 330, 947), (65, 331, 946), (65, 332, 945), (65, 333, 944), (65, 334, 943), (65, 335, 941), (65, 336, 940), (65, 337, 939), (65, 338, 938), (64, 339, 937), (64, 340, 936), (64, 341, 934), (64, 342, 932), (64, 343, 931), (64, 344, 929), (64, 345, 927), (64, 346, 925), (64, 347, 923), (64, 348, 922), (64, 349, 920), (64, 350, 919), (63, 351, 919), (63, 352, 918), (63, 353, 917), (63, 354, 916), (63, 355, 915), (63, 356, 914), (63, 357, 913), (63, 358, 911), (63, 359, 910), (63, 360, 909), (63, 361, 908), (63, 362, 907), (63, 363, 905), (63, 364, 904), (63, 365, 902), (63, 366, 901), (63, 367, 899), (63, 368, 898), (63, 369, 896), (62, 370, 895), (62, 371, 893), (62, 372, 892), (62, 373, 890), (62, 374, 889), (62, 375, 887), (62, 376, 886), (62, 377, 885), (62, 378, 884), (62, 379, 883), (63, 380, 881), (63, 381, 880), (63, 382, 878), (63, 383, 877), (63, 384, 876), (63, 385, 875), (63, 386, 874), (63, 387, 873), (63, 388, 872), (64, 389, 870), (64, 390, 869), (64, 391, 868), (64, 392, 867), (64, 393, 866), (64, 394, 864), (64, 395, 863), (65, 396, 861), (65, 397, 860), (65, 398, 859), (65, 399, 858), (65, 400, 857), (65, 401, 856), (65, 402, 854), (65, 403, 853), (65, 404, 851), (65, 405, 850), (65, 406, 848), (66, 407, 846), (66, 408, 845), (66, 409, 843), (66, 410, 842), (66, 411, 841), (66, 412, 840), (66, 413, 839), (66, 414, 837), (66, 415, 836), (66, 416, 835), (66, 417, 835), (66, 418, 834), (66, 419, 833), (67, 420, 831), (67, 421, 830), (67, 422, 829), (67, 423, 829), (67, 424, 828), (67, 425, 827), (67, 426, 826), (67, 427, 825), (67, 428, 824), (68, 429, 822), (68, 430, 820), (68, 431, 819), (68, 432, 818), (68, 433, 816), (68, 434, 815), (68, 435, 813), (68, 436, 811), (69, 437, 809), (69, 438, 807), (69, 439, 805), (69, 440, 804), (69, 441, 803), (69, 442, 802), (69, 443, 800), (70, 444, 798), (70, 445, 797), (70, 446, 796), (70, 447, 796), (71, 448, 794), (71, 449, 794), (72, 450, 792), (72, 451, 791), (73, 452, 790), (73, 453, 789), (74, 454, 788), (74, 455, 787), (75, 456, 786), (75, 457, 785), (76, 458, 784), (76, 459, 783), (77, 460, 782), (77, 461, 781), (77, 462, 780), (78, 463, 779), (78, 464, 778), (79, 465, 777), (79, 466, 776), (79, 467, 776), (80, 468, 774), (80, 469, 774), (80, 470, 773), (81, 471, 772), (81, 472, 771), (82, 473, 769), (82, 474, 769), (83, 475, 767), (83, 476, 766), (83, 477, 765), (84, 478, 763), (84, 479, 762), (85, 480, 760), (85, 481, 759), (85, 482, 758), (86, 483, 756), (86, 484, 755), (87, 485, 752), (87, 486, 751), (87, 487, 750), (88, 488, 747), (88, 489, 746), (88, 490, 745), (89, 491, 743), (89, 492, 742), (90, 493, 741), (90, 494, 740), (91, 495, 738), (91, 496, 738), (92, 497, 736), (93, 498, 734), (94, 499, 733), (94, 500, 732), (95, 501, 730), (96, 502, 729), (97, 503, 727), (98, 504, 726), (99, 505, 724), (99, 506, 723), (100, 507, 722), (101, 508, 720), (102, 509, 719), (104, 510, 716), (105, 511, 715), (106, 512, 713), (107, 513, 712), (108, 514, 710), (110, 515, 708), (111, 516, 706), (113, 517, 704), (114, 518, 702), (115, 519, 701), (117, 520, 698), (118, 521, 696), (119, 522, 695), (121, 523, 692), (122, 524, 691), (124, 525, 688), (125, 526, 686), (126, 527, 685), (128, 528, 682), (129, 529, 680), (131, 530, 677), (132, 531, 675), (134, 532, 673), (135, 533, 671), (137, 534, 668), (138, 535, 666), (140, 536, 663), (141, 537, 661), (143, 538, 658), (144, 539, 656), (146, 540, 653), (148, 541, 650), (149, 542, 647), (151, 543, 645), (153, 544, 642), (154, 545, 640), (156, 546, 637), (158, 547, 634), (159, 548, 632), (161, 549, 629), (162, 550, 628), (164, 551, 625), (166, 552, 622), (167, 553, 621), (169, 554, 618), (170, 555, 616), (171, 556, 615), (173, 557, 612), (174, 558, 611), (176, 559, 608), (177, 560, 607), (179, 561, 604), (180, 562, 603), (181, 563, 601), (183, 564, 599), (185, 565, 596), (186, 566, 595), (189, 567, 592), (192, 568, 588), (195, 569, 585), (198, 570, 582), (201, 571, 578), (204, 572, 575), (206, 573, 573), (209, 574, 569), (212, 575, 566), (215, 576, 562), (218, 577, 559), (221, 578, 555), (223, 579, 553), (226, 580, 550), (228, 581, 547), (230, 582, 545), (232, 583, 542), (234, 584, 540), (235, 585, 538), (237, 586, 536), (238, 587, 534), (240, 588, 531), (242, 589, 528), (243, 590, 526), (245, 591, 522), (247, 592, 519), (249, 593, 516), (251, 594, 512), (253, 595, 509), (256, 596, 504), (258, 597, 500), (261, 598, 496), (263, 599, 492), (267, 600, 487), (271, 601, 482), (274, 602, 477), (278, 603, 472), (281, 604, 468), (284, 605, 464), (287, 606, 460), (290, 607, 456), (292, 608, 453), (295, 609, 449), (298, 610, 445), (300, 611, 442), (303, 612, 437), (305, 613, 434), (308, 614, 430), (310, 615, 426), (312, 616, 423), (315, 617, 418), (317, 618, 414), (320, 619, 410), (322, 620, 406), (325, 621, 400), (327, 622, 396), (330, 623, 390), (333, 624, 384), (335, 625, 379), (338, 626, 374), (341, 627, 369), (345, 628, 362), (349, 629, 356), (353, 630, 350), (357, 631, 344), (360, 632, 340), (364, 633, 334), (368, 634, 328), (373, 635, 320), (378, 636, 313), (384, 637, 304), (389, 638, 295), (395, 639, 283), (401, 640, 270), (408, 641, 256), (416, 642, 240), (432, 643, 217), (449, 644, 192), (465, 645, 169), (480, 646, 148), (495, 647, 126), (512, 648, 103), (526, 649, 81), (569, 650, 3)], ['526,649,416,642,341,627,297,609,263,599,220,577,186,566,144,539,102,509,91,496,70,447,62,379,65,329,86,265,91,237,101,216,134,183,187,156,225,151,262,138,318,130,358,116,413,104,471,61,506,41,608,23,754,24,892,24,925,22,996,23,1032,27,1066,41,1082,52,1089,72,1089,128,1085,216,1082,237,1045,305,1019,322,949,374,889,429,865,446,850,474,824,501,806,532,789,549,772,586,727,620,683,638,606,649']), (917855882, 492601069, 445, 0, 440, 0, 116, 0.9919521, [(127, 1, 141), (94, 2, 206), (384, 2, 2), (59, 3, 273), (340, 3, 57), (22, 4, 381), (19, 5, 387), (16, 6, 392), (15, 7, 394), (14, 8, 396), (14, 9, 397), (13, 10, 399), (12, 11, 400), (12, 12, 400), (11, 13, 402), (10, 14, 403), (11, 15, 403), (11, 16, 404), (12, 17, 403), (12, 18, 404), (12, 19, 405), (12, 20, 405), (12, 21, 406), (12, 22, 406), (12, 23, 407), (12, 24, 407), (12, 25, 408), (12, 26, 408), (12, 27, 408), (12, 28, 408), (12, 29, 409), (12, 30, 409), (12, 31, 409), (12, 32, 409), (12, 33, 409), (12, 34, 410), (12, 35, 410), (12, 36, 410), (12, 37, 410), (12, 38, 410), (12, 39, 410), (12, 40, 410), (12, 41, 411), (12, 42, 411), (12, 43, 411), (12, 44, 411), (12, 45, 411), (12, 46, 410), (12, 47, 410), (12, 48, 410), (12, 49, 410), (12, 50, 410), (12, 51, 410), (12, 52, 409), (12, 53, 408), (12, 54, 408), (12, 55, 407), (12, 56, 406), (12, 57, 404), (12, 58, 403), (11, 59, 403), (11, 60, 402), (11, 61, 401), (11, 62, 400), (11, 63, 400), (11, 64, 399), (11, 65, 398), (11, 66, 397), (11, 67, 397), (11, 68, 396), (11, 69, 395), (11, 70, 395), (11, 71, 394), (11, 72, 394), (11, 73, 394), (11, 74, 393), (11, 75, 393), (11, 76, 393), (11, 77, 393), (11, 78, 393), (11, 79, 393), (11, 80, 392), (10, 81, 394), (10, 82, 394), (10, 83, 395), (9, 84, 396), (9, 85, 262), (279, 85, 126), (9, 86, 75), (98, 86, 28), (142, 86, 117), (292, 86, 112), (9, 87, 71), (152, 87, 103), (294, 87, 110), (8, 88, 68), (161, 88, 91), (296, 88, 107), (8, 89, 63), (176, 89, 73), (297, 89, 106), (7, 90, 61), (205, 90, 40), (298, 90, 104), (7, 91, 57), (299, 91, 103), (6, 92, 54), (300, 92, 102), (6, 93, 50), (301, 93, 100), (7, 94, 46), (303, 94, 97), (7, 95, 44), (306, 95, 92), (7, 96, 42), (308, 96, 89), (7, 97, 40), (310, 97, 86), (7, 98, 38), (312, 98, 83), (8, 99, 34), (314, 99, 79), (8, 100, 32), (317, 100, 75), (8, 101, 29), (319, 101, 71), (13, 102, 19), (324, 102, 63), (20, 103, 6), (330, 103, 51), (337, 104, 37), (344, 105, 22), (352, 106, 3)], ['344,105,319,101,301,93,291,85,259,85,244,90,205,90,204,89,176,89,161,88,141,85,126,85,125,86,98,86,84,85,56,92,36,101,26,102,8,101,6,92,11,80,11,59,12,58,12,17,10,14,16,6,22,4,58,4,59,3,93,3,94,2,126,2,127,1,267,1,268,2,331,3,396,3,407,6,411,10,419,25,421,34,421,51,410,62,404,71,402,80,404,85,401,92,394,98,386,102,365,105']), (917855882, 492601069, 445, 390, 550, 0, 54, 0.93910396, [(415, 0, 5), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (420, 25, 99), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,414,1,415,0,419,0,420,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1772338851_3257054_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 : 0.19005846977233887 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Sun Mar 1 05:21:11 2026 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 : 10996 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-03-01 05:21:14.559904: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-03-01 05:21:14.590505: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-03-01 05:21:14.592612: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9bb4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-03-01 05:21:14.592651: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-03-01 05:21:14.596129: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-03-01 05:21:14.885526: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2ce680c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-03-01 05:21:14.885568: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-03-01 05:21:14.886957: 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 2026-03-01 05:21:14.887340: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:21:14.890152: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:21:14.892330: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:21:14.892693: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:21:14.895128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:21:14.896359: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:21:14.901141: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:21:14.902926: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:21:14.902998: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:21:14.903709: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-03-01 05:21:14.903722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-03-01 05:21:14.903730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-03-01 05:21:14.905016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10191 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. 2026-03-01 05:21:15.013148: 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 2026-03-01 05:21:15.013238: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:21:15.013262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:21:15.013284: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:21:15.013305: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:21:15.013325: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:21:15.013345: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:21:15.013365: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:21:15.014996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:21:15.016152: 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 2026-03-01 05:21:15.016190: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-03-01 05:21:15.016206: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:21:15.016221: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-03-01 05:21:15.016235: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-03-01 05:21:15.016250: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-03-01 05:21:15.016264: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-03-01 05:21:15.016279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-03-01 05:21:15.017472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-03-01 05:21:15.017503: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-03-01 05:21:15.017510: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-03-01 05:21:15.017517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-03-01 05:21:15.018826: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10191 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 : [] file manque in s3 : ['mask_model.h5'] 2026-03-01 05:21:23.913897: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-03-01 05:21:24.084172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin 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 3257789 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5704 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 : 10996 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'] DEBUG bbox = [118, 7, 2241, 2268] DEBUG masks shape = (2448, 2448) time for calcul the mask position with numpy : 0.4940335750579834 nb_pixel_total : 3693186 time to create 1 rle with new method : 0.3415203094482422 length of segment : 2042 time spent for convertir_results : 2.0612680912017822 time spend for datou_step_exec : 20.494678020477295 time spend to save output : 2.47955322265625e-05 total time spend for step 1 : 20.49470281600952 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 726 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 : 0.016940832138061523 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 7, 2268, 118, 2241, 0.98503286, [(675, 120, 111), (520, 121, 481), (1051, 121, 380), (503, 122, 946), (486, 123, 981), (470, 124, 1014), (455, 125, 1046), (442, 126, 1091), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1222), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (363, 135, 1266), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (323, 159, 1389), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (280, 182, 1508), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 1710), (203, 210, 1712), (201, 211, 1716), (199, 212, 1719), (198, 213, 1722), (196, 214, 1725), (195, 215, 1727), (193, 216, 1730), (192, 217, 1733), (191, 218, 1735), (189, 219, 1738), (188, 220, 1740), (187, 221, 1742), (186, 222, 1744), (185, 223, 1746), (183, 224, 1749), (182, 225, 1751), (181, 226, 1753), (180, 227, 1755), (179, 228, 1757), (178, 229, 1759), (177, 230, 1761), (176, 231, 1762), (176, 232, 1763), (175, 233, 1765), (174, 234, 1767), (173, 235, 1768), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1776), (168, 240, 1777), (167, 241, 1779), (166, 242, 1781), (165, 243, 1783), (164, 244, 1785), (163, 245, 1787), (162, 246, 1789), (161, 247, 1791), (159, 248, 1794), (158, 249, 1796), (157, 250, 1798), (156, 251, 1800), (154, 252, 1803), (153, 253, 1805), (152, 254, 1807), (151, 255, 1809), (149, 256, 1812), (148, 257, 1815), (146, 258, 1818), (145, 259, 1820), (143, 260, 1824), (142, 261, 1826), (140, 262, 1829), (138, 263, 1833), (137, 264, 1835), (135, 265, 1839), (133, 266, 1842), (132, 267, 1845), (130, 268, 1849), (128, 269, 1852), (126, 270, 1856), (125, 271, 1859), (124, 272, 1862), (122, 273, 1865), (121, 274, 1868), (120, 275, 1871), (119, 276, 1873), (118, 277, 1876), (116, 278, 1879), (115, 279, 1881), (114, 280, 1884), (113, 281, 1886), (112, 282, 1888), (111, 283, 1890), (110, 284, 1892), (109, 285, 1895), (108, 286, 1897), (108, 287, 1898), (107, 288, 1900), (106, 289, 1902), (105, 290, 1904), (104, 291, 1906), (103, 292, 1908), (103, 293, 1909), (102, 294, 1911), (101, 295, 1912), (101, 296, 1913), (100, 297, 1915), (99, 298, 1917), (99, 299, 1918), (98, 300, 1919), (97, 301, 1921), (97, 302, 1922), (96, 303, 1924), (95, 304, 1925), (95, 305, 1926), (94, 306, 1928), (94, 307, 1928), (93, 308, 1930), (93, 309, 1930), (93, 310, 1931), (93, 311, 1931), (92, 312, 1933), (92, 313, 1933), (92, 314, 1934), (92, 315, 1934), (91, 316, 1936), (91, 317, 1936), (91, 318, 1937), (91, 319, 1937), (90, 320, 1939), (90, 321, 1939), (90, 322, 1940), (89, 323, 1941), (89, 324, 1942), (89, 325, 1943), (89, 326, 1943), (88, 327, 1945), (88, 328, 1945), (88, 329, 1946), (87, 330, 1948), (87, 331, 1948), (87, 332, 1949), (87, 333, 1949), (86, 334, 1951), (86, 335, 1952), (86, 336, 1952), (85, 337, 1954), (85, 338, 1955), (85, 339, 1955), (85, 340, 1956), (84, 341, 1958), (84, 342, 1959), (84, 343, 1959), (83, 344, 1961), (83, 345, 1962), (83, 346, 1963), (83, 347, 1963), (82, 348, 1965), (82, 349, 1966), (82, 350, 1967), (81, 351, 1969), (81, 352, 1970), (81, 353, 1970), (80, 354, 1972), (80, 355, 1973), (80, 356, 1974), (80, 357, 1975), (79, 358, 1977), (79, 359, 1978), (79, 360, 1979), (78, 361, 1981), (78, 362, 1982), (78, 363, 1983), (77, 364, 1985), (77, 365, 1986), (77, 366, 1987), (77, 367, 1988), (76, 368, 1990), (76, 369, 1991), (76, 370, 1992), (75, 371, 1994), (75, 372, 1995), (75, 373, 1996), (74, 374, 1998), (74, 375, 1999), (74, 376, 2000), (73, 377, 2002), (73, 378, 2003), (73, 379, 2004), (72, 380, 2005), (72, 381, 2006), (72, 382, 2007), (71, 383, 2009), (71, 384, 2009), (71, 385, 2010), (70, 386, 2012), (70, 387, 2012), (70, 388, 2013), (70, 389, 2013), (69, 390, 2015), (69, 391, 2015), (69, 392, 2016), (68, 393, 2017), (68, 394, 2018), (68, 395, 2019), (67, 396, 2020), (67, 397, 2021), (67, 398, 2021), (66, 399, 2023), (66, 400, 2023), (65, 401, 2025), (65, 402, 2025), (65, 403, 2026), (64, 404, 2027), (64, 405, 2028), (64, 406, 2028), (63, 407, 2030), (63, 408, 2030), (63, 409, 2031), (62, 410, 2032), (62, 411, 2033), (61, 412, 2034), (61, 413, 2034), (61, 414, 2035), (60, 415, 2036), (60, 416, 2037), (59, 417, 2038), (59, 418, 2039), (58, 419, 2040), (58, 420, 2041), (58, 421, 2041), (57, 422, 2042), (57, 423, 2043), (56, 424, 2044), (56, 425, 2045), (55, 426, 2046), (55, 427, 2047), (54, 428, 2048), (54, 429, 2048), (53, 430, 2050), (53, 431, 2050), (52, 432, 2052), (52, 433, 2052), (51, 434, 2053), (51, 435, 2054), (50, 436, 2055), (50, 437, 2055), (49, 438, 2057), (49, 439, 2057), (48, 440, 2059), (48, 441, 2059), (47, 442, 2060), (47, 443, 2061), (46, 444, 2062), (46, 445, 2062), (45, 446, 2064), (45, 447, 2064), (44, 448, 2065), (44, 449, 2066), (43, 450, 2067), (43, 451, 2068), (42, 452, 2069), (42, 453, 2069), (41, 454, 2071), (41, 455, 2071), (40, 456, 2072), (40, 457, 2073), (39, 458, 2074), (39, 459, 2074), (39, 460, 2074), (39, 461, 2075), (39, 462, 2075), (38, 463, 2076), (38, 464, 2077), (38, 465, 2077), (38, 466, 2077), (38, 467, 2078), (37, 468, 2079), (37, 469, 2079), (37, 470, 2080), (37, 471, 2080), (37, 472, 2080), (37, 473, 2081), (37, 474, 2081), (36, 475, 2082), (36, 476, 2083), (36, 477, 2083), (36, 478, 2083), (36, 479, 2084), (36, 480, 2084), (35, 481, 2085), (35, 482, 2086), (35, 483, 2086), (35, 484, 2086), (35, 485, 2087), (35, 486, 2087), (34, 487, 2089), (34, 488, 2089), (34, 489, 2089), (34, 490, 2090), (34, 491, 2090), (34, 492, 2091), (34, 493, 2091), (33, 494, 2093), (33, 495, 2093), (33, 496, 2093), (33, 497, 2094), (33, 498, 2094), (33, 499, 2095), (33, 500, 2095), (32, 501, 2097), (32, 502, 2097), (32, 503, 2098), (32, 504, 2098), (32, 505, 2099), (32, 506, 2099), (32, 507, 2100), (31, 508, 2101), (31, 509, 2102), (31, 510, 2102), (31, 511, 2103), (31, 512, 2103), (31, 513, 2104), (31, 514, 2104), (30, 515, 2106), (30, 516, 2106), (30, 517, 2107), (30, 518, 2108), (30, 519, 2108), (30, 520, 2109), (30, 521, 2109), (30, 522, 2110), (29, 523, 2112), (29, 524, 2112), (29, 525, 2113), (29, 526, 2114), (29, 527, 2114), (29, 528, 2115), (29, 529, 2116), (29, 530, 2116), (28, 531, 2118), (28, 532, 2119), (28, 533, 2119), (28, 534, 2120), (28, 535, 2121), (28, 536, 2121), (28, 537, 2121), (28, 538, 2122), (28, 539, 2122), (28, 540, 2122), (28, 541, 2123), (28, 542, 2123), (27, 543, 2124), (27, 544, 2125), (27, 545, 2125), (27, 546, 2125), (27, 547, 2126), (27, 548, 2126), (27, 549, 2126), (27, 550, 2127), (27, 551, 2127), (27, 552, 2127), (27, 553, 2127), (27, 554, 2128), (27, 555, 2128), (27, 556, 2128), (27, 557, 2129), (27, 558, 2129), (27, 559, 2129), (27, 560, 2129), (26, 561, 2131), (26, 562, 2131), (26, 563, 2131), (26, 564, 2132), (26, 565, 2132), (26, 566, 2132), (26, 567, 2132), (26, 568, 2133), (26, 569, 2133), (26, 570, 2133), (26, 571, 2133), (26, 572, 2134), (26, 573, 2134), (26, 574, 2134), (26, 575, 2134), (26, 576, 2135), (26, 577, 2135), (26, 578, 2135), (25, 579, 2136), (25, 580, 2137), (25, 581, 2137), (25, 582, 2137), (25, 583, 2137), (25, 584, 2138), (25, 585, 2138), (25, 586, 2138), (25, 587, 2138), (25, 588, 2139), (25, 589, 2139), (25, 590, 2139), (25, 591, 2139), (25, 592, 2139), (25, 593, 2140), (25, 594, 2140), (25, 595, 2140), (25, 596, 2140), (25, 597, 2141), (25, 598, 2141), (24, 599, 2142), (24, 600, 2142), (24, 601, 2142), (24, 602, 2143), (24, 603, 2143), (24, 604, 2143), (24, 605, 2143), (24, 606, 2143), (24, 607, 2144), (24, 608, 2144), (24, 609, 2144), (24, 610, 2144), (24, 611, 2144), (24, 612, 2144), (24, 613, 2144), (24, 614, 2145), (24, 615, 2145), (24, 616, 2145), (24, 617, 2145), (24, 618, 2145), (24, 619, 2145), (23, 620, 2146), (23, 621, 2146), (23, 622, 2146), (23, 623, 2146), (23, 624, 2146), (23, 625, 2146), (23, 626, 2146), (23, 627, 2146), (23, 628, 2146), (23, 629, 2146), (23, 630, 2147), (23, 631, 2147), (23, 632, 2147), (23, 633, 2147), (23, 634, 2147), (23, 635, 2147), (23, 636, 2147), (23, 637, 2147), (23, 638, 2147), (23, 639, 2147), (23, 640, 2147), (23, 641, 2147), (23, 642, 2147), (22, 643, 2148), (22, 644, 2148), (22, 645, 2148), (22, 646, 2149), (22, 647, 2149), (22, 648, 2149), (22, 649, 2149), (22, 650, 2149), (22, 651, 2149), (22, 652, 2149), (22, 653, 2149), (22, 654, 2149), (22, 655, 2149), (22, 656, 2149), (22, 657, 2149), (22, 658, 2149), (22, 659, 2149), (22, 660, 2149), (22, 661, 2149), (22, 662, 2149), (22, 663, 2150), (22, 664, 2150), (22, 665, 2150), (22, 666, 2150), (22, 667, 2150), (21, 668, 2151), (21, 669, 2151), (21, 670, 2151), (21, 671, 2151), (21, 672, 2151), (21, 673, 2151), (21, 674, 2151), (21, 675, 2151), (21, 676, 2151), (21, 677, 2151), (21, 678, 2151), (21, 679, 2151), (21, 680, 2152), (21, 681, 2152), (21, 682, 2152), (21, 683, 2152), (21, 684, 2152), (21, 685, 2152), (21, 686, 2152), (21, 687, 2152), (21, 688, 2152), (21, 689, 2152), (21, 690, 2152), (21, 691, 2152), (21, 692, 2152), (21, 693, 2152), (21, 694, 2152), (21, 695, 2151), (22, 696, 2150), (22, 697, 2150), (22, 698, 2150), (22, 699, 2150), (22, 700, 2150), (22, 701, 2150), (22, 702, 2150), (22, 703, 2150), (22, 704, 2150), (22, 705, 2150), (22, 706, 2150), (22, 707, 2150), (22, 708, 2150), (22, 709, 2150), (23, 710, 2149), (23, 711, 2149), (23, 712, 2149), (23, 713, 2149), (23, 714, 2149), (23, 715, 2149), (23, 716, 2149), (23, 717, 2149), (23, 718, 2148), (23, 719, 2148), (23, 720, 2148), (23, 721, 2148), (24, 722, 2147), (24, 723, 2147), (24, 724, 2147), (24, 725, 2147), (24, 726, 2147), (24, 727, 2147), (24, 728, 2147), (24, 729, 2147), (24, 730, 2147), (24, 731, 2147), (24, 732, 2147), (24, 733, 2147), (25, 734, 2146), (25, 735, 2146), (25, 736, 2146), (25, 737, 2146), (25, 738, 2146), (25, 739, 2145), (25, 740, 2145), (25, 741, 2145), (25, 742, 2145), (25, 743, 2145), (25, 744, 2145), (25, 745, 2145), (26, 746, 2144), (26, 747, 2144), (26, 748, 2144), (26, 749, 2144), (26, 750, 2144), (26, 751, 2144), (26, 752, 2144), (26, 753, 2144), (26, 754, 2144), (26, 755, 2144), (26, 756, 2144), (27, 757, 2143), (27, 758, 2143), (27, 759, 2143), (27, 760, 2142), (27, 761, 2142), (27, 762, 2142), (27, 763, 2142), (27, 764, 2142), (27, 765, 2142), (27, 766, 2142), (27, 767, 2142), (27, 768, 2142), (27, 769, 2142), (27, 770, 2142), (27, 771, 2142), (27, 772, 2142), (27, 773, 2142), (27, 774, 2142), (27, 775, 2142), (27, 776, 2142), (27, 777, 2142), (27, 778, 2142), (27, 779, 2142), (27, 780, 2141), (27, 781, 2141), (27, 782, 2141), (27, 783, 2141), (27, 784, 2141), (27, 785, 2141), (27, 786, 2141), (27, 787, 2141), (27, 788, 2141), (27, 789, 2141), (26, 790, 2142), (26, 791, 2142), (26, 792, 2142), (26, 793, 2142), (26, 794, 2142), (26, 795, 2142), (26, 796, 2142), (26, 797, 2142), (26, 798, 2141), (26, 799, 2141), (26, 800, 2141), (26, 801, 2141), (26, 802, 2141), (26, 803, 2141), (26, 804, 2141), (26, 805, 2141), (26, 806, 2141), (26, 807, 2141), (26, 808, 2141), (26, 809, 2141), (26, 810, 2141), (26, 811, 2141), (26, 812, 2141), (26, 813, 2141), (26, 814, 2141), (26, 815, 2141), (26, 816, 2140), (26, 817, 2140), (26, 818, 2140), (26, 819, 2140), (26, 820, 2140), (26, 821, 2140), (26, 822, 2140), (26, 823, 2140), (26, 824, 2140), (26, 825, 2140), (26, 826, 2140), (26, 827, 2140), (26, 828, 2140), (26, 829, 2140), (26, 830, 2140), (26, 831, 2140), (26, 832, 2140), (26, 833, 2140), (26, 834, 2139), (26, 835, 2139), (26, 836, 2139), (26, 837, 2139), (26, 838, 2139), (26, 839, 2139), (26, 840, 2138), (26, 841, 2138), (26, 842, 2138), (26, 843, 2137), (26, 844, 2137), (26, 845, 2137), (26, 846, 2136), (26, 847, 2136), (26, 848, 2135), (26, 849, 2135), (26, 850, 2135), (26, 851, 2134), (26, 852, 2134), (26, 853, 2133), (27, 854, 2132), (27, 855, 2131), (27, 856, 2131), (27, 857, 2130), (27, 858, 2130), (27, 859, 2130), (27, 860, 2129), (27, 861, 2129), (27, 862, 2128), (27, 863, 2128), (27, 864, 2127), (27, 865, 2127), (27, 866, 2126), (27, 867, 2126), (27, 868, 2125), (27, 869, 2125), (27, 870, 2124), (27, 871, 2123), (27, 872, 2123), (28, 873, 2121), (28, 874, 2121), (28, 875, 2120), (28, 876, 2120), (28, 877, 2119), (28, 878, 2118), (28, 879, 2118), (28, 880, 2117), (28, 881, 2117), (28, 882, 2116), (28, 883, 2116), (28, 884, 2115), (28, 885, 2114), (28, 886, 2114), (28, 887, 2113), (28, 888, 2113), (28, 889, 2112), (28, 890, 2112), (28, 891, 2111), (29, 892, 2110), (29, 893, 2109), (29, 894, 2109), (29, 895, 2108), (29, 896, 2108), (29, 897, 2107), (29, 898, 2107), (29, 899, 2107), (29, 900, 2106), (29, 901, 2106), (29, 902, 2105), (29, 903, 2105), (29, 904, 2104), (29, 905, 2104), (29, 906, 2104), (29, 907, 2103), (29, 908, 2103), (29, 909, 2102), (30, 910, 2101), (30, 911, 2101), (30, 912, 2100), (30, 913, 2100), (30, 914, 2099), (30, 915, 2099), (30, 916, 2099), (30, 917, 2099), (30, 918, 2098), (30, 919, 2098), (29, 920, 2099), (29, 921, 2099), (29, 922, 2098), (29, 923, 2098), (29, 924, 2098), (29, 925, 2098), (29, 926, 2097), (29, 927, 2097), (29, 928, 2097), (29, 929, 2097), (29, 930, 2097), (29, 931, 2096), (29, 932, 2096), (29, 933, 2096), (29, 934, 2096), (29, 935, 2095), (29, 936, 2095), (29, 937, 2095), (29, 938, 2095), (29, 939, 2094), (29, 940, 2094), (29, 941, 2094), (29, 942, 2094), (29, 943, 2094), (29, 944, 2093), (29, 945, 2093), (29, 946, 2093), (29, 947, 2093), (29, 948, 2093), (28, 949, 2093), (28, 950, 2093), (28, 951, 2093), (28, 952, 2093), (28, 953, 2093), (28, 954, 2092), (28, 955, 2092), (28, 956, 2092), (28, 957, 2092), (28, 958, 2092), (28, 959, 2091), (28, 960, 2091), (28, 961, 2091), (28, 962, 2091), (28, 963, 2091), (28, 964, 2090), (28, 965, 2090), (28, 966, 2090), (28, 967, 2090), (28, 968, 2090), (28, 969, 2089), (28, 970, 2089), (28, 971, 2089), (28, 972, 2089), (28, 973, 2089), (28, 974, 2089), (28, 975, 2088), (28, 976, 2088), (28, 977, 2088), (28, 978, 2088), (27, 979, 2089), (27, 980, 2089), (27, 981, 2088), (27, 982, 2088), (27, 983, 2088), (27, 984, 2088), (27, 985, 2088), (27, 986, 2088), (27, 987, 2087), (27, 988, 2087), (27, 989, 2087), (27, 990, 2087), (27, 991, 2086), (27, 992, 2086), (27, 993, 2086), (27, 994, 2086), (27, 995, 2085), (27, 996, 2085), (27, 997, 2085), (27, 998, 2084), (27, 999, 2084), (27, 1000, 2084), (28, 1001, 2082), (28, 1002, 2082), (28, 1003, 2082), (28, 1004, 2081), (28, 1005, 2081), (28, 1006, 2081), (28, 1007, 2080), (28, 1008, 2080), (28, 1009, 2080), (28, 1010, 2079), (28, 1011, 2079), (28, 1012, 2079), (28, 1013, 2078), (28, 1014, 2078), (28, 1015, 2077), (28, 1016, 2077), (28, 1017, 2077), (28, 1018, 2076), (28, 1019, 2076), (28, 1020, 2076), (28, 1021, 2075), (28, 1022, 2075), (28, 1023, 2074), (28, 1024, 2074), (28, 1025, 2074), (28, 1026, 2073), (28, 1027, 2073), (28, 1028, 2073), (28, 1029, 2072), (29, 1030, 2071), (29, 1031, 2070), (29, 1032, 2070), (29, 1033, 2069), (29, 1034, 2069), (29, 1035, 2069), (29, 1036, 2068), (29, 1037, 2068), (29, 1038, 2067), (29, 1039, 2067), (29, 1040, 2067), (29, 1041, 2066), (29, 1042, 2066), (29, 1043, 2065), (29, 1044, 2065), (29, 1045, 2064), (29, 1046, 2064), (29, 1047, 2063), (29, 1048, 2063), (29, 1049, 2063), (29, 1050, 2062), (29, 1051, 2062), (29, 1052, 2061), (29, 1053, 2061), (29, 1054, 2060), (29, 1055, 2060), (29, 1056, 2059), (29, 1057, 2059), (29, 1058, 2058), (30, 1059, 2057), (30, 1060, 2056), (30, 1061, 2056), (30, 1062, 2055), (30, 1063, 2055), (30, 1064, 2054), (30, 1065, 2054), (30, 1066, 2053), (30, 1067, 2052), (30, 1068, 2052), (30, 1069, 2051), (30, 1070, 2050), (30, 1071, 2049), (30, 1072, 2049), (30, 1073, 2048), (30, 1074, 2047), (30, 1075, 2046), (30, 1076, 2045), (30, 1077, 2044), (30, 1078, 2043), (30, 1079, 2042), (29, 1080, 2042), (29, 1081, 2041), (29, 1082, 2040), (29, 1083, 2039), (29, 1084, 2038), (29, 1085, 2037), (29, 1086, 2036), (29, 1087, 2035), (29, 1088, 2034), (29, 1089, 2033), (29, 1090, 2032), (29, 1091, 2031), (29, 1092, 2030), (29, 1093, 2029), (29, 1094, 2028), (29, 1095, 2027), (29, 1096, 2026), (29, 1097, 2026), (29, 1098, 2025), (29, 1099, 2024), (29, 1100, 2023), (29, 1101, 2022), (29, 1102, 2022), (29, 1103, 2021), (29, 1104, 2020), (29, 1105, 2019), (29, 1106, 2019), (29, 1107, 2018), (29, 1108, 2017), (29, 1109, 2016), (29, 1110, 2016), (29, 1111, 2015), (29, 1112, 2014), (29, 1113, 2014), (29, 1114, 2013), (29, 1115, 2013), (29, 1116, 2012), (29, 1117, 2011), (29, 1118, 2011), (29, 1119, 2010), (29, 1120, 2010), (29, 1121, 2009), (29, 1122, 2009), (29, 1123, 2008), (29, 1124, 2007), (29, 1125, 2007), (29, 1126, 2006), (29, 1127, 2006), (29, 1128, 2005), (29, 1129, 2005), (29, 1130, 2004), (29, 1131, 2004), (29, 1132, 2003), (29, 1133, 2003), (29, 1134, 2003), (28, 1135, 2003), (28, 1136, 2003), (28, 1137, 2002), (28, 1138, 2002), (28, 1139, 2001), (28, 1140, 2001), (28, 1141, 2001), (28, 1142, 2000), (28, 1143, 2000), (28, 1144, 2000), (28, 1145, 1999), (28, 1146, 1999), (28, 1147, 1999), (28, 1148, 1998), (28, 1149, 1998), (28, 1150, 1998), (29, 1151, 1996), (29, 1152, 1996), (29, 1153, 1996), (29, 1154, 1995), (29, 1155, 1995), (29, 1156, 1995), (29, 1157, 1994), (29, 1158, 1994), (29, 1159, 1994), (29, 1160, 1993), (29, 1161, 1993), (29, 1162, 1992), (29, 1163, 1992), (29, 1164, 1992), (29, 1165, 1991), (29, 1166, 1991), (29, 1167, 1991), (29, 1168, 1990), (29, 1169, 1990), (29, 1170, 1989), (29, 1171, 1989), (29, 1172, 1989), (29, 1173, 1988), (29, 1174, 1988), (29, 1175, 1987), (29, 1176, 1987), (29, 1177, 1987), (29, 1178, 1986), (29, 1179, 1986), (29, 1180, 1985), (29, 1181, 1985), (29, 1182, 1985), (29, 1183, 1984), (29, 1184, 1984), (29, 1185, 1983), (29, 1186, 1983), (29, 1187, 1982), (29, 1188, 1982), (29, 1189, 1981), (29, 1190, 1981), (29, 1191, 1981), (29, 1192, 1980), (29, 1193, 1980), (29, 1194, 1979), (29, 1195, 1979), (29, 1196, 1978), (29, 1197, 1978), (29, 1198, 1977), (29, 1199, 1977), (29, 1200, 1976), (29, 1201, 1976), (29, 1202, 1975), (29, 1203, 1975), (29, 1204, 1974), (29, 1205, 1974), (29, 1206, 1973), (29, 1207, 1972), (29, 1208, 1972), (29, 1209, 1971), (29, 1210, 1971), (29, 1211, 1970), (29, 1212, 1970), (29, 1213, 1969), (29, 1214, 1969), (29, 1215, 1968), (29, 1216, 1967), (29, 1217, 1967), (29, 1218, 1966), (29, 1219, 1965), (29, 1220, 1965), (29, 1221, 1964), (29, 1222, 1963), (29, 1223, 1963), (29, 1224, 1962), (29, 1225, 1961), (29, 1226, 1960), (29, 1227, 1960), (29, 1228, 1959), (29, 1229, 1958), (29, 1230, 1957), (29, 1231, 1956), (29, 1232, 1955), (29, 1233, 1955), (29, 1234, 1954), (29, 1235, 1953), (29, 1236, 1952), (29, 1237, 1951), (29, 1238, 1951), (29, 1239, 1950), (29, 1240, 1949), (30, 1241, 1947), (30, 1242, 1947), (30, 1243, 1946), (30, 1244, 1945), (30, 1245, 1945), (30, 1246, 1944), (30, 1247, 1943), (30, 1248, 1943), (30, 1249, 1942), (30, 1250, 1941), (30, 1251, 1941), (30, 1252, 1940), (30, 1253, 1940), (30, 1254, 1939), (30, 1255, 1938), (30, 1256, 1938), (30, 1257, 1937), (30, 1258, 1937), (30, 1259, 1936), (30, 1260, 1936), (30, 1261, 1935), (30, 1262, 1935), (30, 1263, 1934), (30, 1264, 1934), (30, 1265, 1933), (30, 1266, 1933), (30, 1267, 1932), (30, 1268, 1932), (30, 1269, 1931), (30, 1270, 1931), (30, 1271, 1930), (30, 1272, 1930), (30, 1273, 1929), (30, 1274, 1929), (30, 1275, 1929), (30, 1276, 1928), (30, 1277, 1928), (30, 1278, 1927), (30, 1279, 1927), (30, 1280, 1927), (30, 1281, 1926), (30, 1282, 1926), (30, 1283, 1925), (30, 1284, 1925), (30, 1285, 1925), (30, 1286, 1924), (30, 1287, 1924), (30, 1288, 1924), (30, 1289, 1923), (30, 1290, 1923), (30, 1291, 1923), (30, 1292, 1922), (30, 1293, 1922), (30, 1294, 1922), (30, 1295, 1921), (30, 1296, 1921), (30, 1297, 1921), (30, 1298, 1921), (30, 1299, 1920), (30, 1300, 1920), (30, 1301, 1920), (30, 1302, 1920), (30, 1303, 1920), (30, 1304, 1919), (30, 1305, 1919), (30, 1306, 1919), (30, 1307, 1919), (30, 1308, 1918), (30, 1309, 1918), (30, 1310, 1918), (30, 1311, 1918), (31, 1312, 1916), (31, 1313, 1916), (31, 1314, 1916), (31, 1315, 1916), (31, 1316, 1915), (31, 1317, 1915), (31, 1318, 1915), (31, 1319, 1915), (31, 1320, 1914), (31, 1321, 1914), (31, 1322, 1914), (31, 1323, 1914), (31, 1324, 1913), (31, 1325, 1913), (31, 1326, 1913), (31, 1327, 1912), (31, 1328, 1912), (31, 1329, 1912), (31, 1330, 1912), (31, 1331, 1911), (31, 1332, 1911), (31, 1333, 1911), (31, 1334, 1911), (31, 1335, 1910), (31, 1336, 1910), (31, 1337, 1910), (31, 1338, 1909), (31, 1339, 1909), (32, 1340, 1908), (32, 1341, 1908), (32, 1342, 1907), (32, 1343, 1907), (32, 1344, 1907), (32, 1345, 1907), (32, 1346, 1906), (32, 1347, 1906), (32, 1348, 1906), (32, 1349, 1905), (32, 1350, 1905), (32, 1351, 1905), (32, 1352, 1904), (32, 1353, 1904), (32, 1354, 1904), (32, 1355, 1904), (32, 1356, 1903), (32, 1357, 1903), (32, 1358, 1903), (32, 1359, 1902), (32, 1360, 1902), (32, 1361, 1902), (32, 1362, 1901), (32, 1363, 1901), (32, 1364, 1901), (32, 1365, 1900), (33, 1366, 1899), (33, 1367, 1899), (33, 1368, 1899), (33, 1369, 1898), (33, 1370, 1898), (33, 1371, 1897), (33, 1372, 1897), (33, 1373, 1896), (33, 1374, 1896), (33, 1375, 1895), (33, 1376, 1895), (33, 1377, 1894), (33, 1378, 1894), (33, 1379, 1893), (33, 1380, 1892), (33, 1381, 1892), (34, 1382, 1890), (34, 1383, 1890), (34, 1384, 1889), (34, 1385, 1888), (34, 1386, 1888), (34, 1387, 1887), (34, 1388, 1886), (34, 1389, 1886), (34, 1390, 1885), (34, 1391, 1884), (34, 1392, 1884), (34, 1393, 1883), (34, 1394, 1882), (34, 1395, 1882), (35, 1396, 1880), (35, 1397, 1879), (35, 1398, 1878), (35, 1399, 1878), (35, 1400, 1877), (35, 1401, 1876), (35, 1402, 1875), (35, 1403, 1874), (35, 1404, 1873), (35, 1405, 1872), (35, 1406, 1872), (35, 1407, 1871), (35, 1408, 1870), (36, 1409, 1868), (36, 1410, 1867), (36, 1411, 1866), (36, 1412, 1865), (36, 1413, 1864), (36, 1414, 1863), (36, 1415, 1863), (36, 1416, 1862), (36, 1417, 1861), (36, 1418, 1860), (36, 1419, 1859), (36, 1420, 1859), (36, 1421, 1858), (37, 1422, 1856), (37, 1423, 1855), (37, 1424, 1855), (37, 1425, 1854), (37, 1426, 1853), (37, 1427, 1853), (37, 1428, 1852), (37, 1429, 1851), (37, 1430, 1851), (37, 1431, 1850), (37, 1432, 1850), (37, 1433, 1849), (37, 1434, 1848), (38, 1435, 1847), (38, 1436, 1846), (38, 1437, 1846), (38, 1438, 1845), (38, 1439, 1845), (38, 1440, 1844), (38, 1441, 1844), (38, 1442, 1843), (38, 1443, 1843), (38, 1444, 1842), (38, 1445, 1842), (38, 1446, 1842), (38, 1447, 1841), (38, 1448, 1841), (38, 1449, 1841), (38, 1450, 1841), (38, 1451, 1840), (39, 1452, 1839), (39, 1453, 1839), (39, 1454, 1839), (39, 1455, 1839), (39, 1456, 1838), (39, 1457, 1838), (39, 1458, 1838), (39, 1459, 1838), (39, 1460, 1838), (39, 1461, 1837), (39, 1462, 1837), (39, 1463, 1837), (39, 1464, 1837), (39, 1465, 1837), (39, 1466, 1836), (39, 1467, 1836), (39, 1468, 1836), (39, 1469, 1836), (39, 1470, 1835), (39, 1471, 1835), (39, 1472, 1835), (39, 1473, 1835), (39, 1474, 1835), (39, 1475, 1834), (39, 1476, 1834), (39, 1477, 1834), (39, 1478, 1834), (39, 1479, 1834), (39, 1480, 1834), (39, 1481, 1833), (39, 1482, 1833), (39, 1483, 1833), (39, 1484, 1833), (39, 1485, 1833), (39, 1486, 1832), (39, 1487, 1832), (39, 1488, 1832), (39, 1489, 1832), (39, 1490, 1832), (39, 1491, 1831), (39, 1492, 1831), (39, 1493, 1831), (39, 1494, 1831), (40, 1495, 1830), (40, 1496, 1830), (40, 1497, 1829), (40, 1498, 1829), (40, 1499, 1829), (40, 1500, 1829), (40, 1501, 1829), (40, 1502, 1829), (40, 1503, 1828), (40, 1504, 1828), (40, 1505, 1828), (40, 1506, 1828), (40, 1507, 1828), (40, 1508, 1828), (40, 1509, 1827), (40, 1510, 1827), (40, 1511, 1827), (40, 1512, 1827), (40, 1513, 1827), (40, 1514, 1827), (40, 1515, 1826), (40, 1516, 1826), (40, 1517, 1826), (40, 1518, 1826), (40, 1519, 1826), (40, 1520, 1826), (40, 1521, 1825), (40, 1522, 1825), (40, 1523, 1825), (40, 1524, 1825), (40, 1525, 1825), (40, 1526, 1825), (40, 1527, 1825), (40, 1528, 1825), (40, 1529, 1824), (40, 1530, 1824), (40, 1531, 1824), (40, 1532, 1824), (40, 1533, 1824), (40, 1534, 1824), (40, 1535, 1824), (40, 1536, 1824), (40, 1537, 1823), (41, 1538, 1822), (41, 1539, 1822), (41, 1540, 1822), (41, 1541, 1822), (41, 1542, 1822), (41, 1543, 1822), (41, 1544, 1822), (41, 1545, 1821), (41, 1546, 1821), (41, 1547, 1821), (41, 1548, 1821), (41, 1549, 1821), (41, 1550, 1821), (41, 1551, 1821), (41, 1552, 1821), (41, 1553, 1820), (41, 1554, 1820), (41, 1555, 1820), (41, 1556, 1820), (41, 1557, 1820), (41, 1558, 1820), (41, 1559, 1820), (41, 1560, 1820), (41, 1561, 1819), (41, 1562, 1819), (41, 1563, 1819), (41, 1564, 1819), (41, 1565, 1819), (41, 1566, 1819), (41, 1567, 1819), (41, 1568, 1819), (41, 1569, 1818), (41, 1570, 1818), (41, 1571, 1818), (41, 1572, 1818), (41, 1573, 1818), (41, 1574, 1818), (41, 1575, 1818), (41, 1576, 1818), (41, 1577, 1817), (41, 1578, 1817), (41, 1579, 1817), (41, 1580, 1817), (41, 1581, 1817), (41, 1582, 1817), (42, 1583, 1816), (42, 1584, 1815), (42, 1585, 1815), (42, 1586, 1815), (42, 1587, 1815), (42, 1588, 1815), (42, 1589, 1815), (42, 1590, 1815), (42, 1591, 1815), (42, 1592, 1814), (42, 1593, 1814), (42, 1594, 1814), (42, 1595, 1814), (42, 1596, 1814), (42, 1597, 1814), (42, 1598, 1814), (42, 1599, 1814), (42, 1600, 1813), (42, 1601, 1813), (42, 1602, 1813), (42, 1603, 1813), (41, 1604, 1814), (41, 1605, 1814), (41, 1606, 1814), (41, 1607, 1814), (41, 1608, 1814), (41, 1609, 1813), (41, 1610, 1813), (41, 1611, 1813), (41, 1612, 1813), (41, 1613, 1813), (41, 1614, 1813), (41, 1615, 1813), (41, 1616, 1813), (41, 1617, 1813), (41, 1618, 1812), (41, 1619, 1812), (41, 1620, 1812), (41, 1621, 1812), (41, 1622, 1812), (41, 1623, 1812), (41, 1624, 1812), (41, 1625, 1812), (41, 1626, 1811), (40, 1627, 1812), (40, 1628, 1812), (40, 1629, 1812), (40, 1630, 1812), (40, 1631, 1812), (40, 1632, 1812), (40, 1633, 1812), (40, 1634, 1811), (40, 1635, 1811), (40, 1636, 1811), (40, 1637, 1811), (40, 1638, 1811), (40, 1639, 1811), (40, 1640, 1811), (40, 1641, 1811), (40, 1642, 1810), (40, 1643, 1810), (40, 1644, 1810), (40, 1645, 1810), (40, 1646, 1810), (40, 1647, 1810), (40, 1648, 1810), (40, 1649, 1809), (40, 1650, 1809), (39, 1651, 1810), (39, 1652, 1810), (39, 1653, 1810), (39, 1654, 1810), (39, 1655, 1810), (39, 1656, 1810), (39, 1657, 1809), (39, 1658, 1809), (39, 1659, 1809), (39, 1660, 1809), (39, 1661, 1809), (39, 1662, 1809), (39, 1663, 1809), (39, 1664, 1808), (39, 1665, 1808), (39, 1666, 1808), (39, 1667, 1808), (39, 1668, 1808), (39, 1669, 1808), (39, 1670, 1808), (39, 1671, 1807), (39, 1672, 1807), (39, 1673, 1807), (39, 1674, 1807), (39, 1675, 1806), (39, 1676, 1806), (39, 1677, 1806), (40, 1678, 1805), (40, 1679, 1804), (40, 1680, 1804), (40, 1681, 1804), (40, 1682, 1804), (40, 1683, 1803), (41, 1684, 1802), (41, 1685, 1802), (41, 1686, 1802), (41, 1687, 1801), (41, 1688, 1801), (41, 1689, 1801), (42, 1690, 1800), (42, 1691, 1799), (42, 1692, 1799), (42, 1693, 1799), (42, 1694, 1798), (42, 1695, 1798), (43, 1696, 1797), (43, 1697, 1797), (43, 1698, 1796), (43, 1699, 1796), (43, 1700, 1796), (43, 1701, 1795), (44, 1702, 1794), (44, 1703, 1794), (44, 1704, 1793), (44, 1705, 1793), (44, 1706, 1793), (44, 1707, 1793), (45, 1708, 1791), (45, 1709, 1791), (45, 1710, 1791), (45, 1711, 1790), (45, 1712, 1790), (45, 1713, 1790), (46, 1714, 1788), (46, 1715, 1788), (46, 1716, 1788), (46, 1717, 1787), (46, 1718, 1787), (47, 1719, 1786), (47, 1720, 1785), (47, 1721, 1785), (47, 1722, 1784), (47, 1723, 1784), (48, 1724, 1783), (48, 1725, 1782), (48, 1726, 1782), (48, 1727, 1782), (48, 1728, 1781), (49, 1729, 1780), (49, 1730, 1779), (49, 1731, 1779), (49, 1732, 1779), (49, 1733, 1778), (50, 1734, 1777), (50, 1735, 1776), (50, 1736, 1776), (50, 1737, 1776), (51, 1738, 1774), (51, 1739, 1774), (51, 1740, 1773), (51, 1741, 1773), (51, 1742, 1772), (52, 1743, 1771), (52, 1744, 1771), (52, 1745, 1770), (52, 1746, 1770), (52, 1747, 1769), (52, 1748, 1769), (52, 1749, 1768), (52, 1750, 1768), (52, 1751, 1768), (53, 1752, 1766), (53, 1753, 1766), (53, 1754, 1765), (53, 1755, 1765), (53, 1756, 1765), (53, 1757, 1764), (53, 1758, 1764), (53, 1759, 1763), (53, 1760, 1763), (53, 1761, 1763), (53, 1762, 1762), (53, 1763, 1762), (53, 1764, 1762), (53, 1765, 1761), (53, 1766, 1761), (53, 1767, 1761), (53, 1768, 1760), (53, 1769, 1760), (53, 1770, 1759), (53, 1771, 1759), (53, 1772, 1759), (53, 1773, 1758), (53, 1774, 1758), (53, 1775, 1758), (53, 1776, 1757), (53, 1777, 1757), (53, 1778, 1757), (53, 1779, 1756), (53, 1780, 1756), (53, 1781, 1756), (53, 1782, 1755), (53, 1783, 1755), (53, 1784, 1755), (53, 1785, 1754), (53, 1786, 1754), (53, 1787, 1754), (53, 1788, 1753), (53, 1789, 1753), (53, 1790, 1753), (53, 1791, 1753), (53, 1792, 1752), (53, 1793, 1752), (53, 1794, 1752), (53, 1795, 1751), (53, 1796, 1751), (53, 1797, 1751), (53, 1798, 1750), (53, 1799, 1750), (53, 1800, 1750), (53, 1801, 1750), (53, 1802, 1749), (53, 1803, 1749), (53, 1804, 1749), (53, 1805, 1748), (53, 1806, 1748), (53, 1807, 1748), (53, 1808, 1748), (53, 1809, 1747), (53, 1810, 1747), (53, 1811, 1747), (53, 1812, 1746), (53, 1813, 1746), (53, 1814, 1746), (54, 1815, 1745), (54, 1816, 1744), (54, 1817, 1744), (54, 1818, 1744), (54, 1819, 1744), (54, 1820, 1743), (54, 1821, 1743), (54, 1822, 1743), (54, 1823, 1743), (54, 1824, 1742), (54, 1825, 1742), (55, 1826, 1741), (55, 1827, 1740), (56, 1828, 1739), (56, 1829, 1739), (57, 1830, 1737), (57, 1831, 1737), (58, 1832, 1736), (58, 1833, 1735), (59, 1834, 1734), (59, 1835, 1734), (60, 1836, 1732), (60, 1837, 1732), (61, 1838, 1731), (61, 1839, 1730), (61, 1840, 1730), (62, 1841, 1729), (62, 1842, 1728), (63, 1843, 1727), (63, 1844, 1727), (64, 1845, 1725), (64, 1846, 1725), (65, 1847, 1723), (65, 1848, 1723), (66, 1849, 1722), (66, 1850, 1721), (67, 1851, 1720), (67, 1852, 1720), (67, 1853, 1719), (68, 1854, 1718), (68, 1855, 1718), (69, 1856, 1716), (69, 1857, 1716), (70, 1858, 1714), (70, 1859, 1714), (71, 1860, 1713), (71, 1861, 1712), (72, 1862, 1711), (72, 1863, 1711), (72, 1864, 1710), (73, 1865, 1709), (73, 1866, 1708), (74, 1867, 1707), (74, 1868, 1707), (75, 1869, 1705), (75, 1870, 1705), (75, 1871, 1704), (76, 1872, 1703), (76, 1873, 1703), (77, 1874, 1701), (77, 1875, 1701), (78, 1876, 1699), (78, 1877, 1699), (79, 1878, 1698), (79, 1879, 1697), (79, 1880, 1697), (80, 1881, 1695), (80, 1882, 1695), (81, 1883, 1693), (81, 1884, 1693), (82, 1885, 1692), (82, 1886, 1691), (82, 1887, 1691), (83, 1888, 1689), (83, 1889, 1689), (84, 1890, 1687), (84, 1891, 1687), (84, 1892, 1687), (85, 1893, 1685), (85, 1894, 1685), (86, 1895, 1683), (86, 1896, 1683), (87, 1897, 1681), (87, 1898, 1681), (87, 1899, 1680), (88, 1900, 1679), (88, 1901, 1679), (88, 1902, 1678), (89, 1903, 1677), (89, 1904, 1676), (90, 1905, 1675), (90, 1906, 1674), (90, 1907, 1674), (91, 1908, 1672), (91, 1909, 1672), (91, 1910, 1671), (92, 1911, 1670), (92, 1912, 1669), (93, 1913, 1667), (93, 1914, 1667), (94, 1915, 1665), (94, 1916, 1665), (94, 1917, 1664), (95, 1918, 1663), (95, 1919, 1662), (96, 1920, 1661), (96, 1921, 1660), (97, 1922, 1658), (97, 1923, 1658), (97, 1924, 1657), (98, 1925, 1656), (98, 1926, 1655), (99, 1927, 1654), (99, 1928, 1653), (100, 1929, 1651), (100, 1930, 1651), (101, 1931, 1649), (101, 1932, 1648), (102, 1933, 1647), (102, 1934, 1646), (103, 1935, 1644), (103, 1936, 1644), (104, 1937, 1642), (105, 1938, 1640), (105, 1939, 1640), (106, 1940, 1638), (106, 1941, 1637), (107, 1942, 1635), (107, 1943, 1635), (108, 1944, 1633), (109, 1945, 1631), (109, 1946, 1630), (110, 1947, 1628), (110, 1948, 1628), (111, 1949, 1626), (112, 1950, 1624), (112, 1951, 1623), (113, 1952, 1622), (114, 1953, 1620), (114, 1954, 1619), (115, 1955, 1617), (116, 1956, 1616), (117, 1957, 1614), (117, 1958, 1613), (118, 1959, 1611), (119, 1960, 1610), (120, 1961, 1608), (120, 1962, 1607), (121, 1963, 86), (210, 1963, 1516), (122, 1964, 73), (217, 1964, 1509), (123, 1965, 61), (223, 1965, 1502), (123, 1966, 50), (230, 1966, 1494), (124, 1967, 39), (237, 1967, 1487), (125, 1968, 29), (245, 1968, 1478), (126, 1969, 20), (252, 1969, 1470), (127, 1970, 11), (259, 1970, 1463), (128, 1971, 3), (266, 1971, 1455), (274, 1972, 1446), (282, 1973, 1438), (289, 1974, 1430), (294, 1975, 1424), (299, 1976, 1418), (304, 1977, 1412), (309, 1978, 1406), (314, 1979, 1400), (319, 1980, 1394), (325, 1981, 1387), (331, 1982, 1380), (337, 1983, 1373), (344, 1984, 1365), (351, 1985, 1357), (358, 1986, 1349), (366, 1987, 1339), (372, 1988, 1332), (376, 1989, 1327), (380, 1990, 1322), (384, 1991, 1317), (389, 1992, 1310), (393, 1993, 1305), (397, 1994, 1300), (402, 1995, 1293), (406, 1996, 1288), (411, 1997, 1282), (415, 1998, 1276), (420, 1999, 1270), (425, 2000, 1263), (430, 2001, 1257), (435, 2002, 1250), (440, 2003, 1244), (445, 2004, 1237), (451, 2005, 1230), (456, 2006, 1223), (461, 2007, 1217), (466, 2008, 1210), (471, 2009, 1203), (476, 2010, 1196), (481, 2011, 1190), (486, 2012, 1183), (492, 2013, 1175), (497, 2014, 1168), (503, 2015, 1160), (508, 2016, 1073), (514, 2017, 1063), (520, 2018, 1053), (525, 2019, 1044), (531, 2020, 1034), (536, 2021, 1025), (541, 2022, 1016), (546, 2023, 1008), (551, 2024, 999), (556, 2025, 990), (561, 2026, 982), (565, 2027, 974), (570, 2028, 965), (574, 2029, 958), (579, 2030, 950), (583, 2031, 942), (587, 2032, 935), (591, 2033, 928), (595, 2034, 920), (599, 2035, 913), (603, 2036, 906), (607, 2037, 899), (611, 2038, 892), (614, 2039, 883), (617, 2040, 869), (619, 2041, 857), (622, 2042, 844), (624, 2043, 832), (627, 2044, 820), (629, 2045, 808), (631, 2046, 797), (634, 2047, 786), (636, 2048, 780), (638, 2049, 775), (640, 2050, 770), (642, 2051, 765), (643, 2052, 761), (645, 2053, 756), (647, 2054, 751), (649, 2055, 746), (650, 2056, 742), (652, 2057, 736), (654, 2058, 731), (656, 2059, 726), (658, 2060, 720), (660, 2061, 715), (662, 2062, 709), (664, 2063, 703), (666, 2064, 698), (668, 2065, 692), (671, 2066, 685), (673, 2067, 679), (675, 2068, 673), (678, 2069, 666), (680, 2070, 660), (683, 2071, 651), (685, 2072, 644), (688, 2073, 636), (691, 2074, 628), (694, 2075, 619), (699, 2076, 609), (703, 2077, 599), (708, 2078, 588), (712, 2079, 578), (716, 2080, 568), (721, 2081, 557), (725, 2082, 547), (730, 2083, 536), (734, 2084, 526), (738, 2085, 516), (743, 2086, 506), (747, 2087, 497), (751, 2088, 488), (755, 2089, 479), (759, 2090, 470), (764, 2091, 460), (768, 2092, 451), (772, 2093, 443), (776, 2094, 434), (779, 2095, 427), (782, 2096, 419), (785, 2097, 412), (789, 2098, 404), (792, 2099, 397), (795, 2100, 390), (798, 2101, 383), (802, 2102, 375), (805, 2103, 370), (808, 2104, 364), (811, 2105, 359), (814, 2106, 354), (818, 2107, 347), (821, 2108, 342), (824, 2109, 337), (827, 2110, 332), (830, 2111, 327), (833, 2112, 322), (836, 2113, 317), (839, 2114, 312), (842, 2115, 307), (845, 2116, 302), (848, 2117, 297), (851, 2118, 292), (854, 2119, 287), (857, 2120, 283), (860, 2121, 278), (863, 2122, 273), (866, 2123, 269), (869, 2124, 264), (872, 2125, 259), (875, 2126, 255), (877, 2127, 251), (880, 2128, 246), (883, 2129, 241), (886, 2130, 236), (889, 2131, 232), (893, 2132, 226), (896, 2133, 220), (899, 2134, 215), (903, 2135, 209), (906, 2136, 204), (909, 2137, 199), (913, 2138, 193), (917, 2139, 186), (920, 2140, 181), (924, 2141, 174), (928, 2142, 165), (932, 2143, 153), (936, 2144, 142), (946, 2145, 124), (956, 2146, 106), (967, 2147, 86), (978, 2148, 67), (989, 2149, 47), (1001, 2150, 27), (1013, 2151, 6)], ['936,2144,771,2092,685,2072,610,2037,371,1987,216,1963,128,1971,97,1924,54,1825,39,1677,39,1452,29,1240,27,757,21,695,27,543,39,458,93,308,116,278,210,206,291,179,373,132,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2012,294,2098,420,2148,535,2168,614,2165,833,2128,914,2091,1049,2031,1132,1958,1273,1931,1368,1879,1444,1846,1670,1782,1863,1744,1939,1706,1986,1662,2015,1581,2015,1496,2039,1420,2046,1347,2068,1177,2101,1105,2138,1027,2150'])], 'temp/1772338871_3257054_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3690898 proportion of common points : 0.9996216445327364 #&_# TEST SUCCEEDED #&_# : tests/mask_test #&_# #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : sam list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2093353271484375 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:sam Sun Mar 1 05:21:38 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0018701553344726562 nb_pixel_total : 13932 time to create 1 rle with old method : 0.02908158302307129 time for calcul the mask position with numpy : 0.0013728141784667969 nb_pixel_total : 16422 time to create 1 rle with old method : 0.03449416160583496 time for calcul the mask position with numpy : 0.0013217926025390625 nb_pixel_total : 5615 time to create 1 rle with old method : 0.012488603591918945 time for calcul the mask position with numpy : 0.0017268657684326172 nb_pixel_total : 83878 time to create 1 rle with old method : 0.18113470077514648 time for calcul the mask position with numpy : 0.0013422966003417969 nb_pixel_total : 7585 time to create 1 rle with old method : 0.017012596130371094 time for calcul the mask position with numpy : 0.0015528202056884766 nb_pixel_total : 38686 time to create 1 rle with old method : 0.08529090881347656 time for calcul the mask position with numpy : 0.0013132095336914062 nb_pixel_total : 5522 time to create 1 rle with old method : 0.011936426162719727 time for calcul the mask position with numpy : 0.0012929439544677734 nb_pixel_total : 3781 time to create 1 rle with old method : 0.008160114288330078 time for calcul the mask position with numpy : 0.0013475418090820312 nb_pixel_total : 14877 time to create 1 rle with old method : 0.032733917236328125 time for calcul the mask position with numpy : 0.0013213157653808594 nb_pixel_total : 2781 time to create 1 rle with old method : 0.006036996841430664 time for calcul the mask position with numpy : 0.0013415813446044922 nb_pixel_total : 2940 time to create 1 rle with old method : 0.006543159484863281 time for calcul the mask position with numpy : 0.0014252662658691406 nb_pixel_total : 29434 time to create 1 rle with old method : 0.06277036666870117 time for calcul the mask position with numpy : 0.0015325546264648438 nb_pixel_total : 10824 time to create 1 rle with old method : 0.02309727668762207 time for calcul the mask position with numpy : 0.0014464855194091797 nb_pixel_total : 2370 time to create 1 rle with old method : 0.005215644836425781 time for calcul the mask position with numpy : 0.001302480697631836 nb_pixel_total : 4272 time to create 1 rle with old method : 0.009406805038452148 time for calcul the mask position with numpy : 0.0013239383697509766 nb_pixel_total : 2334 time to create 1 rle with old method : 0.0052449703216552734 time for calcul the mask position with numpy : 0.0012829303741455078 nb_pixel_total : 1227 time to create 1 rle with old method : 0.002888917922973633 time for calcul the mask position with numpy : 0.0013210773468017578 nb_pixel_total : 3951 time to create 1 rle with old method : 0.008745431900024414 time for calcul the mask position with numpy : 0.0013155937194824219 nb_pixel_total : 6635 time to create 1 rle with old method : 0.014468908309936523 time for calcul the mask position with numpy : 0.0013430118560791016 nb_pixel_total : 13141 time to create 1 rle with old method : 0.027747154235839844 time for calcul the mask position with numpy : 0.001302957534790039 nb_pixel_total : 4298 time to create 1 rle with old method : 0.00988626480102539 time for calcul the mask position with numpy : 0.0013556480407714844 nb_pixel_total : 16362 time to create 1 rle with old method : 0.035759687423706055 time for calcul the mask position with numpy : 0.001377105712890625 nb_pixel_total : 2079 time to create 1 rle with old method : 0.00442814826965332 time for calcul the mask position with numpy : 0.0013613700866699219 nb_pixel_total : 852 time to create 1 rle with old method : 0.002033710479736328 time for calcul the mask position with numpy : 0.001329183578491211 nb_pixel_total : 8652 time to create 1 rle with old method : 0.019085407257080078 time for calcul the mask position with numpy : 0.001346588134765625 nb_pixel_total : 11966 time to create 1 rle with old method : 0.026408910751342773 time for calcul the mask position with numpy : 0.0013089179992675781 nb_pixel_total : 3304 time to create 1 rle with old method : 0.007415056228637695 time for calcul the mask position with numpy : 0.0012979507446289062 nb_pixel_total : 3537 time to create 1 rle with old method : 0.007915496826171875 time for calcul the mask position with numpy : 0.0013303756713867188 nb_pixel_total : 9873 time to create 1 rle with old method : 0.021971940994262695 time for calcul the mask position with numpy : 0.001295328140258789 nb_pixel_total : 4169 time to create 1 rle with old method : 0.009352445602416992 time for calcul the mask position with numpy : 0.0013401508331298828 nb_pixel_total : 13019 time to create 1 rle with old method : 0.029639005661010742 time for calcul the mask position with numpy : 0.001302957534790039 nb_pixel_total : 2448 time to create 1 rle with old method : 0.005634307861328125 time for calcul the mask position with numpy : 0.0013103485107421875 nb_pixel_total : 6058 time to create 1 rle with old method : 0.013872623443603516 time for calcul the mask position with numpy : 0.0013422966003417969 nb_pixel_total : 3884 time to create 1 rle with old method : 0.009109973907470703 time for calcul the mask position with numpy : 0.001455545425415039 nb_pixel_total : 2729 time to create 1 rle with old method : 0.006337404251098633 time for calcul the mask position with numpy : 0.0014553070068359375 nb_pixel_total : 5435 time to create 1 rle with old method : 0.012285947799682617 time for calcul the mask position with numpy : 0.0014500617980957031 nb_pixel_total : 8485 time to create 1 rle with old method : 0.018962383270263672 time for calcul the mask position with numpy : 0.0012755393981933594 nb_pixel_total : 343 time to create 1 rle with old method : 0.0008151531219482422 time for calcul the mask position with numpy : 0.0012798309326171875 nb_pixel_total : 1253 time to create 1 rle with old method : 0.0027589797973632812 time for calcul the mask position with numpy : 0.0013244152069091797 nb_pixel_total : 10580 time to create 1 rle with old method : 0.02332139015197754 time for calcul the mask position with numpy : 0.0012798309326171875 nb_pixel_total : 1025 time to create 1 rle with old method : 0.0022885799407958984 time for calcul the mask position with numpy : 0.0012812614440917969 nb_pixel_total : 1620 time to create 1 rle with old method : 0.0036001205444335938 time for calcul the mask position with numpy : 0.0012927055358886719 nb_pixel_total : 4138 time to create 1 rle with old method : 0.009334087371826172 time for calcul the mask position with numpy : 0.0012850761413574219 nb_pixel_total : 2380 time to create 1 rle with old method : 0.005762577056884766 time for calcul the mask position with numpy : 0.0014085769653320312 nb_pixel_total : 595 time to create 1 rle with old method : 0.0015041828155517578 time for calcul the mask position with numpy : 0.0014185905456542969 nb_pixel_total : 876 time to create 1 rle with old method : 0.0020635128021240234 time for calcul the mask position with numpy : 0.0013363361358642578 nb_pixel_total : 2323 time to create 1 rle with old method : 0.005342006683349609 time for calcul the mask position with numpy : 0.0012929439544677734 nb_pixel_total : 1673 time to create 1 rle with old method : 0.0036911964416503906 time for calcul the mask position with numpy : 0.0013294219970703125 nb_pixel_total : 2414 time to create 1 rle with old method : 0.005762577056884766 time for calcul the mask position with numpy : 0.0012850761413574219 nb_pixel_total : 577 time to create 1 rle with old method : 0.0013320446014404297 time for calcul the mask position with numpy : 0.0013034343719482422 nb_pixel_total : 1706 time to create 1 rle with old method : 0.003964900970458984 time for calcul the mask position with numpy : 0.0013604164123535156 nb_pixel_total : 336 time to create 1 rle with old method : 0.0008137226104736328 time for calcul the mask position with numpy : 0.0012896060943603516 nb_pixel_total : 966 time to create 1 rle with old method : 0.002164602279663086 time for calcul the mask position with numpy : 0.0012974739074707031 nb_pixel_total : 692 time to create 1 rle with old method : 0.001680135726928711 time for calcul the mask position with numpy : 0.0014183521270751953 nb_pixel_total : 28067 time to create 1 rle with old method : 0.06132936477661133 time for calcul the mask position with numpy : 0.0013031959533691406 nb_pixel_total : 2767 time to create 1 rle with old method : 0.006189823150634766 time for calcul the mask position with numpy : 0.0013456344604492188 nb_pixel_total : 1055 time to create 1 rle with old method : 0.002478361129760742 time for calcul the mask position with numpy : 0.0012798309326171875 nb_pixel_total : 1206 time to create 1 rle with old method : 0.002699613571166992 time for calcul the mask position with numpy : 0.0013227462768554688 nb_pixel_total : 588 time to create 1 rle with old method : 0.0013115406036376953 time for calcul the mask position with numpy : 0.0012841224670410156 nb_pixel_total : 1074 time to create 1 rle with old method : 0.0024366378784179688 time for calcul the mask position with numpy : 0.001298666000366211 nb_pixel_total : 618 time to create 1 rle with old method : 0.0015025138854980469 time for calcul the mask position with numpy : 0.0012884140014648438 nb_pixel_total : 3106 time to create 1 rle with old method : 0.006866455078125 time for calcul the mask position with numpy : 0.0013508796691894531 nb_pixel_total : 8601 time to create 1 rle with old method : 0.0200192928314209 time for calcul the mask position with numpy : 0.0013651847839355469 nb_pixel_total : 877 time to create 1 rle with old method : 0.0020182132720947266 time for calcul the mask position with numpy : 0.0013039112091064453 nb_pixel_total : 713 time to create 1 rle with old method : 0.0017976760864257812 time for calcul the mask position with numpy : 0.0013628005981445312 nb_pixel_total : 16681 time to create 1 rle with old method : 0.03777456283569336 time for calcul the mask position with numpy : 0.0014209747314453125 nb_pixel_total : 18508 time to create 1 rle with old method : 0.041901588439941406 time for calcul the mask position with numpy : 0.0013248920440673828 nb_pixel_total : 9503 time to create 1 rle with old method : 0.021436214447021484 time for calcul the mask position with numpy : 0.0013337135314941406 nb_pixel_total : 1801 time to create 1 rle with old method : 0.004099130630493164 time for calcul the mask position with numpy : 0.0013036727905273438 nb_pixel_total : 1828 time to create 1 rle with old method : 0.00426793098449707 time for calcul the mask position with numpy : 0.001310110092163086 nb_pixel_total : 1513 time to create 1 rle with old method : 0.0034673213958740234 time for calcul the mask position with numpy : 0.001294851303100586 nb_pixel_total : 1336 time to create 1 rle with old method : 0.0030760765075683594 time for calcul the mask position with numpy : 0.0012807846069335938 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006177425384521484 time for calcul the mask position with numpy : 0.0012967586517333984 nb_pixel_total : 3217 time to create 1 rle with old method : 0.00736546516418457 time for calcul the mask position with numpy : 0.0012950897216796875 nb_pixel_total : 248 time to create 1 rle with old method : 0.0006802082061767578 time for calcul the mask position with numpy : 0.0013473033905029297 nb_pixel_total : 972 time to create 1 rle with old method : 0.0024025440216064453 time for calcul the mask position with numpy : 0.001421213150024414 nb_pixel_total : 221 time to create 1 rle with old method : 0.0005748271942138672 time for calcul the mask position with numpy : 0.0013222694396972656 nb_pixel_total : 889 time to create 1 rle with old method : 0.002040863037109375 time for calcul the mask position with numpy : 0.0012912750244140625 nb_pixel_total : 2201 time to create 1 rle with old method : 0.005080223083496094 time for calcul the mask position with numpy : 0.0012857913970947266 nb_pixel_total : 883 time to create 1 rle with old method : 0.0020186901092529297 time for calcul the mask position with numpy : 0.001413106918334961 nb_pixel_total : 735 time to create 1 rle with old method : 0.001817941665649414 time for calcul the mask position with numpy : 0.0013620853424072266 nb_pixel_total : 1634 time to create 1 rle with old method : 0.0037686824798583984 time for calcul the mask position with numpy : 0.001287698745727539 nb_pixel_total : 1484 time to create 1 rle with old method : 0.0034589767456054688 time for calcul the mask position with numpy : 0.0014102458953857422 nb_pixel_total : 5012 time to create 1 rle with old method : 0.011342763900756836 time for calcul the mask position with numpy : 0.0012836456298828125 nb_pixel_total : 299 time to create 1 rle with old method : 0.0007579326629638672 time for calcul the mask position with numpy : 0.001287221908569336 nb_pixel_total : 1442 time to create 1 rle with old method : 0.0070226192474365234 time for calcul the mask position with numpy : 0.0015606880187988281 nb_pixel_total : 7340 time to create 1 rle with old method : 0.01666855812072754 time for calcul the mask position with numpy : 0.001306772232055664 nb_pixel_total : 595 time to create 1 rle with old method : 0.0014865398406982422 time for calcul the mask position with numpy : 0.001287698745727539 nb_pixel_total : 1123 time to create 1 rle with old method : 0.0025353431701660156 time for calcul the mask position with numpy : 0.001356363296508789 nb_pixel_total : 9205 time to create 1 rle with old method : 0.02078080177307129 time for calcul the mask position with numpy : 0.0014119148254394531 nb_pixel_total : 7512 time to create 1 rle with old method : 0.016548633575439453 time for calcul the mask position with numpy : 0.0013072490692138672 nb_pixel_total : 421 time to create 1 rle with old method : 0.0009756088256835938 time for calcul the mask position with numpy : 0.001280069351196289 nb_pixel_total : 480 time to create 1 rle with old method : 0.0011382102966308594 time for calcul the mask position with numpy : 0.0012879371643066406 nb_pixel_total : 1356 time to create 1 rle with old method : 0.003182649612426758 time for calcul the mask position with numpy : 0.0013632774353027344 nb_pixel_total : 1320 time to create 1 rle with old method : 0.002912282943725586 time for calcul the mask position with numpy : 0.0012912750244140625 nb_pixel_total : 947 time to create 1 rle with old method : 0.0021796226501464844 time for calcul the mask position with numpy : 0.0014240741729736328 nb_pixel_total : 886 time to create 1 rle with old method : 0.002191305160522461 time for calcul the mask position with numpy : 0.0013670921325683594 nb_pixel_total : 1383 time to create 1 rle with old method : 0.003360748291015625 time for calcul the mask position with numpy : 0.0013022422790527344 nb_pixel_total : 1678 time to create 1 rle with old method : 0.004002809524536133 time for calcul the mask position with numpy : 0.0013883113861083984 nb_pixel_total : 2656 time to create 1 rle with old method : 0.006377220153808594 batch 1 Loaded 100 chid ids of type : 4677 Number RLEs to save : 9082 TO DO : save crop sub photo not yet done ! Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] Looping around the photos to save general results len do output : 1 /1189321094Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4573', None, None, None, None, None, None, None, None) ('4573', None, '1189321094', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.01610255241394043 save_final save missing photos in datou_result : time spend for datou_step_exec : 14.836296319961548 time spend to save output : 0.01641106605529785 total time spend for step 1 : 14.852707386016846 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1772338898_3257054_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 100 ############################### TEST frcnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : frcnn list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14246630668640137 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:frcnn Sun Mar 1 05:21:53 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : [] file manque in s3 : ['caffemodel', 'test.prototxt'] [libprotobuf ERROR google/protobuf/text_format.cc:307] Error parsing text-format caffe.NetParameter: 325:21: Message type "caffe.LayerParameter" has no field named "roi_pooling_param". WARNING: Logging before InitGoogleLogging() is written to STDERR F0301 05:21:54.841673 3257054 upgrade_proto.cpp:90] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: /data/models_weight/detection_plaque_valcor_010622/test.prototxt *** Check failure stack trace: *** Aborted (core dumped) No data to report. No data to report. ret : 34304 command : coverage3 html -i --omit=/usr/local/lib/python3.8/dist-packages/*,/home/admin/.local/lib/python3.8/site-packages/*,/usr/lib/python3/dist-packages/* -d htmlcov ret : 256 command : coverage3 report -i -m ret : 256 44.45user 22.74system 1:30.61elapsed 74%CPU (0avgtext+0avgdata 2965840maxresident)k 2334192inputs+4744outputs (5330major+2922176minor)pagefaults 0swaps