python /home/admin/mtr/script_for_cron.py -j default -m 20 -a 'python3 ~/workarea/git/Velours/python/prod/datou.py -j batch_current -C 2502549' -s traitement_3459 -M 0 -S 0 -U 100,80,95 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/home/admin/workarea/git/apy', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 3864311 load datou : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? load thcls load pdts Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 3459, datou_cur_ids : ['2502549'] with mtr_portfolio_ids : ['19816777'] and first list_photo_ids : [] new path : /proc/3864311/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, thcl, merge_mask_thcl_custom, rle_unique_nms_with_priority, crop_condition, ventilate_hashtags_in_portfolio, final, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 20 list_input_json : [] origin We have 1 , WARNING: data may be incomplete, need to offset and complete ! BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 20 ; length of list_pids : 20 ; length of list_args : 20 time to download the photos : 6.328114032745361 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 : 0 number of steps : 11 step1:mask_detect Tue Feb 4 12:27:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6150 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-04 12:27:10.280058: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-04 12:27:10.287758: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-04 12:27:10.289242: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2234000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-04 12:27:10.289290: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-04 12:27:10.291907: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-04 12:27:10.402091: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x18e8b040 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-04 12:27:10.402133: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-04 12:27:10.403373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-04 12:27:10.403769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 12:27:10.406953: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 12:27:10.410011: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 12:27:10.410521: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 12:27:10.413121: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 12:27:10.414136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 12:27:10.418535: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 12:27:10.419795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 12:27:10.419870: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 12:27:10.420496: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-04 12:27:10.420512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-04 12:27:10.420538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-04 12:27:10.421614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5616 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-02-04 12:27:10.675295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-04 12:27:10.675381: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 12:27:10.675402: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 12:27:10.675421: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 12:27:10.675439: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 12:27:10.675457: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 12:27:10.675474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 12:27:10.675493: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 12:27:10.676795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 12:27:10.677948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-04 12:27:10.677982: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 12:27:10.678000: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 12:27:10.678017: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 12:27:10.678034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 12:27:10.678051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 12:27:10.678068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 12:27:10.678086: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 12:27:10.679401: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 12:27:10.679434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-04 12:27:10.679445: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-04 12:27:10.679454: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-04 12:27:10.680663: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5616 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 To do loadFromThcl(), then load ParamDescType : thcl2896 thcls : [{'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5309 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5309, 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 16384, 25088, 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 5, 10, 19, 20, 46), datetime.datetime(2021, 5, 10, 19, 20, 46)) {'thcl': {'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'carton_brun', 'carton_gris', 'cartonnette', 'kraft', 'autre_refus', 'metal', 'plastique', 'teint_dans_la_masse', 'environnement'], 'list_hashtags_csv': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'svm_hashtag_type_desc': 5309, 'photo_desc_type': 5309, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'carton_brun', 'carton_gris', 'cartonnette', 'kraft', 'autre_refus', 'metal', 'plastique', 'teint_dans_la_masse', 'environnement'] 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 learn_convoyeur_qualipapia_nantes_poly_100521_1 NUM_CLASSES 10 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 : learn_convoyeur_qualipapia_nantes_poly_100521_1 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-02-04 12:27:20.034165: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 12:27:20.216650: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_convoyeur_qualipapia_nantes_poly_100521_1 /data/models_weight/learn_convoyeur_qualipapia_nantes_poly_100521_1/mask_model.h5 size_local : 256031040 size in s3 : 256031040 create time local : 2021-08-09 05:45:48 create time in s3 : 2021-08-06 18:59:51 mask_model.h5 already exist and didn't need to update list_images length : 20 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 30 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 25 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 24 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 14 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 24 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 29 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 25 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 17 Detection mask done ! Trying to reset tf kernel 3864622 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1058 tf kernel not reseted sub process len(results) : 20 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 20 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 : 6347 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2896 Catched exception ! Connect or reconnect ! thcls : [{'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5309 ['background', 'carton_brun', 'carton_gris', 'cartonnette', 'kraft', 'autre_refus', 'metal', 'plastique', 'teint_dans_la_masse', 'environnement'] time for calcul the mask position with numpy : 0.0035028457641601562 nb_pixel_total : 115480 time to create 1 rle with old method : 0.15668058395385742 length of segment : 472 time for calcul the mask position with numpy : 0.0015730857849121094 nb_pixel_total : 89052 time to create 1 rle with old method : 0.09253668785095215 length of segment : 487 time for calcul the mask position with numpy : 0.00012826919555664062 nb_pixel_total : 3606 time to create 1 rle with old method : 0.004039764404296875 length of segment : 73 time for calcul the mask position with numpy : 0.00027632713317871094 nb_pixel_total : 16351 time to create 1 rle with old method : 0.01765918731689453 length of segment : 133 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 2303 time to create 1 rle with old method : 0.00252532958984375 length of segment : 63 time for calcul the mask position with numpy : 0.0004563331604003906 nb_pixel_total : 25582 time to create 1 rle with old method : 0.02900409698486328 length of segment : 238 time for calcul the mask position with numpy : 0.0018162727355957031 nb_pixel_total : 83908 time to create 1 rle with old method : 0.08765745162963867 length of segment : 666 time for calcul the mask position with numpy : 0.0036966800689697266 nb_pixel_total : 158333 time to create 1 rle with new method : 0.010654449462890625 length of segment : 539 time for calcul the mask position with numpy : 0.0011949539184570312 nb_pixel_total : 41255 time to create 1 rle with old method : 0.043004512786865234 length of segment : 291 time for calcul the mask position with numpy : 0.0016219615936279297 nb_pixel_total : 101269 time to create 1 rle with old method : 0.11240482330322266 length of segment : 491 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 2137 time to create 1 rle with old method : 0.0026161670684814453 length of segment : 46 time for calcul the mask position with numpy : 0.0006346702575683594 nb_pixel_total : 36604 time to create 1 rle with old method : 0.03950142860412598 length of segment : 290 time for calcul the mask position with numpy : 0.00407862663269043 nb_pixel_total : 282776 time to create 1 rle with new method : 0.013476371765136719 length of segment : 986 time for calcul the mask position with numpy : 0.0023856163024902344 nb_pixel_total : 188000 time to create 1 rle with new method : 0.008033037185668945 length of segment : 340 time for calcul the mask position with numpy : 0.0025238990783691406 nb_pixel_total : 171233 time to create 1 rle with new method : 0.00975799560546875 length of segment : 549 time for calcul the mask position with numpy : 0.0006220340728759766 nb_pixel_total : 29452 time to create 1 rle with old method : 0.033045053482055664 length of segment : 211 time for calcul the mask position with numpy : 0.0014619827270507812 nb_pixel_total : 94439 time to create 1 rle with old method : 0.09767699241638184 length of segment : 323 time for calcul the mask position with numpy : 8.821487426757812e-05 nb_pixel_total : 2369 time to create 1 rle with old method : 0.002843141555786133 length of segment : 41 time for calcul the mask position with numpy : 8.916854858398438e-05 nb_pixel_total : 2503 time to create 1 rle with old method : 0.0029561519622802734 length of segment : 80 time for calcul the mask position with numpy : 0.0005424022674560547 nb_pixel_total : 26484 time to create 1 rle with old method : 0.03195548057556152 length of segment : 226 time for calcul the mask position with numpy : 0.00017142295837402344 nb_pixel_total : 3961 time to create 1 rle with old method : 0.004662275314331055 length of segment : 89 time for calcul the mask position with numpy : 0.0007517337799072266 nb_pixel_total : 46615 time to create 1 rle with old method : 0.05095720291137695 length of segment : 246 time for calcul the mask position with numpy : 0.0032110214233398438 nb_pixel_total : 150867 time to create 1 rle with new method : 0.010927915573120117 length of segment : 528 time for calcul the mask position with numpy : 0.0022585391998291016 nb_pixel_total : 104375 time to create 1 rle with old method : 0.10899162292480469 length of segment : 573 time for calcul the mask position with numpy : 0.004230022430419922 nb_pixel_total : 280098 time to create 1 rle with new method : 0.014399290084838867 length of segment : 310 time for calcul the mask position with numpy : 0.0026335716247558594 nb_pixel_total : 165139 time to create 1 rle with new method : 0.00921487808227539 length of segment : 554 time for calcul the mask position with numpy : 0.00031256675720214844 nb_pixel_total : 17838 time to create 1 rle with old method : 0.019759654998779297 length of segment : 187 time for calcul the mask position with numpy : 0.0033316612243652344 nb_pixel_total : 206496 time to create 1 rle with new method : 0.013966798782348633 length of segment : 744 time for calcul the mask position with numpy : 0.04324603080749512 nb_pixel_total : 2113494 time to create 1 rle with new method : 0.12364864349365234 length of segment : 3756 time for calcul the mask position with numpy : 0.016060590744018555 nb_pixel_total : 810890 time to create 1 rle with new method : 0.04266810417175293 length of segment : 1636 time for calcul the mask position with numpy : 0.004164695739746094 nb_pixel_total : 301891 time to create 1 rle with new method : 0.012686967849731445 length of segment : 955 time for calcul the mask position with numpy : 0.0019927024841308594 nb_pixel_total : 115849 time to create 1 rle with old method : 0.11605215072631836 length of segment : 305 time for calcul the mask position with numpy : 0.0002346038818359375 nb_pixel_total : 2730 time to create 1 rle with old method : 0.0029802322387695312 length of segment : 73 time for calcul the mask position with numpy : 0.002899169921875 nb_pixel_total : 96328 time to create 1 rle with old method : 0.10167431831359863 length of segment : 426 time for calcul the mask position with numpy : 0.0036466121673583984 nb_pixel_total : 102542 time to create 1 rle with old method : 0.10917901992797852 length of segment : 446 time for calcul the mask position with numpy : 0.0009036064147949219 nb_pixel_total : 23080 time to create 1 rle with old method : 0.024694204330444336 length of segment : 269 time for calcul the mask position with numpy : 0.0034637451171875 nb_pixel_total : 154864 time to create 1 rle with new method : 0.006638288497924805 length of segment : 546 time for calcul the mask position with numpy : 0.001566171646118164 nb_pixel_total : 69562 time to create 1 rle with old method : 0.07198381423950195 length of segment : 562 time for calcul the mask position with numpy : 0.0023577213287353516 nb_pixel_total : 64758 time to create 1 rle with old method : 0.07078289985656738 length of segment : 410 time for calcul the mask position with numpy : 0.001013040542602539 nb_pixel_total : 29676 time to create 1 rle with old method : 0.03060126304626465 length of segment : 336 time for calcul the mask position with numpy : 0.0049343109130859375 nb_pixel_total : 249067 time to create 1 rle with new method : 0.006987571716308594 length of segment : 691 time for calcul the mask position with numpy : 0.0039179325103759766 nb_pixel_total : 162952 time to create 1 rle with new method : 0.006400346755981445 length of segment : 543 time for calcul the mask position with numpy : 0.0045757293701171875 nb_pixel_total : 125537 time to create 1 rle with old method : 0.13176584243774414 length of segment : 562 time for calcul the mask position with numpy : 0.0008106231689453125 nb_pixel_total : 31102 time to create 1 rle with old method : 0.03329753875732422 length of segment : 247 time for calcul the mask position with numpy : 0.00015306472778320312 nb_pixel_total : 6010 time to create 1 rle with old method : 0.006985902786254883 length of segment : 115 time for calcul the mask position with numpy : 0.0019114017486572266 nb_pixel_total : 28854 time to create 1 rle with old method : 0.031203746795654297 length of segment : 282 time for calcul the mask position with numpy : 0.0008349418640136719 nb_pixel_total : 36038 time to create 1 rle with old method : 0.03791642189025879 length of segment : 172 time for calcul the mask position with numpy : 0.0004911422729492188 nb_pixel_total : 35269 time to create 1 rle with old method : 0.03825950622558594 length of segment : 158 time for calcul the mask position with numpy : 0.002746105194091797 nb_pixel_total : 120596 time to create 1 rle with old method : 0.1500556468963623 length of segment : 372 time for calcul the mask position with numpy : 0.0018794536590576172 nb_pixel_total : 124428 time to create 1 rle with old method : 0.12433171272277832 length of segment : 363 time for calcul the mask position with numpy : 0.0004925727844238281 nb_pixel_total : 9694 time to create 1 rle with old method : 0.011012554168701172 length of segment : 174 time for calcul the mask position with numpy : 0.001444101333618164 nb_pixel_total : 35209 time to create 1 rle with old method : 0.03647184371948242 length of segment : 265 time for calcul the mask position with numpy : 0.00477290153503418 nb_pixel_total : 258453 time to create 1 rle with new method : 0.009322404861450195 length of segment : 804 time for calcul the mask position with numpy : 0.00018715858459472656 nb_pixel_total : 2730 time to create 1 rle with old method : 0.0030736923217773438 length of segment : 73 time for calcul the mask position with numpy : 0.0026023387908935547 nb_pixel_total : 96333 time to create 1 rle with old method : 0.10085225105285645 length of segment : 426 time for calcul the mask position with numpy : 0.0022640228271484375 nb_pixel_total : 102546 time to create 1 rle with old method : 0.10628294944763184 length of segment : 446 time for calcul the mask position with numpy : 0.0009746551513671875 nb_pixel_total : 23084 time to create 1 rle with old method : 0.024518728256225586 length of segment : 269 time for calcul the mask position with numpy : 0.0035500526428222656 nb_pixel_total : 154887 time to create 1 rle with new method : 0.0054149627685546875 length of segment : 546 time for calcul the mask position with numpy : 0.0016033649444580078 nb_pixel_total : 69567 time to create 1 rle with old method : 0.07243466377258301 length of segment : 564 time for calcul the mask position with numpy : 0.002173185348510742 nb_pixel_total : 64786 time to create 1 rle with old method : 0.0683903694152832 length of segment : 409 time for calcul the mask position with numpy : 0.0011649131774902344 nb_pixel_total : 29676 time to create 1 rle with old method : 0.031119823455810547 length of segment : 336 time for calcul the mask position with numpy : 0.0050432682037353516 nb_pixel_total : 249044 time to create 1 rle with new method : 0.00694727897644043 length of segment : 691 time for calcul the mask position with numpy : 0.0042760372161865234 nb_pixel_total : 125422 time to create 1 rle with old method : 0.13020992279052734 length of segment : 561 time for calcul the mask position with numpy : 0.001453399658203125 nb_pixel_total : 31105 time to create 1 rle with old method : 0.032853126525878906 length of segment : 247 time for calcul the mask position with numpy : 0.00019097328186035156 nb_pixel_total : 6008 time to create 1 rle with old method : 0.007171154022216797 length of segment : 115 time for calcul the mask position with numpy : 0.0019614696502685547 nb_pixel_total : 28853 time to create 1 rle with old method : 0.032042741775512695 length of segment : 282 time for calcul the mask position with numpy : 0.0008494853973388672 nb_pixel_total : 36032 time to create 1 rle with old method : 0.04158592224121094 length of segment : 172 time for calcul the mask position with numpy : 0.0004570484161376953 nb_pixel_total : 35256 time to create 1 rle with old method : 0.038846731185913086 length of segment : 158 time for calcul the mask position with numpy : 0.0032694339752197266 nb_pixel_total : 120597 time to create 1 rle with old method : 0.12705516815185547 length of segment : 372 time for calcul the mask position with numpy : 0.002108335494995117 nb_pixel_total : 124422 time to create 1 rle with old method : 0.12725019454956055 length of segment : 363 time for calcul the mask position with numpy : 0.004096508026123047 nb_pixel_total : 161056 time to create 1 rle with new method : 0.007635354995727539 length of segment : 537 time for calcul the mask position with numpy : 0.00043010711669921875 nb_pixel_total : 9679 time to create 1 rle with old method : 0.010005950927734375 length of segment : 174 time for calcul the mask position with numpy : 0.0008063316345214844 nb_pixel_total : 35210 time to create 1 rle with old method : 0.0383601188659668 length of segment : 265 time for calcul the mask position with numpy : 0.004709959030151367 nb_pixel_total : 258610 time to create 1 rle with new method : 0.008368730545043945 length of segment : 804 time for calcul the mask position with numpy : 0.0017824172973632812 nb_pixel_total : 71124 time to create 1 rle with old method : 0.09230232238769531 length of segment : 320 time for calcul the mask position with numpy : 0.001203775405883789 nb_pixel_total : 61351 time to create 1 rle with old method : 0.06604814529418945 length of segment : 323 time for calcul the mask position with numpy : 0.00017404556274414062 nb_pixel_total : 3882 time to create 1 rle with old method : 0.004561662673950195 length of segment : 53 time for calcul the mask position with numpy : 0.00029587745666503906 nb_pixel_total : 2418 time to create 1 rle with old method : 0.0030329227447509766 length of segment : 53 time for calcul the mask position with numpy : 0.00045990943908691406 nb_pixel_total : 11990 time to create 1 rle with old method : 0.013121843338012695 length of segment : 225 time for calcul the mask position with numpy : 0.00014019012451171875 nb_pixel_total : 4341 time to create 1 rle with old method : 0.004572153091430664 length of segment : 84 time for calcul the mask position with numpy : 0.0003857612609863281 nb_pixel_total : 9042 time to create 1 rle with old method : 0.01037454605102539 length of segment : 88 time for calcul the mask position with numpy : 0.0002205371856689453 nb_pixel_total : 3495 time to create 1 rle with old method : 0.0037970542907714844 length of segment : 86 time for calcul the mask position with numpy : 0.0005121231079101562 nb_pixel_total : 7332 time to create 1 rle with old method : 0.007960796356201172 length of segment : 237 time for calcul the mask position with numpy : 0.000888824462890625 nb_pixel_total : 32281 time to create 1 rle with old method : 0.03461456298828125 length of segment : 227 time for calcul the mask position with numpy : 0.0016942024230957031 nb_pixel_total : 22821 time to create 1 rle with old method : 0.024247407913208008 length of segment : 403 time for calcul the mask position with numpy : 0.0034027099609375 nb_pixel_total : 123132 time to create 1 rle with old method : 0.12718629837036133 length of segment : 502 time for calcul the mask position with numpy : 0.0008258819580078125 nb_pixel_total : 28812 time to create 1 rle with old method : 0.03231382369995117 length of segment : 180 time for calcul the mask position with numpy : 0.0017440319061279297 nb_pixel_total : 70688 time to create 1 rle with old method : 0.0758366584777832 length of segment : 319 time for calcul the mask position with numpy : 0.0011851787567138672 nb_pixel_total : 61367 time to create 1 rle with old method : 0.06555747985839844 length of segment : 323 time for calcul the mask position with numpy : 0.00016617774963378906 nb_pixel_total : 3878 time to create 1 rle with old method : 0.004227399826049805 length of segment : 53 time for calcul the mask position with numpy : 0.00029468536376953125 nb_pixel_total : 2420 time to create 1 rle with old method : 0.003217935562133789 length of segment : 53 time for calcul the mask position with numpy : 0.0004901885986328125 nb_pixel_total : 11990 time to create 1 rle with old method : 0.013754844665527344 length of segment : 225 time for calcul the mask position with numpy : 0.0001499652862548828 nb_pixel_total : 4310 time to create 1 rle with old method : 0.004893779754638672 length of segment : 84 time for calcul the mask position with numpy : 0.0003941059112548828 nb_pixel_total : 9037 time to create 1 rle with old method : 0.010655641555786133 length of segment : 88 time for calcul the mask position with numpy : 0.00022029876708984375 nb_pixel_total : 3494 time to create 1 rle with old method : 0.00394749641418457 length of segment : 86 time for calcul the mask position with numpy : 0.0004961490631103516 nb_pixel_total : 7340 time to create 1 rle with old method : 0.008388042449951172 length of segment : 237 time for calcul the mask position with numpy : 0.0008237361907958984 nb_pixel_total : 32280 time to create 1 rle with old method : 0.03304028511047363 length of segment : 227 time for calcul the mask position with numpy : 0.0016558170318603516 nb_pixel_total : 22945 time to create 1 rle with old method : 0.025991201400756836 length of segment : 408 time for calcul the mask position with numpy : 0.003226757049560547 nb_pixel_total : 122812 time to create 1 rle with old method : 0.14687800407409668 length of segment : 502 time for calcul the mask position with numpy : 0.0008111000061035156 nb_pixel_total : 28822 time to create 1 rle with old method : 0.03180527687072754 length of segment : 181 time for calcul the mask position with numpy : 0.0012028217315673828 nb_pixel_total : 57020 time to create 1 rle with old method : 0.06163620948791504 length of segment : 293 time for calcul the mask position with numpy : 0.0006022453308105469 nb_pixel_total : 11631 time to create 1 rle with old method : 0.018530845642089844 length of segment : 205 time for calcul the mask position with numpy : 0.0003364086151123047 nb_pixel_total : 4503 time to create 1 rle with old method : 0.008328437805175781 length of segment : 85 time for calcul the mask position with numpy : 0.0027587413787841797 nb_pixel_total : 65877 time to create 1 rle with old method : 0.10426497459411621 length of segment : 234 time for calcul the mask position with numpy : 0.0013628005981445312 nb_pixel_total : 51240 time to create 1 rle with old method : 0.0585932731628418 length of segment : 300 time for calcul the mask position with numpy : 0.0011184215545654297 nb_pixel_total : 42172 time to create 1 rle with old method : 0.0474705696105957 length of segment : 187 time for calcul the mask position with numpy : 0.001127004623413086 nb_pixel_total : 32262 time to create 1 rle with old method : 0.0361483097076416 length of segment : 172 time for calcul the mask position with numpy : 0.0003833770751953125 nb_pixel_total : 6559 time to create 1 rle with old method : 0.007422208786010742 length of segment : 124 time for calcul the mask position with numpy : 0.0012791156768798828 nb_pixel_total : 40514 time to create 1 rle with old method : 0.04625201225280762 length of segment : 179 time for calcul the mask position with numpy : 0.0003249645233154297 nb_pixel_total : 3861 time to create 1 rle with old method : 0.004530906677246094 length of segment : 107 time for calcul the mask position with numpy : 0.0024678707122802734 nb_pixel_total : 84209 time to create 1 rle with old method : 0.10123872756958008 length of segment : 445 time for calcul the mask position with numpy : 0.0009224414825439453 nb_pixel_total : 44688 time to create 1 rle with old method : 0.06317782402038574 length of segment : 200 time for calcul the mask position with numpy : 0.0010166168212890625 nb_pixel_total : 23983 time to create 1 rle with old method : 0.027101516723632812 length of segment : 268 time for calcul the mask position with numpy : 0.0023660659790039062 nb_pixel_total : 52245 time to create 1 rle with old method : 0.06537938117980957 length of segment : 500 time for calcul the mask position with numpy : 0.006138324737548828 nb_pixel_total : 250668 time to create 1 rle with new method : 0.01060628890991211 length of segment : 522 time for calcul the mask position with numpy : 0.0017514228820800781 nb_pixel_total : 47270 time to create 1 rle with old method : 0.05143928527832031 length of segment : 426 time for calcul the mask position with numpy : 0.0004942417144775391 nb_pixel_total : 11631 time to create 1 rle with old method : 0.013878822326660156 length of segment : 205 time for calcul the mask position with numpy : 0.00023317337036132812 nb_pixel_total : 4504 time to create 1 rle with old method : 0.005541324615478516 length of segment : 85 time for calcul the mask position with numpy : 0.0019576549530029297 nb_pixel_total : 65881 time to create 1 rle with old method : 0.07749247550964355 length of segment : 235 time for calcul the mask position with numpy : 0.001340627670288086 nb_pixel_total : 51239 time to create 1 rle with old method : 0.05670022964477539 length of segment : 300 time for calcul the mask position with numpy : 0.0011680126190185547 nb_pixel_total : 42171 time to create 1 rle with old method : 0.04812908172607422 length of segment : 187 time for calcul the mask position with numpy : 0.0009143352508544922 nb_pixel_total : 32258 time to create 1 rle with old method : 0.03588247299194336 length of segment : 172 time for calcul the mask position with numpy : 0.00032782554626464844 nb_pixel_total : 6563 time to create 1 rle with old method : 0.007457256317138672 length of segment : 124 time for calcul the mask position with numpy : 0.0010554790496826172 nb_pixel_total : 40582 time to create 1 rle with old method : 0.04597067832946777 length of segment : 177 time for calcul the mask position with numpy : 0.00030493736267089844 nb_pixel_total : 3864 time to create 1 rle with old method : 0.004785060882568359 length of segment : 107 time for calcul the mask position with numpy : 0.0024347305297851562 nb_pixel_total : 84213 time to create 1 rle with old method : 0.09862923622131348 length of segment : 446 time for calcul the mask position with numpy : 0.000827789306640625 nb_pixel_total : 44686 time to create 1 rle with old method : 0.04878354072570801 length of segment : 200 time for calcul the mask position with numpy : 0.0009565353393554688 nb_pixel_total : 23974 time to create 1 rle with old method : 0.026389360427856445 length of segment : 268 time for calcul the mask position with numpy : 0.0015838146209716797 nb_pixel_total : 52257 time to create 1 rle with old method : 0.05545520782470703 length of segment : 499 time for calcul the mask position with numpy : 0.005202531814575195 nb_pixel_total : 250674 time to create 1 rle with new method : 0.007685661315917969 length of segment : 522 time for calcul the mask position with numpy : 0.0017590522766113281 nb_pixel_total : 47335 time to create 1 rle with old method : 0.05225825309753418 length of segment : 428 time for calcul the mask position with numpy : 0.00043892860412597656 nb_pixel_total : 11755 time to create 1 rle with old method : 0.013073921203613281 length of segment : 239 time for calcul the mask position with numpy : 0.00024199485778808594 nb_pixel_total : 4438 time to create 1 rle with old method : 0.005057334899902344 length of segment : 86 time for calcul the mask position with numpy : 0.001402139663696289 nb_pixel_total : 59517 time to create 1 rle with old method : 0.06300830841064453 length of segment : 227 time for calcul the mask position with numpy : 0.00118255615234375 nb_pixel_total : 52249 time to create 1 rle with old method : 0.05764603614807129 length of segment : 307 time for calcul the mask position with numpy : 0.0023314952850341797 nb_pixel_total : 82861 time to create 1 rle with old method : 0.08522367477416992 length of segment : 439 time for calcul the mask position with numpy : 0.0002923011779785156 nb_pixel_total : 4346 time to create 1 rle with old method : 0.0050411224365234375 length of segment : 100 time for calcul the mask position with numpy : 0.00031280517578125 nb_pixel_total : 6256 time to create 1 rle with old method : 0.007157564163208008 length of segment : 128 time for calcul the mask position with numpy : 0.0013806819915771484 nb_pixel_total : 48323 time to create 1 rle with old method : 0.053696632385253906 length of segment : 466 time for calcul the mask position with numpy : 0.0016515254974365234 nb_pixel_total : 53866 time to create 1 rle with old method : 0.05933022499084473 length of segment : 464 time for calcul the mask position with numpy : 0.006232738494873047 nb_pixel_total : 263640 time to create 1 rle with new method : 0.010968923568725586 length of segment : 657 time for calcul the mask position with numpy : 0.005376100540161133 nb_pixel_total : 248347 time to create 1 rle with new method : 0.010646581649780273 length of segment : 654 time for calcul the mask position with numpy : 0.0014488697052001953 nb_pixel_total : 51544 time to create 1 rle with old method : 0.05510425567626953 length of segment : 314 time for calcul the mask position with numpy : 0.000980377197265625 nb_pixel_total : 42957 time to create 1 rle with old method : 0.04701733589172363 length of segment : 191 time for calcul the mask position with numpy : 0.001821756362915039 nb_pixel_total : 88466 time to create 1 rle with old method : 0.09315633773803711 length of segment : 336 time for calcul the mask position with numpy : 0.0007522106170654297 nb_pixel_total : 49645 time to create 1 rle with old method : 0.05464816093444824 length of segment : 432 time for calcul the mask position with numpy : 0.0004973411560058594 nb_pixel_total : 11752 time to create 1 rle with old method : 0.01333928108215332 length of segment : 239 time for calcul the mask position with numpy : 0.0002465248107910156 nb_pixel_total : 4442 time to create 1 rle with old method : 0.005144834518432617 length of segment : 86 time for calcul the mask position with numpy : 0.0014243125915527344 nb_pixel_total : 59513 time to create 1 rle with old method : 0.06602978706359863 length of segment : 227 time for calcul the mask position with numpy : 0.0011849403381347656 nb_pixel_total : 52254 time to create 1 rle with old method : 0.0576786994934082 length of segment : 308 time for calcul the mask position with numpy : 0.0026099681854248047 nb_pixel_total : 82868 time to create 1 rle with old method : 0.09112262725830078 length of segment : 439 time for calcul the mask position with numpy : 0.0002956390380859375 nb_pixel_total : 4214 time to create 1 rle with old method : 0.005273342132568359 length of segment : 100 time for calcul the mask position with numpy : 0.0003459453582763672 nb_pixel_total : 6245 time to create 1 rle with old method : 0.0066301822662353516 length of segment : 127 time for calcul the mask position with numpy : 0.0012972354888916016 nb_pixel_total : 48307 time to create 1 rle with old method : 0.05159926414489746 length of segment : 466 time for calcul the mask position with numpy : 0.0016298294067382812 nb_pixel_total : 54088 time to create 1 rle with old method : 0.05811190605163574 length of segment : 461 time for calcul the mask position with numpy : 0.006561994552612305 nb_pixel_total : 263592 time to create 1 rle with new method : 0.011681795120239258 length of segment : 657 time for calcul the mask position with numpy : 0.0054705142974853516 nb_pixel_total : 247901 time to create 1 rle with new method : 0.009414911270141602 length of segment : 657 time for calcul the mask position with numpy : 0.0007572174072265625 nb_pixel_total : 51550 time to create 1 rle with old method : 0.05626535415649414 length of segment : 314 time for calcul the mask position with numpy : 0.0009152889251708984 nb_pixel_total : 42953 time to create 1 rle with old method : 0.04826211929321289 length of segment : 191 time for calcul the mask position with numpy : 0.0017747879028320312 nb_pixel_total : 87665 time to create 1 rle with old method : 0.09239625930786133 length of segment : 333 time for calcul the mask position with numpy : 0.0006718635559082031 nb_pixel_total : 49667 time to create 1 rle with old method : 0.055684566497802734 length of segment : 432 time for calcul the mask position with numpy : 0.00070953369140625 nb_pixel_total : 16172 time to create 1 rle with old method : 0.018358945846557617 length of segment : 235 time for calcul the mask position with numpy : 0.005159139633178711 nb_pixel_total : 235751 time to create 1 rle with new method : 0.007245540618896484 length of segment : 464 time for calcul the mask position with numpy : 0.0016677379608154297 nb_pixel_total : 50777 time to create 1 rle with old method : 0.05600714683532715 length of segment : 280 time for calcul the mask position with numpy : 0.001999378204345703 nb_pixel_total : 60234 time to create 1 rle with old method : 0.06344819068908691 length of segment : 361 time for calcul the mask position with numpy : 0.0023229122161865234 nb_pixel_total : 64700 time to create 1 rle with old method : 0.07039570808410645 length of segment : 424 time for calcul the mask position with numpy : 0.0002605915069580078 nb_pixel_total : 4030 time to create 1 rle with old method : 0.004993438720703125 length of segment : 88 time for calcul the mask position with numpy : 0.0022516250610351562 nb_pixel_total : 64372 time to create 1 rle with old method : 0.07121801376342773 length of segment : 264 time for calcul the mask position with numpy : 0.0008144378662109375 nb_pixel_total : 25138 time to create 1 rle with old method : 0.028244972229003906 length of segment : 195 time for calcul the mask position with numpy : 0.0046308040618896484 nb_pixel_total : 168136 time to create 1 rle with new method : 0.006379365921020508 length of segment : 416 time for calcul the mask position with numpy : 0.0004901885986328125 nb_pixel_total : 18056 time to create 1 rle with old method : 0.02214527130126953 length of segment : 170 time for calcul the mask position with numpy : 0.0002727508544921875 nb_pixel_total : 3256 time to create 1 rle with old method : 0.003925323486328125 length of segment : 146 time for calcul the mask position with numpy : 0.0004341602325439453 nb_pixel_total : 7894 time to create 1 rle with old method : 0.009211063385009766 length of segment : 149 time for calcul the mask position with numpy : 0.007345438003540039 nb_pixel_total : 238002 time to create 1 rle with new method : 0.011142969131469727 length of segment : 772 time for calcul the mask position with numpy : 0.0012137889862060547 nb_pixel_total : 46886 time to create 1 rle with old method : 0.051802873611450195 length of segment : 327 time for calcul the mask position with numpy : 0.0003027915954589844 nb_pixel_total : 5656 time to create 1 rle with old method : 0.006328582763671875 length of segment : 101 time for calcul the mask position with numpy : 0.006569862365722656 nb_pixel_total : 167025 time to create 1 rle with new method : 0.012235403060913086 length of segment : 1045 time for calcul the mask position with numpy : 0.001413106918334961 nb_pixel_total : 52532 time to create 1 rle with old method : 0.05520200729370117 length of segment : 224 time for calcul the mask position with numpy : 0.0011224746704101562 nb_pixel_total : 47836 time to create 1 rle with old method : 0.05102729797363281 length of segment : 300 time for calcul the mask position with numpy : 0.00019884109497070312 nb_pixel_total : 4237 time to create 1 rle with old method : 0.004437685012817383 length of segment : 77 time for calcul the mask position with numpy : 0.0043735504150390625 nb_pixel_total : 224842 time to create 1 rle with new method : 0.006577014923095703 length of segment : 599 time for calcul the mask position with numpy : 0.0006275177001953125 nb_pixel_total : 16151 time to create 1 rle with old method : 0.017215490341186523 length of segment : 235 time for calcul the mask position with numpy : 0.005561351776123047 nb_pixel_total : 235757 time to create 1 rle with new method : 0.00703120231628418 length of segment : 464 time for calcul the mask position with numpy : 0.0015285015106201172 nb_pixel_total : 50769 time to create 1 rle with old method : 0.04960441589355469 length of segment : 281 time for calcul the mask position with numpy : 0.0018014907836914062 nb_pixel_total : 61820 time to create 1 rle with old method : 0.06504273414611816 length of segment : 379 time for calcul the mask position with numpy : 0.0022125244140625 nb_pixel_total : 64916 time to create 1 rle with old method : 0.07053184509277344 length of segment : 426 time for calcul the mask position with numpy : 0.0002276897430419922 nb_pixel_total : 4030 time to create 1 rle with old method : 0.004209756851196289 length of segment : 88 time for calcul the mask position with numpy : 0.002051830291748047 nb_pixel_total : 64373 time to create 1 rle with old method : 0.06801366806030273 length of segment : 264 time for calcul the mask position with numpy : 0.0008111000061035156 nb_pixel_total : 25141 time to create 1 rle with old method : 0.0276944637298584 length of segment : 195 time for calcul the mask position with numpy : 0.004261016845703125 nb_pixel_total : 168014 time to create 1 rle with new method : 0.005971193313598633 length of segment : 415 time for calcul the mask position with numpy : 0.0004489421844482422 nb_pixel_total : 18056 time to create 1 rle with old method : 0.019105911254882812 length of segment : 170 time for calcul the mask position with numpy : 0.00026798248291015625 nb_pixel_total : 3257 time to create 1 rle with old method : 0.003576040267944336 length of segment : 146 time for calcul the mask position with numpy : 0.00040841102600097656 nb_pixel_total : 7893 time to create 1 rle with old method : 0.00823521614074707 length of segment : 149 time for calcul the mask position with numpy : 0.0063934326171875 nb_pixel_total : 238010 time to create 1 rle with new method : 0.010191917419433594 length of segment : 772 time for calcul the mask position with numpy : 0.0011930465698242188 nb_pixel_total : 46890 time to create 1 rle with old method : 0.05044722557067871 length of segment : 327 time for calcul the mask position with numpy : 0.006091594696044922 nb_pixel_total : 167457 time to create 1 rle with new method : 0.011137962341308594 length of segment : 1039 time for calcul the mask position with numpy : 0.0002846717834472656 nb_pixel_total : 5649 time to create 1 rle with old method : 0.006238222122192383 length of segment : 100 time for calcul the mask position with numpy : 0.0013718605041503906 nb_pixel_total : 52592 time to create 1 rle with old method : 0.05431413650512695 length of segment : 222 time for calcul the mask position with numpy : 0.0010733604431152344 nb_pixel_total : 47834 time to create 1 rle with old method : 0.05207347869873047 length of segment : 299 time for calcul the mask position with numpy : 0.004290103912353516 nb_pixel_total : 224932 time to create 1 rle with new method : 0.0065348148345947266 length of segment : 599 time for calcul the mask position with numpy : 0.005036830902099609 nb_pixel_total : 237023 time to create 1 rle with new method : 0.006708383560180664 length of segment : 568 time for calcul the mask position with numpy : 0.0002818107604980469 nb_pixel_total : 6003 time to create 1 rle with old method : 0.006401538848876953 length of segment : 94 time for calcul the mask position with numpy : 0.002063274383544922 nb_pixel_total : 67608 time to create 1 rle with old method : 0.07627224922180176 length of segment : 254 time for calcul the mask position with numpy : 0.0005612373352050781 nb_pixel_total : 12374 time to create 1 rle with old method : 0.013828277587890625 length of segment : 260 time for calcul the mask position with numpy : 0.004218339920043945 nb_pixel_total : 169703 time to create 1 rle with new method : 0.005506753921508789 length of segment : 466 time for calcul the mask position with numpy : 0.0005834102630615234 nb_pixel_total : 13376 time to create 1 rle with old method : 0.015065431594848633 length of segment : 189 time for calcul the mask position with numpy : 0.0019409656524658203 nb_pixel_total : 58607 time to create 1 rle with old method : 0.058335304260253906 length of segment : 397 time for calcul the mask position with numpy : 0.002255678176879883 nb_pixel_total : 86526 time to create 1 rle with old method : 0.09196782112121582 length of segment : 420 time for calcul the mask position with numpy : 0.00147247314453125 nb_pixel_total : 49740 time to create 1 rle with old method : 0.04949188232421875 length of segment : 267 time for calcul the mask position with numpy : 0.006877422332763672 nb_pixel_total : 117098 time to create 1 rle with old method : 0.12461590766906738 length of segment : 883 time for calcul the mask position with numpy : 0.0013098716735839844 nb_pixel_total : 52056 time to create 1 rle with old method : 0.05525684356689453 length of segment : 304 time for calcul the mask position with numpy : 0.0008339881896972656 nb_pixel_total : 46223 time to create 1 rle with old method : 0.046819448471069336 length of segment : 312 time for calcul the mask position with numpy : 0.0009436607360839844 nb_pixel_total : 37982 time to create 1 rle with old method : 0.04203152656555176 length of segment : 271 time for calcul the mask position with numpy : 0.004584789276123047 nb_pixel_total : 231046 time to create 1 rle with new method : 0.007569551467895508 length of segment : 574 time for calcul the mask position with numpy : 0.0007469654083251953 nb_pixel_total : 36566 time to create 1 rle with old method : 0.04088234901428223 length of segment : 557 time for calcul the mask position with numpy : 0.0008873939514160156 nb_pixel_total : 25927 time to create 1 rle with old method : 0.027892589569091797 length of segment : 190 time for calcul the mask position with numpy : 0.000255584716796875 nb_pixel_total : 4752 time to create 1 rle with old method : 0.005453586578369141 length of segment : 75 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 1408 time to create 1 rle with old method : 0.0016448497772216797 length of segment : 49 time for calcul the mask position with numpy : 0.00023317337036132812 nb_pixel_total : 5865 time to create 1 rle with old method : 0.006953716278076172 length of segment : 136 time for calcul the mask position with numpy : 0.002076864242553711 nb_pixel_total : 120341 time to create 1 rle with old method : 0.12713623046875 length of segment : 526 time for calcul the mask position with numpy : 0.0010097026824951172 nb_pixel_total : 27385 time to create 1 rle with old method : 0.03890347480773926 length of segment : 221 time for calcul the mask position with numpy : 0.00629425048828125 nb_pixel_total : 243927 time to create 1 rle with new method : 0.007781982421875 length of segment : 590 time for calcul the mask position with numpy : 0.0002694129943847656 nb_pixel_total : 6004 time to create 1 rle with old method : 0.006256818771362305 length of segment : 94 time for calcul the mask position with numpy : 0.0019152164459228516 nb_pixel_total : 67914 time to create 1 rle with old method : 0.07554221153259277 length of segment : 255 time for calcul the mask position with numpy : 0.0005288124084472656 nb_pixel_total : 12360 time to create 1 rle with old method : 0.01420903205871582 length of segment : 259 time for calcul the mask position with numpy : 0.004292964935302734 nb_pixel_total : 169639 time to create 1 rle with new method : 0.006083250045776367 length of segment : 464 time for calcul the mask position with numpy : 0.002633333206176758 nb_pixel_total : 58425 time to create 1 rle with old method : 0.06831598281860352 length of segment : 399 time for calcul the mask position with numpy : 0.00032329559326171875 nb_pixel_total : 13657 time to create 1 rle with old method : 0.015329360961914062 length of segment : 191 time for calcul the mask position with numpy : 0.0022864341735839844 nb_pixel_total : 86518 time to create 1 rle with old method : 0.09626412391662598 length of segment : 420 time for calcul the mask position with numpy : 0.0014948844909667969 nb_pixel_total : 49740 time to create 1 rle with old method : 0.05217909812927246 length of segment : 267 time for calcul the mask position with numpy : 0.007098674774169922 nb_pixel_total : 119872 time to create 1 rle with old method : 0.13225603103637695 length of segment : 882 time for calcul the mask position with numpy : 0.001306772232055664 nb_pixel_total : 52054 time to create 1 rle with old method : 0.056377410888671875 length of segment : 304 time for calcul the mask position with numpy : 0.0009338855743408203 nb_pixel_total : 37980 time to create 1 rle with old method : 0.040685415267944336 length of segment : 271 time for calcul the mask position with numpy : 0.00487065315246582 nb_pixel_total : 231007 time to create 1 rle with new method : 0.0075147151947021484 length of segment : 573 time for calcul the mask position with numpy : 0.0007960796356201172 nb_pixel_total : 36687 time to create 1 rle with old method : 0.04216814041137695 length of segment : 554 time for calcul the mask position with numpy : 0.0008847713470458984 nb_pixel_total : 25933 time to create 1 rle with old method : 0.028790712356567383 length of segment : 190 time for calcul the mask position with numpy : 0.00027108192443847656 nb_pixel_total : 4752 time to create 1 rle with old method : 0.005246877670288086 length of segment : 75 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 1408 time to create 1 rle with old method : 0.001705169677734375 length of segment : 49 time for calcul the mask position with numpy : 0.0002346038818359375 nb_pixel_total : 5873 time to create 1 rle with old method : 0.0068628787994384766 length of segment : 135 time for calcul the mask position with numpy : 0.0011546611785888672 nb_pixel_total : 52137 time to create 1 rle with old method : 0.05673837661743164 length of segment : 396 time for calcul the mask position with numpy : 0.0021915435791015625 nb_pixel_total : 120351 time to create 1 rle with old method : 0.12349724769592285 length of segment : 526 time for calcul the mask position with numpy : 0.0009789466857910156 nb_pixel_total : 27391 time to create 1 rle with old method : 0.029961109161376953 length of segment : 221 time for calcul the mask position with numpy : 0.0017364025115966797 nb_pixel_total : 71435 time to create 1 rle with old method : 0.07044529914855957 length of segment : 348 time for calcul the mask position with numpy : 0.004129648208618164 nb_pixel_total : 183366 time to create 1 rle with new method : 0.006998538970947266 length of segment : 822 time for calcul the mask position with numpy : 0.000362396240234375 nb_pixel_total : 6851 time to create 1 rle with old method : 0.007318258285522461 length of segment : 92 time for calcul the mask position with numpy : 0.0012309551239013672 nb_pixel_total : 35921 time to create 1 rle with old method : 0.039466142654418945 length of segment : 268 time for calcul the mask position with numpy : 0.003920793533325195 nb_pixel_total : 101468 time to create 1 rle with old method : 0.11309599876403809 length of segment : 561 time for calcul the mask position with numpy : 0.0007693767547607422 nb_pixel_total : 27184 time to create 1 rle with old method : 0.028853178024291992 length of segment : 255 time for calcul the mask position with numpy : 0.0024995803833007812 nb_pixel_total : 119724 time to create 1 rle with old method : 0.12174677848815918 length of segment : 628 time for calcul the mask position with numpy : 0.0006012916564941406 nb_pixel_total : 22309 time to create 1 rle with old method : 0.023694515228271484 length of segment : 239 time for calcul the mask position with numpy : 0.0001480579376220703 nb_pixel_total : 4457 time to create 1 rle with old method : 0.005303859710693359 length of segment : 84 time for calcul the mask position with numpy : 0.0006415843963623047 nb_pixel_total : 35569 time to create 1 rle with old method : 0.03893446922302246 length of segment : 269 time for calcul the mask position with numpy : 0.0009148120880126953 nb_pixel_total : 40902 time to create 1 rle with old method : 0.04273843765258789 length of segment : 314 time for calcul the mask position with numpy : 0.0006320476531982422 nb_pixel_total : 25372 time to create 1 rle with old method : 0.027458667755126953 length of segment : 242 time for calcul the mask position with numpy : 0.0030584335327148438 nb_pixel_total : 123489 time to create 1 rle with old method : 0.13645124435424805 length of segment : 789 time for calcul the mask position with numpy : 0.00023245811462402344 nb_pixel_total : 1929 time to create 1 rle with old method : 0.002307891845703125 length of segment : 97 time for calcul the mask position with numpy : 0.003472566604614258 nb_pixel_total : 125973 time to create 1 rle with old method : 0.1453077793121338 length of segment : 455 time for calcul the mask position with numpy : 0.0005033016204833984 nb_pixel_total : 17035 time to create 1 rle with old method : 0.021672964096069336 length of segment : 104 time for calcul the mask position with numpy : 0.00030684471130371094 nb_pixel_total : 9726 time to create 1 rle with old method : 0.011013507843017578 length of segment : 234 time for calcul the mask position with numpy : 0.0001220703125 nb_pixel_total : 1557 time to create 1 rle with old method : 0.0017604827880859375 length of segment : 52 time for calcul the mask position with numpy : 0.0004601478576660156 nb_pixel_total : 13116 time to create 1 rle with old method : 0.014087915420532227 length of segment : 134 time for calcul the mask position with numpy : 0.0001537799835205078 nb_pixel_total : 3162 time to create 1 rle with old method : 0.0035517215728759766 length of segment : 57 time for calcul the mask position with numpy : 0.0016105175018310547 nb_pixel_total : 46836 time to create 1 rle with old method : 0.05151939392089844 length of segment : 353 time for calcul the mask position with numpy : 0.0031366348266601562 nb_pixel_total : 78420 time to create 1 rle with old method : 0.08676791191101074 length of segment : 388 time for calcul the mask position with numpy : 0.000982522964477539 nb_pixel_total : 25932 time to create 1 rle with old method : 0.027847766876220703 length of segment : 281 time for calcul the mask position with numpy : 0.0010876655578613281 nb_pixel_total : 42044 time to create 1 rle with old method : 0.047063589096069336 length of segment : 340 time for calcul the mask position with numpy : 0.0011074542999267578 nb_pixel_total : 38716 time to create 1 rle with old method : 0.04182863235473633 length of segment : 211 time for calcul the mask position with numpy : 0.0015451908111572266 nb_pixel_total : 71485 time to create 1 rle with old method : 0.07684946060180664 length of segment : 246 time for calcul the mask position with numpy : 0.006809234619140625 nb_pixel_total : 387071 time to create 1 rle with new method : 0.009410858154296875 length of segment : 834 time for calcul the mask position with numpy : 0.001600027084350586 nb_pixel_total : 72657 time to create 1 rle with old method : 0.1016697883605957 length of segment : 251 time for calcul the mask position with numpy : 0.0008080005645751953 nb_pixel_total : 23150 time to create 1 rle with old method : 0.026451826095581055 length of segment : 219 time for calcul the mask position with numpy : 0.000179290771484375 nb_pixel_total : 2555 time to create 1 rle with old method : 0.002886533737182617 length of segment : 86 time for calcul the mask position with numpy : 0.002714395523071289 nb_pixel_total : 94070 time to create 1 rle with old method : 0.09835577011108398 length of segment : 365 time for calcul the mask position with numpy : 0.0011126995086669922 nb_pixel_total : 70515 time to create 1 rle with old method : 0.07687520980834961 length of segment : 243 time for calcul the mask position with numpy : 0.000732421875 nb_pixel_total : 9440 time to create 1 rle with old method : 0.011396646499633789 length of segment : 157 time for calcul the mask position with numpy : 0.002057790756225586 nb_pixel_total : 110496 time to create 1 rle with old method : 0.1256415843963623 length of segment : 406 time for calcul the mask position with numpy : 0.007203817367553711 nb_pixel_total : 321757 time to create 1 rle with new method : 0.011865854263305664 length of segment : 426 time for calcul the mask position with numpy : 0.0003497600555419922 nb_pixel_total : 6703 time to create 1 rle with old method : 0.00832819938659668 length of segment : 100 time for calcul the mask position with numpy : 0.0007178783416748047 nb_pixel_total : 29106 time to create 1 rle with old method : 0.03422832489013672 length of segment : 195 time for calcul the mask position with numpy : 0.0006463527679443359 nb_pixel_total : 25088 time to create 1 rle with old method : 0.027827739715576172 length of segment : 155 time for calcul the mask position with numpy : 0.0015349388122558594 nb_pixel_total : 40400 time to create 1 rle with old method : 0.04695463180541992 length of segment : 635 time for calcul the mask position with numpy : 0.00013065338134765625 nb_pixel_total : 2020 time to create 1 rle with old method : 0.0022847652435302734 length of segment : 77 time for calcul the mask position with numpy : 0.0003147125244140625 nb_pixel_total : 17012 time to create 1 rle with old method : 0.019796133041381836 length of segment : 215 time for calcul the mask position with numpy : 0.0022094249725341797 nb_pixel_total : 70876 time to create 1 rle with old method : 0.07250261306762695 length of segment : 415 time for calcul the mask position with numpy : 0.0003364086151123047 nb_pixel_total : 7045 time to create 1 rle with old method : 0.008311986923217773 length of segment : 109 time for calcul the mask position with numpy : 0.0003101825714111328 nb_pixel_total : 4549 time to create 1 rle with old method : 0.005446910858154297 length of segment : 102 time for calcul the mask position with numpy : 0.0004253387451171875 nb_pixel_total : 10191 time to create 1 rle with old method : 0.011493682861328125 length of segment : 83 time for calcul the mask position with numpy : 0.0032656192779541016 nb_pixel_total : 100663 time to create 1 rle with old method : 0.10965347290039062 length of segment : 397 time for calcul the mask position with numpy : 0.0011982917785644531 nb_pixel_total : 25871 time to create 1 rle with old method : 0.029867172241210938 length of segment : 229 time for calcul the mask position with numpy : 0.004053354263305664 nb_pixel_total : 130970 time to create 1 rle with old method : 0.1482534408569336 length of segment : 658 time for calcul the mask position with numpy : 0.005197048187255859 nb_pixel_total : 129420 time to create 1 rle with old method : 0.13840079307556152 length of segment : 482 time for calcul the mask position with numpy : 0.0053594112396240234 nb_pixel_total : 160422 time to create 1 rle with new method : 0.007999658584594727 length of segment : 618 time for calcul the mask position with numpy : 0.0004279613494873047 nb_pixel_total : 19947 time to create 1 rle with old method : 0.020914316177368164 length of segment : 235 time for calcul the mask position with numpy : 0.0024328231811523438 nb_pixel_total : 110395 time to create 1 rle with old method : 0.12363743782043457 length of segment : 351 time for calcul the mask position with numpy : 0.0009763240814208984 nb_pixel_total : 59764 time to create 1 rle with old method : 0.06724238395690918 length of segment : 338 time for calcul the mask position with numpy : 0.006172657012939453 nb_pixel_total : 131422 time to create 1 rle with old method : 0.14516854286193848 length of segment : 553 time for calcul the mask position with numpy : 0.0009984970092773438 nb_pixel_total : 27356 time to create 1 rle with old method : 0.03070354461669922 length of segment : 226 time for calcul the mask position with numpy : 0.002775907516479492 nb_pixel_total : 122936 time to create 1 rle with old method : 0.1318526268005371 length of segment : 546 time for calcul the mask position with numpy : 0.0009436607360839844 nb_pixel_total : 31841 time to create 1 rle with old method : 0.03416609764099121 length of segment : 158 time for calcul the mask position with numpy : 0.0002722740173339844 nb_pixel_total : 4202 time to create 1 rle with old method : 0.004795551300048828 length of segment : 91 time for calcul the mask position with numpy : 0.0017921924591064453 nb_pixel_total : 41541 time to create 1 rle with old method : 0.04686546325683594 length of segment : 265 time for calcul the mask position with numpy : 0.0008285045623779297 nb_pixel_total : 21566 time to create 1 rle with old method : 0.027094602584838867 length of segment : 211 time for calcul the mask position with numpy : 0.00040268898010253906 nb_pixel_total : 24607 time to create 1 rle with old method : 0.027639150619506836 length of segment : 215 time for calcul the mask position with numpy : 0.0035598278045654297 nb_pixel_total : 133000 time to create 1 rle with old method : 0.13973236083984375 length of segment : 459 time for calcul the mask position with numpy : 0.0003173351287841797 nb_pixel_total : 16309 time to create 1 rle with old method : 0.018416166305541992 length of segment : 117 time for calcul the mask position with numpy : 0.0003688335418701172 nb_pixel_total : 9086 time to create 1 rle with old method : 0.010175943374633789 length of segment : 105 time for calcul the mask position with numpy : 0.00077056884765625 nb_pixel_total : 12137 time to create 1 rle with old method : 0.013809919357299805 length of segment : 205 time for calcul the mask position with numpy : 0.0003228187561035156 nb_pixel_total : 4562 time to create 1 rle with old method : 0.005415678024291992 length of segment : 91 time for calcul the mask position with numpy : 0.0025987625122070312 nb_pixel_total : 149981 time to create 1 rle with old method : 0.16450905799865723 length of segment : 587 time for calcul the mask position with numpy : 0.0007555484771728516 nb_pixel_total : 33216 time to create 1 rle with old method : 0.03643631935119629 length of segment : 232 time for calcul the mask position with numpy : 0.003324270248413086 nb_pixel_total : 161110 time to create 1 rle with new method : 0.008940458297729492 length of segment : 606 time for calcul the mask position with numpy : 0.0029587745666503906 nb_pixel_total : 90829 time to create 1 rle with old method : 0.10051441192626953 length of segment : 352 time for calcul the mask position with numpy : 0.0003879070281982422 nb_pixel_total : 7049 time to create 1 rle with old method : 0.008031129837036133 length of segment : 145 time for calcul the mask position with numpy : 0.0013327598571777344 nb_pixel_total : 43134 time to create 1 rle with old method : 0.04474639892578125 length of segment : 347 time for calcul the mask position with numpy : 0.00017499923706054688 nb_pixel_total : 2516 time to create 1 rle with old method : 0.002668142318725586 length of segment : 80 time for calcul the mask position with numpy : 0.0033142566680908203 nb_pixel_total : 106572 time to create 1 rle with old method : 0.11965203285217285 length of segment : 406 time for calcul the mask position with numpy : 0.0048503875732421875 nb_pixel_total : 95267 time to create 1 rle with old method : 0.12939095497131348 length of segment : 471 time for calcul the mask position with numpy : 0.0002982616424560547 nb_pixel_total : 5497 time to create 1 rle with old method : 0.006636142730712891 length of segment : 171 time for calcul the mask position with numpy : 0.00018906593322753906 nb_pixel_total : 3623 time to create 1 rle with old method : 0.004602193832397461 length of segment : 58 time for calcul the mask position with numpy : 0.006164073944091797 nb_pixel_total : 305855 time to create 1 rle with new method : 0.008377552032470703 length of segment : 531 time for calcul the mask position with numpy : 0.005898952484130859 nb_pixel_total : 120028 time to create 1 rle with old method : 0.12198948860168457 length of segment : 732 time for calcul the mask position with numpy : 0.0011320114135742188 nb_pixel_total : 43770 time to create 1 rle with old method : 0.04797554016113281 length of segment : 249 time for calcul the mask position with numpy : 0.0009775161743164062 nb_pixel_total : 51959 time to create 1 rle with old method : 0.053694725036621094 length of segment : 276 time for calcul the mask position with numpy : 0.0019941329956054688 nb_pixel_total : 98445 time to create 1 rle with old method : 0.10676407814025879 length of segment : 416 time for calcul the mask position with numpy : 0.007376194000244141 nb_pixel_total : 351893 time to create 1 rle with new method : 0.010680913925170898 length of segment : 746 time for calcul the mask position with numpy : 0.0018074512481689453 nb_pixel_total : 58492 time to create 1 rle with old method : 0.06220364570617676 length of segment : 305 time for calcul the mask position with numpy : 0.0049211978912353516 nb_pixel_total : 107413 time to create 1 rle with old method : 0.11573338508605957 length of segment : 678 time for calcul the mask position with numpy : 0.00019049644470214844 nb_pixel_total : 2128 time to create 1 rle with old method : 0.002714395523071289 length of segment : 65 time for calcul the mask position with numpy : 0.0001659393310546875 nb_pixel_total : 5610 time to create 1 rle with old method : 0.006701469421386719 length of segment : 89 time for calcul the mask position with numpy : 0.0005700588226318359 nb_pixel_total : 12820 time to create 1 rle with old method : 0.014460086822509766 length of segment : 200 time for calcul the mask position with numpy : 0.0003936290740966797 nb_pixel_total : 5302 time to create 1 rle with old method : 0.006186246871948242 length of segment : 156 time for calcul the mask position with numpy : 0.0002243518829345703 nb_pixel_total : 3368 time to create 1 rle with old method : 0.0038254261016845703 length of segment : 98 time for calcul the mask position with numpy : 0.0004451274871826172 nb_pixel_total : 10462 time to create 1 rle with old method : 0.011317014694213867 length of segment : 140 time for calcul the mask position with numpy : 0.0002522468566894531 nb_pixel_total : 10753 time to create 1 rle with old method : 0.011984109878540039 length of segment : 149 time for calcul the mask position with numpy : 0.0007107257843017578 nb_pixel_total : 24392 time to create 1 rle with old method : 0.026639461517333984 length of segment : 236 time for calcul the mask position with numpy : 0.00016236305236816406 nb_pixel_total : 2362 time to create 1 rle with old method : 0.0029039382934570312 length of segment : 76 time for calcul the mask position with numpy : 0.00013566017150878906 nb_pixel_total : 2367 time to create 1 rle with old method : 0.0026693344116210938 length of segment : 61 time for calcul the mask position with numpy : 0.0009648799896240234 nb_pixel_total : 41428 time to create 1 rle with old method : 0.044892311096191406 length of segment : 226 time for calcul the mask position with numpy : 0.00031566619873046875 nb_pixel_total : 9610 time to create 1 rle with old method : 0.011329412460327148 length of segment : 73 time for calcul the mask position with numpy : 0.0005981922149658203 nb_pixel_total : 11121 time to create 1 rle with old method : 0.012923002243041992 length of segment : 166 time for calcul the mask position with numpy : 0.00030541419982910156 nb_pixel_total : 7442 time to create 1 rle with old method : 0.008723020553588867 length of segment : 87 time for calcul the mask position with numpy : 0.0002295970916748047 nb_pixel_total : 4394 time to create 1 rle with old method : 0.004892110824584961 length of segment : 86 time for calcul the mask position with numpy : 0.0023202896118164062 nb_pixel_total : 113093 time to create 1 rle with old method : 0.12472772598266602 length of segment : 407 time for calcul the mask position with numpy : 0.0022568702697753906 nb_pixel_total : 97565 time to create 1 rle with old method : 0.1084451675415039 length of segment : 497 time for calcul the mask position with numpy : 0.0027227401733398438 nb_pixel_total : 115386 time to create 1 rle with old method : 0.12337565422058105 length of segment : 435 time for calcul the mask position with numpy : 0.0046613216400146484 nb_pixel_total : 211716 time to create 1 rle with new method : 0.007599830627441406 length of segment : 679 time for calcul the mask position with numpy : 0.00011730194091796875 nb_pixel_total : 4431 time to create 1 rle with old method : 0.004889011383056641 length of segment : 69 time for calcul the mask position with numpy : 0.005554676055908203 nb_pixel_total : 290866 time to create 1 rle with new method : 0.00873112678527832 length of segment : 403 time for calcul the mask position with numpy : 0.0015070438385009766 nb_pixel_total : 96031 time to create 1 rle with old method : 0.10395669937133789 length of segment : 498 time for calcul the mask position with numpy : 0.004605293273925781 nb_pixel_total : 269691 time to create 1 rle with new method : 0.009983301162719727 length of segment : 393 time for calcul the mask position with numpy : 0.001642465591430664 nb_pixel_total : 91849 time to create 1 rle with old method : 0.09902429580688477 length of segment : 307 time spent for convertir_results : 31.58127188682556 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 351 chid ids of type : 3663 Number RLEs to save : 115705 save missing photos in datou_result : time spend for datou_step_exec : 98.69622230529785 time spend to save output : 11.130146980285645 total time spend for step 1 : 109.8263692855835 step2:crop_condition Tue Feb 4 12:28:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3663 Loading chi in step crop for list_pids : 20 ! batch 1 Loaded 351 chid ids of type : 3663 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : teint_dans_la_masse param for this class : {'min_score': 0.7} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 18 About to insert : list_path_to_insert length 18 new photo from crops ! we have finished the crop for the class : teint_dans_la_masse begin to crop the class : autre_refus param for this class : {'min_score': 0.5} filtre for class : autre_refus hashtag_id of this class : 2107752406 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 35 About to insert : list_path_to_insert length 35 new photo from crops ! we have finished the crop for the class : autre_refus begin to crop the class : carton_gris param for this class : {'min_score': 0.5} filtre for class : carton_gris hashtag_id of this class : 2107753020 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 19 About to insert : list_path_to_insert length 19 new photo from crops ! we have finished the crop for the class : carton_gris begin to crop the class : cartonnette param for this class : {'min_score': 0.5} filtre for class : cartonnette hashtag_id of this class : 702398920 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 122 About to insert : list_path_to_insert length 122 new photo from crops ! we have finished the crop for the class : cartonnette begin to crop the class : carton_brun param for this class : {'min_score': 0.7} filtre for class : carton_brun hashtag_id of this class : 2107753024 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 17 About to insert : list_path_to_insert length 17 new photo from crops ! we have finished the crop for the class : carton_brun begin to crop the class : plastique param for this class : {'min_score': 0.5} filtre for class : plastique hashtag_id of this class : 492725882 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 14 About to insert : list_path_to_insert length 14 new photo from crops ! we have finished the crop for the class : plastique begin to crop the class : kraft param for this class : {'min_score': 0.5} filtre for class : kraft hashtag_id of this class : 493202403 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! we have finished the crop for the class : kraft begin to crop the class : metal param for this class : {'min_score': 0.5} filtre for class : metal hashtag_id of this class : 492628673 delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] Looping around the photos to save general results len do output : 226 /-3653468771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469082Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468909Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469091Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469120Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469081Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469101Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468930Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468933Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468965Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468969Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468963Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468973Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469040Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469059Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469063Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469074Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469080Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469094Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469095Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469093Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469097Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469096Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469115Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469118Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469113Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469121Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469114Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468903Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469109Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469087Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469104Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 698 time used for this insertion : 0.49665403366088867 save_final save missing photos in datou_result : time spend for datou_step_exec : 101.6726667881012 time spend to save output : 0.5035407543182373 total time spend for step 2 : 102.17620754241943 step3:thcl Tue Feb 4 12:30:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step Thcl ! nombre de thcls : 2 we are using the classfication for multi_thcl [2456, 2868] time to import caffe and check if the image exist : 0.014702558517456055 time to convert the images to numpy array : 0.2403392791748047 time to import caffe and check if the image exist : 0.014528036117553711 time to convert the images to numpy array : 0.24845528602600098 time to import caffe and check if the image exist : 0.01855182647705078 time to convert the images to numpy array : 0.30221056938171387 time to import caffe and check if the image exist : 0.01817035675048828 time to convert the images to numpy array : 0.3027827739715576 time to import caffe and check if the image exist : 0.021548986434936523 time to convert the images to numpy array : 0.29979419708251953 time to import caffe and check if the image exist : 0.014564275741577148 time to convert the images to numpy array : 0.31941795349121094 time to import caffe and check if the image exist : 0.0222775936126709 time to convert the images to numpy array : 0.3147702217102051 time to import caffe and check if the image exist : 0.013763666152954102 time to convert the images to numpy array : 0.3354525566101074 time to import caffe and check if the image exist : 0.020115137100219727 time to convert the images to numpy array : 0.3401343822479248 time to import caffe and check if the image exist : 0.02016901969909668 time to convert the images to numpy array : 0.3555443286895752 total time to convert the images to numpy array : 0.5631940364837646 list photo_ids error: [] list photo_ids correct : [-3653468771, -3653468777, -3653468776, -3653468814, -3653468804, -3653468834, -3653468825, -3653468846, -3653468854, -3653468859, -3653468867, -3653468878, -3653468893, -3653468908, -3653468923, -3653469015, -3653469082, -3653469105, -3653468792, -3653468793, -3653468786, -3653468779, -3653468819, -3653469090, -3653469086, -3653469096, -3653469108, -3653469115, -3653469116, -3653469118, -3653469113, -3653469121, -3653469114, -3653468774, -3653468785, -3653468797, -3653468850, -3653468863, -3653468876, -3653468873, -3653468891, -3653468888, -3653468903, -3653468918, -3653468937, -3653468957, -3653469011, -3653469001, -3653469024, -3653469045, -3653469068, -3653469091, -3653469120, -3653468794, -3653468882, -3653468875, -3653468897, -3653468890, -3653468905, -3653468920, -3653468934, -3653468954, -3653468979, -3653468981, -3653469000, -3653469002, -3653469016, -3653469034, -3653469081, -3653469035, -3653469037, -3653469036, -3653469054, -3653469053, -3653469051, -3653469059, -3653469062, -3653469063, -3653469074, -3653469071, -3653469058, -3653469061, -3653469067, -3653469075, -3653469064, -3653469060, -3653469080, -3653469094, -3653469095, -3653469093, -3653469097, -3653469084, -3653468830, -3653468841, -3653468828, -3653468843, -3653468826, -3653468829, -3653468845, -3653468871, -3653468858, -3653468881, -3653468874, -3653468886, -3653468896, -3653468889, -3653468901, -3653468911, -3653468915, -3653468912, -3653468904, -3653468913, -3653468910, -3653468926, -3653468930, -3653469013, -3653469069, -3653469109, -3653469111, -3653468811, -3653468820, -3653468832, -3653468840, -3653468856, -3653468869, -3653468940, -3653468960, -3653468975, -3653468991, -3653468996, -3653469012, -3653469087, -3653469092, -3653469104, -3653468927, -3653468919, -3653468928, -3653468925, -3653468936, -3653468951, -3653468948, -3653468944, -3653468933, -3653468945, -3653468938, -3653468949, -3653468943, -3653468956, -3653468970, -3653468968, -3653468964, -3653468953, -3653468965, -3653468958, -3653468969, -3653468963, -3653468977, -3653468823, -3653468839, -3653468844, -3653468855, -3653468868, -3653468884, -3653468885, -3653468899, -3653468900, -3653468909, -3653468924, -3653468939, -3653468935, -3653468947, -3653468959, -3653468955, -3653468966, -3653468986, -3653468983, -3653468990, -3653468980, -3653469006, -3653469003, -3653468984, -3653468978, -3653468971, -3653468973, -3653468982, -3653468997, -3653469004, -3653468999, -3653468992, -3653468994, -3653469010, -3653469014, -3653469017, -3653469025, -3653469022, -3653469019, -3653469020, -3653469023, -3653469040, -3653469039, -3653469046, -3653469033, -3653469047, -3653469073, -3653469066, -3653469101, -3653468784, -3653468783, -3653468778, -3653468787, -3653468795, -3653468796, -3653468772, -3653468790, -3653468782, -3653468802, -3653468801, -3653468798, -3653468813, -3653468809, -3653468812, -3653468807, -3653468822, -3653468805, -3653468808, -3653468833] number of photos to traite : 226 try to delete the photos incorrect in DB tagging for thcl : 2456 To do loadFromThcl(), then load ParamDescType : thcl2456 thcls : [{'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'}] thcl {'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'} Update svm_hashtag_type_desc : 4999 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4999, 'learn_qualipapia_papier_refus_from_vlg_data_aug', 16384, 25088, 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2020, 10, 23, 14, 27, 22), datetime.datetime(2020, 10, 23, 14, 27, 22)) To loadFromThcl() : net_4999 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 5548 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4999, 'learn_qualipapia_papier_refus_from_vlg_data_aug', 16384, 25088, 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2020, 10, 23, 14, 27, 22), datetime.datetime(2020, 10, 23, 14, 27, 22)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_qualipapia_papier_refus_from_vlg_data_aug Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_qualipapia_papier_refus_from_vlg_data_aug model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/caffemodel size_local : 44972172 size in s3 : 44972172 create time local : 2021-08-09 05:55:48 create time in s3 : 2021-08-06 19:28:49 caffemodel already exist and didn't need to update /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/deploy.prototxt size_local : 17311 size in s3 : 17311 create time local : 2021-08-09 05:55:48 create time in s3 : 2021-08-06 19:28:49 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:55:48 create time in s3 : 2021-08-06 19:28:51 mean.npy already exist and didn't need to update /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/synset_words.txt size_local : 57 size in s3 : 57 create time local : 2021-08-09 05:55:48 create time in s3 : 2021-08-06 19:28:49 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/:/home/admin/workarea/git/apy/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/deploy.prototxt caffemodel_filename : /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory WARNING: Logging before InitGoogleLogging() is written to STDERR F0204 12:30:46.480783 3864311 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Command terminated by signal 6 164.13user 63.56system 3:49.56elapsed 99%CPU (0avgtext+0avgdata 6487704maxresident)k 18704inputs+108368outputs (201major+10422292minor)pagefaults 0swaps