python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 20 -a ' -a 3318 ' -s datou_3318 -M 0 -S 0 -U 95,95,120 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/caffe_cuda8_python3/python', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/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 : 2624324 load datou : 3318 # 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( 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 : chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? [(photo_id, hashtag_id, hashtag_type, x0, x1, y0, y1, score, seg_temp, polygons), ...] was removed should we ? chemin de la photo was removed should we ? [ (photo_id_loc, hashtag_id, hashtag_type, x0, x1, y0, y1, score, None), ...] was removed should we ? chemin de la photo was removed should we ? id de la photo (peut être local ou global) was removed should we ? chemin de la photo was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? donnée sous forme de texte was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? id de la photo (peut être local ou global) was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? None was removed should we ? donnée sous forme de nombre was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? donnée sous forme de texte was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) load thcls load THCL from format json or kwargs add thcl : 2847 in CacheModelConfig load pdts add pdt : 5275 in CacheModelConfig 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 : 3318, datou_cur_ids : ['2735840'] with mtr_portfolio_ids : ['22175831'] and first list_photo_ids : [] new path : /proc/2624324/ 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 13 ; length of list_pids : 13 ; length of list_args : 13 time to download the photos : 3.07966947555542 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 : 10 step1:mask_detect Wed Apr 9 19:30:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10151 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-09 19:30:35.532007: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-09 19:30:35.559152: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-09 19:30:35.561081: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fda2c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-09 19:30:35.561140: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-09 19:30:35.565132: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-09 19:30:35.696864: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x189b2190 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-09 19:30:35.696908: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-09 19:30:35.697802: 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 2025-04-09 19:30:35.698086: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 19:30:35.700225: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 19:30:35.702291: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 19:30:35.702603: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 19:30:35.704903: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 19:30:35.705985: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 19:30:35.710501: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 19:30:35.712013: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 19:30:35.712094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 19:30:35.712836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 19:30:35.712852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 19:30:35.712861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 19:30:35.714518: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9256 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. 2025-04-09 19:30:36.044992: 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 2025-04-09 19:30:36.045075: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 19:30:36.045096: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 19:30:36.045115: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 19:30:36.045133: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 19:30:36.045150: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 19:30:36.045168: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 19:30:36.045186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 19:30:36.046710: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 19:30:36.048014: 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 2025-04-09 19:30:36.048071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 19:30:36.048093: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 19:30:36.048111: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 19:30:36.048127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 19:30:36.048143: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 19:30:36.048159: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 19:30:36.048175: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 19:30:36.049414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 19:30:36.049445: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 19:30:36.049454: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 19:30:36.049462: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 19:30:36.050759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9256 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 : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', '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_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 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_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-09 19:30:47.426987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 19:30:47.719044: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 13 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 76 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 70 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 7.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 17 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 38 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 32 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 39 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 78 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 63 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 59 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 34 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, 17) 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, 17) 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, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 57 Detection mask done ! Trying to reset tf kernel 2625288 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4862 tf kernel not reseted sub process len(results) : 13 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 13 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 : 10151 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.0006043910980224609 nb_pixel_total : 9776 time to create 1 rle with old method : 0.018456697463989258 length of segment : 121 time for calcul the mask position with numpy : 0.002919435501098633 nb_pixel_total : 44948 time to create 1 rle with old method : 0.050401926040649414 length of segment : 423 time for calcul the mask position with numpy : 0.0007905960083007812 nb_pixel_total : 35203 time to create 1 rle with old method : 0.04038190841674805 length of segment : 179 time for calcul the mask position with numpy : 0.0007894039154052734 nb_pixel_total : 24863 time to create 1 rle with old method : 0.028234243392944336 length of segment : 222 time for calcul the mask position with numpy : 0.0004315376281738281 nb_pixel_total : 15552 time to create 1 rle with old method : 0.01828169822692871 length of segment : 166 time for calcul the mask position with numpy : 0.00046515464782714844 nb_pixel_total : 15879 time to create 1 rle with old method : 0.02039623260498047 length of segment : 185 time for calcul the mask position with numpy : 0.0006341934204101562 nb_pixel_total : 17296 time to create 1 rle with old method : 0.05004310607910156 length of segment : 286 time for calcul the mask position with numpy : 0.001748800277709961 nb_pixel_total : 27438 time to create 1 rle with old method : 0.035449981689453125 length of segment : 288 time for calcul the mask position with numpy : 0.003209829330444336 nb_pixel_total : 46064 time to create 1 rle with old method : 0.05495858192443848 length of segment : 445 time for calcul the mask position with numpy : 0.0011608600616455078 nb_pixel_total : 24172 time to create 1 rle with old method : 0.02945566177368164 length of segment : 126 time for calcul the mask position with numpy : 0.0006272792816162109 nb_pixel_total : 8802 time to create 1 rle with old method : 0.010715246200561523 length of segment : 85 time for calcul the mask position with numpy : 0.0031843185424804688 nb_pixel_total : 56672 time to create 1 rle with old method : 0.06628251075744629 length of segment : 216 time for calcul the mask position with numpy : 0.0002903938293457031 nb_pixel_total : 6035 time to create 1 rle with old method : 0.007195711135864258 length of segment : 80 time for calcul the mask position with numpy : 0.005135774612426758 nb_pixel_total : 83784 time to create 1 rle with old method : 0.09446597099304199 length of segment : 245 time for calcul the mask position with numpy : 0.000982522964477539 nb_pixel_total : 20386 time to create 1 rle with old method : 0.023133277893066406 length of segment : 193 time for calcul the mask position with numpy : 0.00025010108947753906 nb_pixel_total : 10505 time to create 1 rle with old method : 0.012144804000854492 length of segment : 118 time for calcul the mask position with numpy : 0.0005261898040771484 nb_pixel_total : 18671 time to create 1 rle with old method : 0.02184295654296875 length of segment : 186 time for calcul the mask position with numpy : 0.0001697540283203125 nb_pixel_total : 7673 time to create 1 rle with old method : 0.009260177612304688 length of segment : 107 time for calcul the mask position with numpy : 0.0010700225830078125 nb_pixel_total : 37265 time to create 1 rle with old method : 0.0427546501159668 length of segment : 241 time for calcul the mask position with numpy : 0.00023865699768066406 nb_pixel_total : 9234 time to create 1 rle with old method : 0.01056671142578125 length of segment : 140 time for calcul the mask position with numpy : 0.000186920166015625 nb_pixel_total : 4742 time to create 1 rle with old method : 0.0055065155029296875 length of segment : 139 time for calcul the mask position with numpy : 0.0003151893615722656 nb_pixel_total : 18446 time to create 1 rle with old method : 0.02087712287902832 length of segment : 123 time for calcul the mask position with numpy : 0.0021066665649414062 nb_pixel_total : 32873 time to create 1 rle with old method : 0.03669548034667969 length of segment : 316 time for calcul the mask position with numpy : 0.00331878662109375 nb_pixel_total : 36061 time to create 1 rle with old method : 0.040813446044921875 length of segment : 599 time for calcul the mask position with numpy : 0.006199836730957031 nb_pixel_total : 81258 time to create 1 rle with old method : 0.09844803810119629 length of segment : 487 time for calcul the mask position with numpy : 0.0001747608184814453 nb_pixel_total : 4681 time to create 1 rle with old method : 0.0055043697357177734 length of segment : 105 time for calcul the mask position with numpy : 0.001216888427734375 nb_pixel_total : 45003 time to create 1 rle with old method : 0.05173301696777344 length of segment : 344 time for calcul the mask position with numpy : 0.0019421577453613281 nb_pixel_total : 55047 time to create 1 rle with old method : 0.06783342361450195 length of segment : 303 time for calcul the mask position with numpy : 0.0007677078247070312 nb_pixel_total : 8726 time to create 1 rle with old method : 0.011630773544311523 length of segment : 128 time for calcul the mask position with numpy : 0.0005433559417724609 nb_pixel_total : 19649 time to create 1 rle with old method : 0.02310490608215332 length of segment : 112 time for calcul the mask position with numpy : 0.0012793540954589844 nb_pixel_total : 17215 time to create 1 rle with old method : 0.020359516143798828 length of segment : 162 time for calcul the mask position with numpy : 0.0005321502685546875 nb_pixel_total : 12719 time to create 1 rle with old method : 0.015047788619995117 length of segment : 114 time for calcul the mask position with numpy : 0.0006194114685058594 nb_pixel_total : 21088 time to create 1 rle with old method : 0.024402618408203125 length of segment : 226 time for calcul the mask position with numpy : 0.0008249282836914062 nb_pixel_total : 44946 time to create 1 rle with old method : 0.05079364776611328 length of segment : 335 time for calcul the mask position with numpy : 0.0007634162902832031 nb_pixel_total : 9749 time to create 1 rle with old method : 0.016279220581054688 length of segment : 143 time for calcul the mask position with numpy : 0.00017499923706054688 nb_pixel_total : 3974 time to create 1 rle with old method : 0.0068514347076416016 length of segment : 68 time for calcul the mask position with numpy : 0.0003733634948730469 nb_pixel_total : 15787 time to create 1 rle with old method : 0.02436661720275879 length of segment : 132 time for calcul the mask position with numpy : 0.0004570484161376953 nb_pixel_total : 6223 time to create 1 rle with old method : 0.007454633712768555 length of segment : 103 time for calcul the mask position with numpy : 0.0005745887756347656 nb_pixel_total : 14029 time to create 1 rle with old method : 0.016339540481567383 length of segment : 260 time for calcul the mask position with numpy : 0.0006458759307861328 nb_pixel_total : 19166 time to create 1 rle with old method : 0.02301812171936035 length of segment : 160 time for calcul the mask position with numpy : 0.0005059242248535156 nb_pixel_total : 24098 time to create 1 rle with old method : 0.02718806266784668 length of segment : 210 time for calcul the mask position with numpy : 0.0024433135986328125 nb_pixel_total : 37903 time to create 1 rle with old method : 0.042459964752197266 length of segment : 280 time for calcul the mask position with numpy : 0.0008788108825683594 nb_pixel_total : 29122 time to create 1 rle with old method : 0.03276801109313965 length of segment : 275 time for calcul the mask position with numpy : 0.0024487972259521484 nb_pixel_total : 38439 time to create 1 rle with old method : 0.04209184646606445 length of segment : 253 time for calcul the mask position with numpy : 0.0009150505065917969 nb_pixel_total : 12193 time to create 1 rle with old method : 0.016963720321655273 length of segment : 140 time for calcul the mask position with numpy : 0.002118349075317383 nb_pixel_total : 26742 time to create 1 rle with old method : 0.03838539123535156 length of segment : 210 time for calcul the mask position with numpy : 0.019372224807739258 nb_pixel_total : 219606 time to create 1 rle with new method : 0.026271820068359375 length of segment : 597 time for calcul the mask position with numpy : 0.0032978057861328125 nb_pixel_total : 37258 time to create 1 rle with old method : 0.051505088806152344 length of segment : 320 time for calcul the mask position with numpy : 0.0012466907501220703 nb_pixel_total : 15423 time to create 1 rle with old method : 0.021507740020751953 length of segment : 199 time for calcul the mask position with numpy : 0.001798868179321289 nb_pixel_total : 26718 time to create 1 rle with old method : 0.04326200485229492 length of segment : 152 time for calcul the mask position with numpy : 0.0007841587066650391 nb_pixel_total : 20425 time to create 1 rle with old method : 0.03471827507019043 length of segment : 155 time for calcul the mask position with numpy : 0.006536006927490234 nb_pixel_total : 61246 time to create 1 rle with old method : 0.11240077018737793 length of segment : 284 time for calcul the mask position with numpy : 0.001138448715209961 nb_pixel_total : 8484 time to create 1 rle with old method : 0.013289451599121094 length of segment : 115 time for calcul the mask position with numpy : 0.001711130142211914 nb_pixel_total : 23847 time to create 1 rle with old method : 0.036202192306518555 length of segment : 176 time for calcul the mask position with numpy : 0.0011057853698730469 nb_pixel_total : 23970 time to create 1 rle with old method : 0.03998136520385742 length of segment : 350 time for calcul the mask position with numpy : 0.0012691020965576172 nb_pixel_total : 12466 time to create 1 rle with old method : 0.020456552505493164 length of segment : 150 time for calcul the mask position with numpy : 0.0006849765777587891 nb_pixel_total : 16281 time to create 1 rle with old method : 0.029045581817626953 length of segment : 203 time for calcul the mask position with numpy : 0.0029685497283935547 nb_pixel_total : 30426 time to create 1 rle with old method : 0.03544330596923828 length of segment : 156 time for calcul the mask position with numpy : 0.0007376670837402344 nb_pixel_total : 13394 time to create 1 rle with old method : 0.015215158462524414 length of segment : 163 time for calcul the mask position with numpy : 0.0011510848999023438 nb_pixel_total : 10117 time to create 1 rle with old method : 0.012015342712402344 length of segment : 178 time for calcul the mask position with numpy : 0.003197908401489258 nb_pixel_total : 75174 time to create 1 rle with old method : 0.08413100242614746 length of segment : 441 time for calcul the mask position with numpy : 0.0002827644348144531 nb_pixel_total : 2843 time to create 1 rle with old method : 0.0034456253051757812 length of segment : 56 time for calcul the mask position with numpy : 0.00023794174194335938 nb_pixel_total : 6818 time to create 1 rle with old method : 0.008231401443481445 length of segment : 126 time for calcul the mask position with numpy : 0.0009951591491699219 nb_pixel_total : 18900 time to create 1 rle with old method : 0.022308349609375 length of segment : 124 time for calcul the mask position with numpy : 0.015121698379516602 nb_pixel_total : 256463 time to create 1 rle with new method : 0.018590927124023438 length of segment : 820 time for calcul the mask position with numpy : 0.0004627704620361328 nb_pixel_total : 7759 time to create 1 rle with old method : 0.009155511856079102 length of segment : 93 time for calcul the mask position with numpy : 0.0005104541778564453 nb_pixel_total : 6444 time to create 1 rle with old method : 0.007657527923583984 length of segment : 69 time for calcul the mask position with numpy : 0.0006463527679443359 nb_pixel_total : 10642 time to create 1 rle with old method : 0.012241363525390625 length of segment : 153 time for calcul the mask position with numpy : 0.0017268657684326172 nb_pixel_total : 30532 time to create 1 rle with old method : 0.03405308723449707 length of segment : 237 time for calcul the mask position with numpy : 0.0006396770477294922 nb_pixel_total : 13745 time to create 1 rle with old method : 0.014964580535888672 length of segment : 135 time for calcul the mask position with numpy : 0.002523183822631836 nb_pixel_total : 31126 time to create 1 rle with old method : 0.050570011138916016 length of segment : 331 time for calcul the mask position with numpy : 0.006108760833740234 nb_pixel_total : 123351 time to create 1 rle with old method : 0.12964081764221191 length of segment : 415 time for calcul the mask position with numpy : 0.0018913745880126953 nb_pixel_total : 33594 time to create 1 rle with old method : 0.038945913314819336 length of segment : 129 time for calcul the mask position with numpy : 0.0007460117340087891 nb_pixel_total : 12362 time to create 1 rle with old method : 0.013962984085083008 length of segment : 122 time for calcul the mask position with numpy : 0.0006763935089111328 nb_pixel_total : 14252 time to create 1 rle with old method : 0.016906023025512695 length of segment : 121 time for calcul the mask position with numpy : 0.003150463104248047 nb_pixel_total : 23592 time to create 1 rle with old method : 0.027144670486450195 length of segment : 542 time for calcul the mask position with numpy : 0.0008935928344726562 nb_pixel_total : 16701 time to create 1 rle with old method : 0.01898932456970215 length of segment : 192 time for calcul the mask position with numpy : 0.0017812252044677734 nb_pixel_total : 37104 time to create 1 rle with old method : 0.038678646087646484 length of segment : 201 time for calcul the mask position with numpy : 0.0006666183471679688 nb_pixel_total : 11668 time to create 1 rle with old method : 0.013561725616455078 length of segment : 107 time for calcul the mask position with numpy : 0.0015854835510253906 nb_pixel_total : 33707 time to create 1 rle with old method : 0.03783702850341797 length of segment : 332 time for calcul the mask position with numpy : 0.0006585121154785156 nb_pixel_total : 10676 time to create 1 rle with old method : 0.011924266815185547 length of segment : 110 time for calcul the mask position with numpy : 0.0005438327789306641 nb_pixel_total : 10013 time to create 1 rle with old method : 0.01143646240234375 length of segment : 116 time for calcul the mask position with numpy : 0.0016071796417236328 nb_pixel_total : 14192 time to create 1 rle with old method : 0.01825571060180664 length of segment : 137 time for calcul the mask position with numpy : 0.0005085468292236328 nb_pixel_total : 6930 time to create 1 rle with old method : 0.011759042739868164 length of segment : 102 time for calcul the mask position with numpy : 0.0008692741394042969 nb_pixel_total : 50263 time to create 1 rle with old method : 0.06549382209777832 length of segment : 329 time for calcul the mask position with numpy : 0.0009160041809082031 nb_pixel_total : 68483 time to create 1 rle with old method : 0.07720303535461426 length of segment : 262 time for calcul the mask position with numpy : 0.00047969818115234375 nb_pixel_total : 27578 time to create 1 rle with old method : 0.030920982360839844 length of segment : 365 time for calcul the mask position with numpy : 0.0002865791320800781 nb_pixel_total : 22139 time to create 1 rle with old method : 0.025583744049072266 length of segment : 125 time for calcul the mask position with numpy : 0.03315424919128418 nb_pixel_total : 641577 time to create 1 rle with new method : 0.06078362464904785 length of segment : 910 time for calcul the mask position with numpy : 0.0006494522094726562 nb_pixel_total : 42534 time to create 1 rle with old method : 0.04870939254760742 length of segment : 229 time for calcul the mask position with numpy : 0.0002505779266357422 nb_pixel_total : 14751 time to create 1 rle with old method : 0.016841411590576172 length of segment : 109 time for calcul the mask position with numpy : 0.0002846717834472656 nb_pixel_total : 17119 time to create 1 rle with old method : 0.019945621490478516 length of segment : 155 time for calcul the mask position with numpy : 0.0005741119384765625 nb_pixel_total : 35532 time to create 1 rle with old method : 0.03993797302246094 length of segment : 296 time for calcul the mask position with numpy : 0.0002117156982421875 nb_pixel_total : 15321 time to create 1 rle with old method : 0.017519712448120117 length of segment : 108 time for calcul the mask position with numpy : 0.0004839897155761719 nb_pixel_total : 32744 time to create 1 rle with old method : 0.036562442779541016 length of segment : 228 time for calcul the mask position with numpy : 0.0004086494445800781 nb_pixel_total : 20629 time to create 1 rle with old method : 0.022870540618896484 length of segment : 189 time for calcul the mask position with numpy : 0.001077413558959961 nb_pixel_total : 66023 time to create 1 rle with old method : 0.0745847225189209 length of segment : 248 time for calcul the mask position with numpy : 0.0015845298767089844 nb_pixel_total : 89095 time to create 1 rle with old method : 0.10388898849487305 length of segment : 414 time for calcul the mask position with numpy : 0.0009970664978027344 nb_pixel_total : 33393 time to create 1 rle with old method : 0.040570974349975586 length of segment : 346 time for calcul the mask position with numpy : 0.0012660026550292969 nb_pixel_total : 47699 time to create 1 rle with old method : 0.06377291679382324 length of segment : 470 time for calcul the mask position with numpy : 0.00571441650390625 nb_pixel_total : 344703 time to create 1 rle with new method : 0.018066883087158203 length of segment : 739 time for calcul the mask position with numpy : 0.0011522769927978516 nb_pixel_total : 53961 time to create 1 rle with old method : 0.06124997138977051 length of segment : 378 time for calcul the mask position with numpy : 0.004102230072021484 nb_pixel_total : 221939 time to create 1 rle with new method : 0.015131235122680664 length of segment : 601 time for calcul the mask position with numpy : 0.0040318965911865234 nb_pixel_total : 237714 time to create 1 rle with new method : 0.012718915939331055 length of segment : 506 time for calcul the mask position with numpy : 0.0007672309875488281 nb_pixel_total : 33709 time to create 1 rle with old method : 0.038162946701049805 length of segment : 326 time for calcul the mask position with numpy : 0.0005075931549072266 nb_pixel_total : 28353 time to create 1 rle with old method : 0.03233027458190918 length of segment : 175 time for calcul the mask position with numpy : 0.0013179779052734375 nb_pixel_total : 51901 time to create 1 rle with old method : 0.0588688850402832 length of segment : 305 time for calcul the mask position with numpy : 0.0016312599182128906 nb_pixel_total : 86356 time to create 1 rle with old method : 0.09551572799682617 length of segment : 370 time for calcul the mask position with numpy : 0.0005939006805419922 nb_pixel_total : 34304 time to create 1 rle with old method : 0.03962135314941406 length of segment : 227 time for calcul the mask position with numpy : 0.00031638145446777344 nb_pixel_total : 12073 time to create 1 rle with old method : 0.014063119888305664 length of segment : 150 time for calcul the mask position with numpy : 0.001280069351196289 nb_pixel_total : 82845 time to create 1 rle with old method : 0.09300041198730469 length of segment : 415 time for calcul the mask position with numpy : 0.0024907588958740234 nb_pixel_total : 101382 time to create 1 rle with old method : 0.1334383487701416 length of segment : 546 time for calcul the mask position with numpy : 0.0024404525756835938 nb_pixel_total : 137757 time to create 1 rle with old method : 0.1682283878326416 length of segment : 454 time for calcul the mask position with numpy : 0.004218101501464844 nb_pixel_total : 216327 time to create 1 rle with new method : 0.015262842178344727 length of segment : 1055 time for calcul the mask position with numpy : 0.0013859272003173828 nb_pixel_total : 67343 time to create 1 rle with old method : 0.09836101531982422 length of segment : 396 time for calcul the mask position with numpy : 0.001918792724609375 nb_pixel_total : 105383 time to create 1 rle with old method : 0.11765766143798828 length of segment : 461 time for calcul the mask position with numpy : 0.0032470226287841797 nb_pixel_total : 195401 time to create 1 rle with new method : 0.008483648300170898 length of segment : 496 time for calcul the mask position with numpy : 0.0003020763397216797 nb_pixel_total : 12891 time to create 1 rle with old method : 0.014831066131591797 length of segment : 208 time for calcul the mask position with numpy : 0.000997781753540039 nb_pixel_total : 63202 time to create 1 rle with old method : 0.08706498146057129 length of segment : 437 time for calcul the mask position with numpy : 0.00179290771484375 nb_pixel_total : 107014 time to create 1 rle with old method : 0.12043642997741699 length of segment : 350 time for calcul the mask position with numpy : 0.0007352828979492188 nb_pixel_total : 38304 time to create 1 rle with old method : 0.04269909858703613 length of segment : 368 time for calcul the mask position with numpy : 0.0023262500762939453 nb_pixel_total : 114228 time to create 1 rle with old method : 0.12705183029174805 length of segment : 427 time for calcul the mask position with numpy : 0.0018198490142822266 nb_pixel_total : 116104 time to create 1 rle with old method : 0.1283118724822998 length of segment : 349 time for calcul the mask position with numpy : 0.0003483295440673828 nb_pixel_total : 15923 time to create 1 rle with old method : 0.018637657165527344 length of segment : 165 time for calcul the mask position with numpy : 0.0003533363342285156 nb_pixel_total : 14298 time to create 1 rle with old method : 0.016935110092163086 length of segment : 198 time for calcul the mask position with numpy : 0.001802682876586914 nb_pixel_total : 137028 time to create 1 rle with old method : 0.15488839149475098 length of segment : 378 time for calcul the mask position with numpy : 0.0006551742553710938 nb_pixel_total : 40368 time to create 1 rle with old method : 0.04538702964782715 length of segment : 194 time for calcul the mask position with numpy : 0.0012350082397460938 nb_pixel_total : 88043 time to create 1 rle with old method : 0.09554743766784668 length of segment : 310 time for calcul the mask position with numpy : 0.0005486011505126953 nb_pixel_total : 25592 time to create 1 rle with old method : 0.02845311164855957 length of segment : 207 time for calcul the mask position with numpy : 0.0007836818695068359 nb_pixel_total : 49486 time to create 1 rle with old method : 0.05434131622314453 length of segment : 413 time for calcul the mask position with numpy : 0.0012180805206298828 nb_pixel_total : 69814 time to create 1 rle with old method : 0.07555198669433594 length of segment : 339 time for calcul the mask position with numpy : 0.0008475780487060547 nb_pixel_total : 51595 time to create 1 rle with old method : 0.05688285827636719 length of segment : 305 time for calcul the mask position with numpy : 0.0007793903350830078 nb_pixel_total : 26407 time to create 1 rle with old method : 0.0434422492980957 length of segment : 259 time for calcul the mask position with numpy : 0.0010607242584228516 nb_pixel_total : 44287 time to create 1 rle with old method : 0.05024218559265137 length of segment : 304 time for calcul the mask position with numpy : 0.00039458274841308594 nb_pixel_total : 21580 time to create 1 rle with old method : 0.025008440017700195 length of segment : 72 time for calcul the mask position with numpy : 0.0038809776306152344 nb_pixel_total : 245128 time to create 1 rle with new method : 0.008827447891235352 length of segment : 492 time for calcul the mask position with numpy : 0.0003809928894042969 nb_pixel_total : 18929 time to create 1 rle with old method : 0.02126288414001465 length of segment : 106 time for calcul the mask position with numpy : 0.003129720687866211 nb_pixel_total : 190266 time to create 1 rle with new method : 0.008414030075073242 length of segment : 369 time for calcul the mask position with numpy : 0.0003809928894042969 nb_pixel_total : 14115 time to create 1 rle with old method : 0.016112327575683594 length of segment : 148 time for calcul the mask position with numpy : 0.0011615753173828125 nb_pixel_total : 65033 time to create 1 rle with old method : 0.07212996482849121 length of segment : 407 time for calcul the mask position with numpy : 0.0009989738464355469 nb_pixel_total : 53062 time to create 1 rle with old method : 0.05831646919250488 length of segment : 402 time for calcul the mask position with numpy : 0.0003974437713623047 nb_pixel_total : 19376 time to create 1 rle with old method : 0.02235269546508789 length of segment : 187 time for calcul the mask position with numpy : 0.0005097389221191406 nb_pixel_total : 17381 time to create 1 rle with old method : 0.02042675018310547 length of segment : 122 time for calcul the mask position with numpy : 0.0008199214935302734 nb_pixel_total : 44904 time to create 1 rle with old method : 0.05032658576965332 length of segment : 435 time for calcul the mask position with numpy : 0.0006184577941894531 nb_pixel_total : 18834 time to create 1 rle with old method : 0.02333664894104004 length of segment : 184 time for calcul the mask position with numpy : 0.0012116432189941406 nb_pixel_total : 65059 time to create 1 rle with old method : 0.07707500457763672 length of segment : 301 time for calcul the mask position with numpy : 0.0007679462432861328 nb_pixel_total : 38805 time to create 1 rle with old method : 0.04347681999206543 length of segment : 247 time for calcul the mask position with numpy : 0.0012052059173583984 nb_pixel_total : 64341 time to create 1 rle with old method : 0.07172918319702148 length of segment : 465 time for calcul the mask position with numpy : 0.0003936290740966797 nb_pixel_total : 19517 time to create 1 rle with old method : 0.022278547286987305 length of segment : 230 time for calcul the mask position with numpy : 0.0014023780822753906 nb_pixel_total : 77491 time to create 1 rle with old method : 0.08631753921508789 length of segment : 288 time for calcul the mask position with numpy : 0.0001990795135498047 nb_pixel_total : 7319 time to create 1 rle with old method : 0.012430906295776367 length of segment : 90 time for calcul the mask position with numpy : 0.0016167163848876953 nb_pixel_total : 74687 time to create 1 rle with old method : 0.09689831733703613 length of segment : 428 time for calcul the mask position with numpy : 0.0005068778991699219 nb_pixel_total : 19642 time to create 1 rle with old method : 0.0215299129486084 length of segment : 153 time for calcul the mask position with numpy : 0.0005490779876708984 nb_pixel_total : 21971 time to create 1 rle with old method : 0.025320768356323242 length of segment : 181 time for calcul the mask position with numpy : 0.0003402233123779297 nb_pixel_total : 8745 time to create 1 rle with old method : 0.014646530151367188 length of segment : 92 time for calcul the mask position with numpy : 0.00024247169494628906 nb_pixel_total : 7648 time to create 1 rle with old method : 0.012855768203735352 length of segment : 91 time for calcul the mask position with numpy : 0.0004968643188476562 nb_pixel_total : 14343 time to create 1 rle with old method : 0.017652034759521484 length of segment : 135 time for calcul the mask position with numpy : 0.0002646446228027344 nb_pixel_total : 8937 time to create 1 rle with old method : 0.01031351089477539 length of segment : 105 time for calcul the mask position with numpy : 0.0003333091735839844 nb_pixel_total : 8907 time to create 1 rle with old method : 0.010503768920898438 length of segment : 131 time for calcul the mask position with numpy : 0.000997304916381836 nb_pixel_total : 45699 time to create 1 rle with old method : 0.05050206184387207 length of segment : 283 time for calcul the mask position with numpy : 0.0003261566162109375 nb_pixel_total : 14407 time to create 1 rle with old method : 0.016344308853149414 length of segment : 143 time for calcul the mask position with numpy : 0.00018143653869628906 nb_pixel_total : 8025 time to create 1 rle with old method : 0.009161233901977539 length of segment : 81 time for calcul the mask position with numpy : 0.0004954338073730469 nb_pixel_total : 22784 time to create 1 rle with old method : 0.025368928909301758 length of segment : 242 time for calcul the mask position with numpy : 0.0003063678741455078 nb_pixel_total : 12184 time to create 1 rle with old method : 0.013724327087402344 length of segment : 153 time for calcul the mask position with numpy : 0.0002617835998535156 nb_pixel_total : 10824 time to create 1 rle with old method : 0.012449502944946289 length of segment : 150 time for calcul the mask position with numpy : 0.0009059906005859375 nb_pixel_total : 29643 time to create 1 rle with old method : 0.03295326232910156 length of segment : 217 time for calcul the mask position with numpy : 0.0013763904571533203 nb_pixel_total : 54574 time to create 1 rle with old method : 0.0629417896270752 length of segment : 331 time for calcul the mask position with numpy : 0.0013988018035888672 nb_pixel_total : 44737 time to create 1 rle with old method : 0.057035207748413086 length of segment : 298 time for calcul the mask position with numpy : 0.0003025531768798828 nb_pixel_total : 12631 time to create 1 rle with old method : 0.013834714889526367 length of segment : 162 time for calcul the mask position with numpy : 0.0002906322479248047 nb_pixel_total : 11964 time to create 1 rle with old method : 0.013268709182739258 length of segment : 90 time for calcul the mask position with numpy : 0.000545501708984375 nb_pixel_total : 24367 time to create 1 rle with old method : 0.027227163314819336 length of segment : 181 time for calcul the mask position with numpy : 0.00015544891357421875 nb_pixel_total : 5567 time to create 1 rle with old method : 0.006317138671875 length of segment : 71 time for calcul the mask position with numpy : 0.0005552768707275391 nb_pixel_total : 22995 time to create 1 rle with old method : 0.0254058837890625 length of segment : 219 time for calcul the mask position with numpy : 0.0011174678802490234 nb_pixel_total : 34513 time to create 1 rle with old method : 0.0384981632232666 length of segment : 384 time for calcul the mask position with numpy : 0.0006988048553466797 nb_pixel_total : 30495 time to create 1 rle with old method : 0.03374075889587402 length of segment : 179 time for calcul the mask position with numpy : 0.00041484832763671875 nb_pixel_total : 15005 time to create 1 rle with old method : 0.016978740692138672 length of segment : 173 time for calcul the mask position with numpy : 0.0006392002105712891 nb_pixel_total : 28112 time to create 1 rle with old method : 0.031227827072143555 length of segment : 281 time for calcul the mask position with numpy : 0.00025200843811035156 nb_pixel_total : 9976 time to create 1 rle with old method : 0.01122593879699707 length of segment : 115 time for calcul the mask position with numpy : 0.0005905628204345703 nb_pixel_total : 17559 time to create 1 rle with old method : 0.01996445655822754 length of segment : 160 time for calcul the mask position with numpy : 0.00022220611572265625 nb_pixel_total : 8447 time to create 1 rle with old method : 0.010019063949584961 length of segment : 147 time for calcul the mask position with numpy : 0.0003898143768310547 nb_pixel_total : 12439 time to create 1 rle with old method : 0.01430654525756836 length of segment : 231 time for calcul the mask position with numpy : 0.0006918907165527344 nb_pixel_total : 13761 time to create 1 rle with old method : 0.016080379486083984 length of segment : 172 time for calcul the mask position with numpy : 0.00031304359436035156 nb_pixel_total : 13923 time to create 1 rle with old method : 0.01642441749572754 length of segment : 115 time for calcul the mask position with numpy : 0.001505136489868164 nb_pixel_total : 67040 time to create 1 rle with old method : 0.0764772891998291 length of segment : 220 time for calcul the mask position with numpy : 0.00014209747314453125 nb_pixel_total : 4722 time to create 1 rle with old method : 0.005040168762207031 length of segment : 81 time for calcul the mask position with numpy : 0.0005588531494140625 nb_pixel_total : 25727 time to create 1 rle with old method : 0.027604103088378906 length of segment : 192 time for calcul the mask position with numpy : 0.0002052783966064453 nb_pixel_total : 8704 time to create 1 rle with old method : 0.009821653366088867 length of segment : 140 time for calcul the mask position with numpy : 0.00044989585876464844 nb_pixel_total : 21871 time to create 1 rle with old method : 0.024906158447265625 length of segment : 150 time for calcul the mask position with numpy : 0.00037026405334472656 nb_pixel_total : 11252 time to create 1 rle with old method : 0.012474775314331055 length of segment : 173 time for calcul the mask position with numpy : 0.00035119056701660156 nb_pixel_total : 8408 time to create 1 rle with old method : 0.009752273559570312 length of segment : 152 time for calcul the mask position with numpy : 0.0003268718719482422 nb_pixel_total : 9717 time to create 1 rle with old method : 0.011369943618774414 length of segment : 112 time for calcul the mask position with numpy : 0.0004062652587890625 nb_pixel_total : 12784 time to create 1 rle with old method : 0.015369653701782227 length of segment : 146 time for calcul the mask position with numpy : 0.0004458427429199219 nb_pixel_total : 23615 time to create 1 rle with old method : 0.02823352813720703 length of segment : 159 time for calcul the mask position with numpy : 0.0002028942108154297 nb_pixel_total : 7842 time to create 1 rle with old method : 0.00925755500793457 length of segment : 107 time for calcul the mask position with numpy : 0.0003509521484375 nb_pixel_total : 17324 time to create 1 rle with old method : 0.019933700561523438 length of segment : 159 time for calcul the mask position with numpy : 0.0005574226379394531 nb_pixel_total : 19936 time to create 1 rle with old method : 0.025569677352905273 length of segment : 145 time for calcul the mask position with numpy : 0.0016014575958251953 nb_pixel_total : 74536 time to create 1 rle with old method : 0.08328437805175781 length of segment : 368 time for calcul the mask position with numpy : 0.0013990402221679688 nb_pixel_total : 48158 time to create 1 rle with old method : 0.05455160140991211 length of segment : 218 time for calcul the mask position with numpy : 0.0005972385406494141 nb_pixel_total : 30143 time to create 1 rle with old method : 0.03434896469116211 length of segment : 207 time for calcul the mask position with numpy : 0.0003898143768310547 nb_pixel_total : 13536 time to create 1 rle with old method : 0.015385627746582031 length of segment : 181 time for calcul the mask position with numpy : 0.0015642642974853516 nb_pixel_total : 59769 time to create 1 rle with old method : 0.06695318222045898 length of segment : 373 time for calcul the mask position with numpy : 0.0013003349304199219 nb_pixel_total : 61076 time to create 1 rle with old method : 0.06696677207946777 length of segment : 237 time for calcul the mask position with numpy : 0.0002453327178955078 nb_pixel_total : 9787 time to create 1 rle with old method : 0.011258125305175781 length of segment : 126 time for calcul the mask position with numpy : 0.001352548599243164 nb_pixel_total : 73004 time to create 1 rle with old method : 0.10773777961730957 length of segment : 461 time for calcul the mask position with numpy : 0.0008769035339355469 nb_pixel_total : 26334 time to create 1 rle with old method : 0.029446840286254883 length of segment : 432 time for calcul the mask position with numpy : 0.0005805492401123047 nb_pixel_total : 24442 time to create 1 rle with old method : 0.027765989303588867 length of segment : 148 time for calcul the mask position with numpy : 0.0006783008575439453 nb_pixel_total : 31049 time to create 1 rle with old method : 0.03454327583312988 length of segment : 253 time for calcul the mask position with numpy : 0.0006194114685058594 nb_pixel_total : 24775 time to create 1 rle with old method : 0.02895188331604004 length of segment : 180 time for calcul the mask position with numpy : 0.0005848407745361328 nb_pixel_total : 28123 time to create 1 rle with old method : 0.03188586235046387 length of segment : 165 time for calcul the mask position with numpy : 0.0001423358917236328 nb_pixel_total : 5398 time to create 1 rle with old method : 0.006233692169189453 length of segment : 94 time for calcul the mask position with numpy : 0.0002818107604980469 nb_pixel_total : 15362 time to create 1 rle with old method : 0.017316579818725586 length of segment : 158 time for calcul the mask position with numpy : 0.001444101333618164 nb_pixel_total : 56391 time to create 1 rle with old method : 0.06526064872741699 length of segment : 606 time for calcul the mask position with numpy : 0.000507354736328125 nb_pixel_total : 23599 time to create 1 rle with old method : 0.026795148849487305 length of segment : 180 time for calcul the mask position with numpy : 0.0001614093780517578 nb_pixel_total : 4385 time to create 1 rle with old method : 0.005383014678955078 length of segment : 75 time for calcul the mask position with numpy : 0.00020432472229003906 nb_pixel_total : 6867 time to create 1 rle with old method : 0.008086681365966797 length of segment : 104 time for calcul the mask position with numpy : 0.0016584396362304688 nb_pixel_total : 67761 time to create 1 rle with old method : 0.07628989219665527 length of segment : 240 time for calcul the mask position with numpy : 0.0005145072937011719 nb_pixel_total : 25012 time to create 1 rle with old method : 0.0271453857421875 length of segment : 196 time for calcul the mask position with numpy : 0.0008497238159179688 nb_pixel_total : 36524 time to create 1 rle with old method : 0.04092812538146973 length of segment : 190 time for calcul the mask position with numpy : 0.0004336833953857422 nb_pixel_total : 14003 time to create 1 rle with old method : 0.015799283981323242 length of segment : 125 time for calcul the mask position with numpy : 0.0002033710479736328 nb_pixel_total : 7022 time to create 1 rle with old method : 0.008277654647827148 length of segment : 120 time for calcul the mask position with numpy : 0.0011377334594726562 nb_pixel_total : 26510 time to create 1 rle with old method : 0.030569076538085938 length of segment : 190 time for calcul the mask position with numpy : 0.0006868839263916016 nb_pixel_total : 13370 time to create 1 rle with old method : 0.01565694808959961 length of segment : 141 time for calcul the mask position with numpy : 0.0023462772369384766 nb_pixel_total : 29568 time to create 1 rle with old method : 0.0333864688873291 length of segment : 277 time for calcul the mask position with numpy : 0.001104116439819336 nb_pixel_total : 20336 time to create 1 rle with old method : 0.023565292358398438 length of segment : 157 time for calcul the mask position with numpy : 0.0010459423065185547 nb_pixel_total : 18884 time to create 1 rle with old method : 0.02185845375061035 length of segment : 180 time for calcul the mask position with numpy : 0.0012497901916503906 nb_pixel_total : 19884 time to create 1 rle with old method : 0.022918224334716797 length of segment : 179 time for calcul the mask position with numpy : 0.0035772323608398438 nb_pixel_total : 69800 time to create 1 rle with old method : 0.07852554321289062 length of segment : 394 time for calcul the mask position with numpy : 0.0009481906890869141 nb_pixel_total : 14213 time to create 1 rle with old method : 0.016503334045410156 length of segment : 169 time for calcul the mask position with numpy : 0.0006463527679443359 nb_pixel_total : 11965 time to create 1 rle with old method : 0.013965129852294922 length of segment : 128 time for calcul the mask position with numpy : 0.0007512569427490234 nb_pixel_total : 14312 time to create 1 rle with old method : 0.018748760223388672 length of segment : 128 time for calcul the mask position with numpy : 0.0029680728912353516 nb_pixel_total : 63845 time to create 1 rle with old method : 0.09528946876525879 length of segment : 338 time for calcul the mask position with numpy : 0.0013837814331054688 nb_pixel_total : 30383 time to create 1 rle with old method : 0.03402066230773926 length of segment : 190 time for calcul the mask position with numpy : 0.00577855110168457 nb_pixel_total : 100012 time to create 1 rle with old method : 0.11128377914428711 length of segment : 463 time for calcul the mask position with numpy : 0.0010790824890136719 nb_pixel_total : 20254 time to create 1 rle with old method : 0.02302694320678711 length of segment : 153 time for calcul the mask position with numpy : 0.0025784969329833984 nb_pixel_total : 60405 time to create 1 rle with old method : 0.06644296646118164 length of segment : 664 time for calcul the mask position with numpy : 0.0014491081237792969 nb_pixel_total : 25280 time to create 1 rle with old method : 0.040662527084350586 length of segment : 211 time for calcul the mask position with numpy : 0.0007047653198242188 nb_pixel_total : 15205 time to create 1 rle with old method : 0.01721787452697754 length of segment : 159 time for calcul the mask position with numpy : 0.0007958412170410156 nb_pixel_total : 22957 time to create 1 rle with old method : 0.02592921257019043 length of segment : 157 time for calcul the mask position with numpy : 0.002566814422607422 nb_pixel_total : 64676 time to create 1 rle with old method : 0.0695345401763916 length of segment : 286 time for calcul the mask position with numpy : 0.0004372596740722656 nb_pixel_total : 11610 time to create 1 rle with old method : 0.013272285461425781 length of segment : 152 time for calcul the mask position with numpy : 0.0003039836883544922 nb_pixel_total : 4387 time to create 1 rle with old method : 0.004833221435546875 length of segment : 83 time for calcul the mask position with numpy : 0.0010807514190673828 nb_pixel_total : 20657 time to create 1 rle with old method : 0.022652149200439453 length of segment : 315 time for calcul the mask position with numpy : 0.0005638599395751953 nb_pixel_total : 9828 time to create 1 rle with old method : 0.013333797454833984 length of segment : 139 time for calcul the mask position with numpy : 0.003957033157348633 nb_pixel_total : 35419 time to create 1 rle with old method : 0.056113481521606445 length of segment : 495 time for calcul the mask position with numpy : 0.0006146430969238281 nb_pixel_total : 7415 time to create 1 rle with old method : 0.008760690689086914 length of segment : 127 time for calcul the mask position with numpy : 0.0030698776245117188 nb_pixel_total : 46599 time to create 1 rle with old method : 0.051012277603149414 length of segment : 1165 time for calcul the mask position with numpy : 0.0008921623229980469 nb_pixel_total : 20900 time to create 1 rle with old method : 0.02292656898498535 length of segment : 246 time for calcul the mask position with numpy : 0.0005857944488525391 nb_pixel_total : 8651 time to create 1 rle with old method : 0.00983572006225586 length of segment : 147 time for calcul the mask position with numpy : 0.0013227462768554688 nb_pixel_total : 29464 time to create 1 rle with old method : 0.03345608711242676 length of segment : 122 time for calcul the mask position with numpy : 0.0005400180816650391 nb_pixel_total : 29219 time to create 1 rle with old method : 0.03307700157165527 length of segment : 198 time for calcul the mask position with numpy : 0.0012362003326416016 nb_pixel_total : 23040 time to create 1 rle with old method : 0.025792360305786133 length of segment : 193 time for calcul the mask position with numpy : 0.0008137226104736328 nb_pixel_total : 16036 time to create 1 rle with old method : 0.018351078033447266 length of segment : 182 time for calcul the mask position with numpy : 0.0005903244018554688 nb_pixel_total : 7281 time to create 1 rle with old method : 0.00853729248046875 length of segment : 115 time for calcul the mask position with numpy : 0.00057220458984375 nb_pixel_total : 11434 time to create 1 rle with old method : 0.012950420379638672 length of segment : 113 time for calcul the mask position with numpy : 0.0016069412231445312 nb_pixel_total : 43532 time to create 1 rle with old method : 0.04798746109008789 length of segment : 201 time for calcul the mask position with numpy : 0.0037441253662109375 nb_pixel_total : 66181 time to create 1 rle with old method : 0.07192039489746094 length of segment : 586 time for calcul the mask position with numpy : 0.003233194351196289 nb_pixel_total : 86710 time to create 1 rle with old method : 0.09745168685913086 length of segment : 521 time for calcul the mask position with numpy : 0.00040149688720703125 nb_pixel_total : 12893 time to create 1 rle with old method : 0.01446843147277832 length of segment : 133 time for calcul the mask position with numpy : 0.0014662742614746094 nb_pixel_total : 18909 time to create 1 rle with old method : 0.022426605224609375 length of segment : 281 time for calcul the mask position with numpy : 0.0006701946258544922 nb_pixel_total : 36338 time to create 1 rle with old method : 0.040178775787353516 length of segment : 182 time for calcul the mask position with numpy : 0.0015795230865478516 nb_pixel_total : 93022 time to create 1 rle with old method : 0.11273813247680664 length of segment : 385 time for calcul the mask position with numpy : 0.0015730857849121094 nb_pixel_total : 100906 time to create 1 rle with old method : 0.13069510459899902 length of segment : 368 time for calcul the mask position with numpy : 0.0011777877807617188 nb_pixel_total : 81498 time to create 1 rle with old method : 0.09433627128601074 length of segment : 318 time for calcul the mask position with numpy : 0.0002307891845703125 nb_pixel_total : 9316 time to create 1 rle with old method : 0.010570287704467773 length of segment : 194 time for calcul the mask position with numpy : 0.0010099411010742188 nb_pixel_total : 50721 time to create 1 rle with old method : 0.057193756103515625 length of segment : 402 time for calcul the mask position with numpy : 0.0005772113800048828 nb_pixel_total : 35705 time to create 1 rle with old method : 0.03925800323486328 length of segment : 284 time for calcul the mask position with numpy : 0.0016155242919921875 nb_pixel_total : 115217 time to create 1 rle with old method : 0.12677359580993652 length of segment : 493 time for calcul the mask position with numpy : 0.0018589496612548828 nb_pixel_total : 104297 time to create 1 rle with old method : 0.11225676536560059 length of segment : 563 time for calcul the mask position with numpy : 0.0004744529724121094 nb_pixel_total : 18558 time to create 1 rle with old method : 0.020447492599487305 length of segment : 236 time for calcul the mask position with numpy : 0.0005488395690917969 nb_pixel_total : 28444 time to create 1 rle with old method : 0.03215956687927246 length of segment : 270 time for calcul the mask position with numpy : 0.0013422966003417969 nb_pixel_total : 62739 time to create 1 rle with old method : 0.07024931907653809 length of segment : 302 time for calcul the mask position with numpy : 0.0014905929565429688 nb_pixel_total : 83505 time to create 1 rle with old method : 0.09081864356994629 length of segment : 390 time for calcul the mask position with numpy : 0.0013947486877441406 nb_pixel_total : 98181 time to create 1 rle with old method : 0.10770750045776367 length of segment : 291 time for calcul the mask position with numpy : 0.000446319580078125 nb_pixel_total : 14537 time to create 1 rle with old method : 0.01683950424194336 length of segment : 247 time for calcul the mask position with numpy : 0.002471446990966797 nb_pixel_total : 116857 time to create 1 rle with old method : 0.1298050880432129 length of segment : 589 time for calcul the mask position with numpy : 0.002337217330932617 nb_pixel_total : 127225 time to create 1 rle with old method : 0.13970398902893066 length of segment : 457 time for calcul the mask position with numpy : 0.0005297660827636719 nb_pixel_total : 19420 time to create 1 rle with old method : 0.022524595260620117 length of segment : 242 time for calcul the mask position with numpy : 0.0006577968597412109 nb_pixel_total : 35587 time to create 1 rle with old method : 0.0407872200012207 length of segment : 168 time for calcul the mask position with numpy : 0.0016582012176513672 nb_pixel_total : 106328 time to create 1 rle with old method : 0.1360459327697754 length of segment : 491 time for calcul the mask position with numpy : 0.002909421920776367 nb_pixel_total : 131487 time to create 1 rle with old method : 0.1469864845275879 length of segment : 668 time for calcul the mask position with numpy : 0.0016467571258544922 nb_pixel_total : 84779 time to create 1 rle with old method : 0.09902405738830566 length of segment : 764 time for calcul the mask position with numpy : 0.0004436969757080078 nb_pixel_total : 27834 time to create 1 rle with old method : 0.03144693374633789 length of segment : 213 time for calcul the mask position with numpy : 0.0006515979766845703 nb_pixel_total : 44192 time to create 1 rle with old method : 0.057956695556640625 length of segment : 182 time for calcul the mask position with numpy : 0.0019183158874511719 nb_pixel_total : 73016 time to create 1 rle with old method : 0.0892333984375 length of segment : 419 time for calcul the mask position with numpy : 0.0003724098205566406 nb_pixel_total : 16254 time to create 1 rle with old method : 0.019582033157348633 length of segment : 197 time for calcul the mask position with numpy : 0.0046389102935791016 nb_pixel_total : 314500 time to create 1 rle with new method : 0.013259410858154297 length of segment : 841 time for calcul the mask position with numpy : 0.0003044605255126953 nb_pixel_total : 15986 time to create 1 rle with old method : 0.018794536590576172 length of segment : 110 time for calcul the mask position with numpy : 0.0007119178771972656 nb_pixel_total : 40964 time to create 1 rle with old method : 0.04671168327331543 length of segment : 266 time for calcul the mask position with numpy : 0.0012972354888916016 nb_pixel_total : 62051 time to create 1 rle with old method : 0.06905412673950195 length of segment : 541 time for calcul the mask position with numpy : 0.001210927963256836 nb_pixel_total : 62682 time to create 1 rle with old method : 0.07117700576782227 length of segment : 264 time for calcul the mask position with numpy : 0.0004744529724121094 nb_pixel_total : 26572 time to create 1 rle with old method : 0.029088735580444336 length of segment : 180 time for calcul the mask position with numpy : 0.0009002685546875 nb_pixel_total : 58509 time to create 1 rle with old method : 0.0663750171661377 length of segment : 227 time for calcul the mask position with numpy : 0.0005581378936767578 nb_pixel_total : 39771 time to create 1 rle with old method : 0.045128822326660156 length of segment : 230 time for calcul the mask position with numpy : 0.7807998657226562 nb_pixel_total : 908721 time to create 1 rle with new method : 0.0850839614868164 length of segment : 1522 time for calcul the mask position with numpy : 0.0017192363739013672 nb_pixel_total : 115129 time to create 1 rle with old method : 0.1250288486480713 length of segment : 420 time for calcul the mask position with numpy : 0.0026133060455322266 nb_pixel_total : 105649 time to create 1 rle with old method : 0.11446142196655273 length of segment : 577 time for calcul the mask position with numpy : 0.006004810333251953 nb_pixel_total : 255134 time to create 1 rle with new method : 0.009315013885498047 length of segment : 601 time for calcul the mask position with numpy : 0.0007681846618652344 nb_pixel_total : 20754 time to create 1 rle with old method : 0.024042367935180664 length of segment : 165 time for calcul the mask position with numpy : 0.0007395744323730469 nb_pixel_total : 17867 time to create 1 rle with old method : 0.02058243751525879 length of segment : 232 time for calcul the mask position with numpy : 0.001804351806640625 nb_pixel_total : 57770 time to create 1 rle with old method : 0.07279014587402344 length of segment : 541 time for calcul the mask position with numpy : 0.0029408931732177734 nb_pixel_total : 123215 time to create 1 rle with old method : 0.13725686073303223 length of segment : 348 time for calcul the mask position with numpy : 0.0005638599395751953 nb_pixel_total : 32501 time to create 1 rle with old method : 0.03696298599243164 length of segment : 214 time for calcul the mask position with numpy : 0.025556564331054688 nb_pixel_total : 503663 time to create 1 rle with new method : 0.46199560165405273 length of segment : 1138 time for calcul the mask position with numpy : 0.0012295246124267578 nb_pixel_total : 60581 time to create 1 rle with old method : 0.06801748275756836 length of segment : 374 time for calcul the mask position with numpy : 0.0027303695678710938 nb_pixel_total : 41761 time to create 1 rle with old method : 0.05037736892700195 length of segment : 218 time for calcul the mask position with numpy : 0.007772684097290039 nb_pixel_total : 208999 time to create 1 rle with new method : 0.011735677719116211 length of segment : 417 time for calcul the mask position with numpy : 0.00445556640625 nb_pixel_total : 133253 time to create 1 rle with old method : 0.1500687599182129 length of segment : 392 time for calcul the mask position with numpy : 0.0002925395965576172 nb_pixel_total : 5844 time to create 1 rle with old method : 0.0068738460540771484 length of segment : 89 time for calcul the mask position with numpy : 0.0019714832305908203 nb_pixel_total : 40967 time to create 1 rle with old method : 0.047988176345825195 length of segment : 294 time for calcul the mask position with numpy : 0.005941629409790039 nb_pixel_total : 150012 time to create 1 rle with new method : 0.008559465408325195 length of segment : 486 time for calcul the mask position with numpy : 0.010239839553833008 nb_pixel_total : 232151 time to create 1 rle with new method : 0.01352071762084961 length of segment : 763 time for calcul the mask position with numpy : 0.004767656326293945 nb_pixel_total : 120262 time to create 1 rle with old method : 0.13596749305725098 length of segment : 316 time for calcul the mask position with numpy : 0.004647016525268555 nb_pixel_total : 182487 time to create 1 rle with new method : 0.008054018020629883 length of segment : 420 time for calcul the mask position with numpy : 0.0053098201751708984 nb_pixel_total : 136314 time to create 1 rle with old method : 0.15250706672668457 length of segment : 428 time for calcul the mask position with numpy : 0.0005283355712890625 nb_pixel_total : 12331 time to create 1 rle with old method : 0.014055728912353516 length of segment : 165 time for calcul the mask position with numpy : 0.0017559528350830078 nb_pixel_total : 48088 time to create 1 rle with old method : 0.05443000793457031 length of segment : 364 time for calcul the mask position with numpy : 0.0006256103515625 nb_pixel_total : 20749 time to create 1 rle with old method : 0.023182392120361328 length of segment : 217 time for calcul the mask position with numpy : 0.0011982917785644531 nb_pixel_total : 23647 time to create 1 rle with old method : 0.026830673217773438 length of segment : 204 time for calcul the mask position with numpy : 0.0017437934875488281 nb_pixel_total : 38434 time to create 1 rle with old method : 0.043218135833740234 length of segment : 366 time for calcul the mask position with numpy : 0.0008912086486816406 nb_pixel_total : 17996 time to create 1 rle with old method : 0.020251750946044922 length of segment : 222 time for calcul the mask position with numpy : 0.0007872581481933594 nb_pixel_total : 25301 time to create 1 rle with old method : 0.02919173240661621 length of segment : 186 time for calcul the mask position with numpy : 0.00021529197692871094 nb_pixel_total : 3173 time to create 1 rle with old method : 0.003797769546508789 length of segment : 83 time for calcul the mask position with numpy : 0.0019598007202148438 nb_pixel_total : 31529 time to create 1 rle with old method : 0.035382747650146484 length of segment : 344 time for calcul the mask position with numpy : 0.0014014244079589844 nb_pixel_total : 29834 time to create 1 rle with old method : 0.03315114974975586 length of segment : 316 time for calcul the mask position with numpy : 0.002966642379760742 nb_pixel_total : 66251 time to create 1 rle with old method : 0.07459139823913574 length of segment : 400 time for calcul the mask position with numpy : 0.0005700588226318359 nb_pixel_total : 9332 time to create 1 rle with old method : 0.010352373123168945 length of segment : 154 time for calcul the mask position with numpy : 0.017181396484375 nb_pixel_total : 349303 time to create 1 rle with new method : 0.025979042053222656 length of segment : 708 time for calcul the mask position with numpy : 0.00033211708068847656 nb_pixel_total : 6481 time to create 1 rle with old method : 0.00759434700012207 length of segment : 67 time for calcul the mask position with numpy : 0.0006568431854248047 nb_pixel_total : 19240 time to create 1 rle with old method : 0.021965503692626953 length of segment : 115 time for calcul the mask position with numpy : 0.00027942657470703125 nb_pixel_total : 4987 time to create 1 rle with old method : 0.006140470504760742 length of segment : 239 time for calcul the mask position with numpy : 0.0006270408630371094 nb_pixel_total : 27555 time to create 1 rle with old method : 0.031165122985839844 length of segment : 204 time for calcul the mask position with numpy : 0.0016372203826904297 nb_pixel_total : 49117 time to create 1 rle with old method : 0.054029226303100586 length of segment : 331 time for calcul the mask position with numpy : 0.0002665519714355469 nb_pixel_total : 15407 time to create 1 rle with old method : 0.0174560546875 length of segment : 105 time spent for convertir_results : 28.939398527145386 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 333 chid ids of type : 3594 Number RLEs to save : 90391 save missing photos in datou_result : time spend for datou_step_exec : 142.09606504440308 time spend to save output : 7.703786373138428 total time spend for step 1 : 149.7998514175415 step2:crop_condition Wed Apr 9 19:33:02 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 : 3594 Loading chi in step crop for list_pids : 13 ! batch 1 Loaded 333 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 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 ! 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 : 231 About to insert : list_path_to_insert length 231 new photo from crops ! About to upload 231 photos upload in portfolio : 3736932 init cache_photo without model_param we have 231 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744220037_2624324 we have uploaded 231 photos in the portfolio 3736932 time of upload the photos Elapsed time : 79.6306095123291 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 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 : 41 About to insert : list_path_to_insert length 41 new photo from crops ! About to upload 41 photos upload in portfolio : 3736932 init cache_photo without model_param we have 41 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744220126_2624324 we have uploaded 41 photos in the portfolio 3736932 time of upload the photos Elapsed time : 15.325374841690063 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 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 : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744220143_2624324 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.9564204216003418 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 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 : 52 About to insert : list_path_to_insert length 52 new photo from crops ! About to upload 52 photos upload in portfolio : 3736932 init cache_photo without model_param we have 52 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744220165_2624324 we have uploaded 52 photos in the portfolio 3736932 time of upload the photos Elapsed time : 29.21341824531555 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 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 : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 3736932 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744220196_2624324 we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 6.200106382369995 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 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 ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744220204_2624324 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.744908332824707 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350815765, 1350815760, 1350815755, 1350815746, 1350815654, 1350815508, 1350815501, 1350815493, 1350815491, 1350815489, 1350815420, 1350815414, 1350815411] Looping around the photos to save general results len do output : 333 /1350842839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842906Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842907Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842917Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842931Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842932Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842941Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842946Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842950Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842965Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842967Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350842997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843056Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843059Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843065Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843072Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843076Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843078Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843081Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843082Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843083Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843085Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843087Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843089Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843091Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843093Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843094Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843095Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843096Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843097Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843098Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843099Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843100Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843101Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843104Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843106Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843109Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843110Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843113Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843114Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843115Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843117Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843118Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843119Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843120Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843121Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843123Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843124Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843125Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843126Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843127Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843129Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843131Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843134Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843135Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843139Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843141Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843143Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843147Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843154Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843160Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843164Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843167Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843169Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843174Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843186Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843191Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843193Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843198Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843207Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843212Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843217Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843219Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843220Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843222Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843224Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843225Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843226Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843228Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843229Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843230Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843232Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843233Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843235Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843236Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843237Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843239Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843240Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843242Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843243Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843246Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843248Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843250Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843252Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843254Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843255Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843256Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843391Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843398Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843408Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843411Didn't retrieve data 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.Didn't retrieve data .Didn't retrieve data . /1350843737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350843751Didn'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 ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815765', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815760', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815755', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815746', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815654', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815508', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815501', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815493', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815491', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815489', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815420', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815414', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815411', None, None, None, None, None, '2735840') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1012 time used for this insertion : 0.12299036979675293 save_final save missing photos in datou_result : time spend for datou_step_exec : 222.57678270339966 time spend to save output : 0.13152360916137695 total time spend for step 2 : 222.70830631256104 step3:rle_unique_nms_with_priority Wed Apr 9 19:36:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 333 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 43 nb_hashtags : 3 time to prepare the origin masks : 3.9814181327819824 time for calcul the mask position with numpy : 0.6321773529052734 nb_pixel_total : 5967517 time to create 1 rle with new method : 0.9913349151611328 time for calcul the mask position with numpy : 0.0309598445892334 nb_pixel_total : 8802 time to create 1 rle with old method : 0.010288715362548828 time for calcul the mask position with numpy : 0.029187440872192383 nb_pixel_total : 44948 time to create 1 rle with old method : 0.050165414810180664 time for calcul the mask position with numpy : 0.029322385787963867 nb_pixel_total : 32873 time to create 1 rle with old method : 0.03686213493347168 time for calcul the mask position with numpy : 0.029074430465698242 nb_pixel_total : 81258 time to create 1 rle with old method : 0.09033584594726562 time for calcul the mask position with numpy : 0.030672550201416016 nb_pixel_total : 37903 time to create 1 rle with old method : 0.04311251640319824 time for calcul the mask position with numpy : 0.029303789138793945 nb_pixel_total : 8726 time to create 1 rle with old method : 0.009811639785766602 time for calcul the mask position with numpy : 0.029140472412109375 nb_pixel_total : 20386 time to create 1 rle with old method : 0.03579449653625488 time for calcul the mask position with numpy : 0.029384613037109375 nb_pixel_total : 36061 time to create 1 rle with old method : 0.04002499580383301 time for calcul the mask position with numpy : 0.028485536575317383 nb_pixel_total : 17215 time to create 1 rle with old method : 0.0191037654876709 time for calcul the mask position with numpy : 0.028351783752441406 nb_pixel_total : 9749 time to create 1 rle with old method : 0.010698556900024414 time for calcul the mask position with numpy : 0.02916264533996582 nb_pixel_total : 83784 time to create 1 rle with old method : 0.09003400802612305 time for calcul the mask position with numpy : 0.028003692626953125 nb_pixel_total : 56672 time to create 1 rle with old method : 0.06274938583374023 time for calcul the mask position with numpy : 0.03053569793701172 nb_pixel_total : 46064 time to create 1 rle with old method : 0.05298662185668945 time for calcul the mask position with numpy : 0.0287933349609375 nb_pixel_total : 6035 time to create 1 rle with old method : 0.006722927093505859 time for calcul the mask position with numpy : 0.028873682022094727 nb_pixel_total : 6223 time to create 1 rle with old method : 0.0069293975830078125 time for calcul the mask position with numpy : 0.028168678283691406 nb_pixel_total : 55047 time to create 1 rle with old method : 0.06139206886291504 time for calcul the mask position with numpy : 0.02893805503845215 nb_pixel_total : 27438 time to create 1 rle with old method : 0.030749082565307617 time for calcul the mask position with numpy : 0.028801679611206055 nb_pixel_total : 24863 time to create 1 rle with old method : 0.027518510818481445 time for calcul the mask position with numpy : 0.028881072998046875 nb_pixel_total : 37265 time to create 1 rle with old method : 0.04152965545654297 time for calcul the mask position with numpy : 0.02889871597290039 nb_pixel_total : 21088 time to create 1 rle with old method : 0.023540973663330078 time for calcul the mask position with numpy : 0.02842545509338379 nb_pixel_total : 45003 time to create 1 rle with old method : 0.057297468185424805 time for calcul the mask position with numpy : 0.032689809799194336 nb_pixel_total : 29122 time to create 1 rle with old method : 0.03974795341491699 time for calcul the mask position with numpy : 0.02898883819580078 nb_pixel_total : 15552 time to create 1 rle with old method : 0.017360925674438477 time for calcul the mask position with numpy : 0.028952360153198242 nb_pixel_total : 17296 time to create 1 rle with old method : 0.01937246322631836 time for calcul the mask position with numpy : 0.028348684310913086 nb_pixel_total : 18671 time to create 1 rle with old method : 0.02042222023010254 time for calcul the mask position with numpy : 0.02859807014465332 nb_pixel_total : 19649 time to create 1 rle with old method : 0.02167654037475586 time for calcul the mask position with numpy : 0.02830362319946289 nb_pixel_total : 24098 time to create 1 rle with old method : 0.02646613121032715 time for calcul the mask position with numpy : 0.028624296188354492 nb_pixel_total : 9234 time to create 1 rle with old method : 0.010026693344116211 time for calcul the mask position with numpy : 0.02811288833618164 nb_pixel_total : 19166 time to create 1 rle with old method : 0.020906448364257812 time for calcul the mask position with numpy : 0.028088092803955078 nb_pixel_total : 10505 time to create 1 rle with old method : 0.01149749755859375 time for calcul the mask position with numpy : 0.02784419059753418 nb_pixel_total : 4681 time to create 1 rle with old method : 0.005093574523925781 time for calcul the mask position with numpy : 0.028275251388549805 nb_pixel_total : 35203 time to create 1 rle with old method : 0.03945755958557129 time for calcul the mask position with numpy : 0.027507305145263672 nb_pixel_total : 15787 time to create 1 rle with old method : 0.01720261573791504 time for calcul the mask position with numpy : 0.027912139892578125 nb_pixel_total : 12719 time to create 1 rle with old method : 0.01390528678894043 time for calcul the mask position with numpy : 0.02893972396850586 nb_pixel_total : 15879 time to create 1 rle with old method : 0.017928361892700195 time for calcul the mask position with numpy : 0.0292665958404541 nb_pixel_total : 44946 time to create 1 rle with old method : 0.04968547821044922 time for calcul the mask position with numpy : 0.028656005859375 nb_pixel_total : 7673 time to create 1 rle with old method : 0.010246038436889648 time for calcul the mask position with numpy : 0.028794050216674805 nb_pixel_total : 14029 time to create 1 rle with old method : 0.015699386596679688 time for calcul the mask position with numpy : 0.028581857681274414 nb_pixel_total : 9776 time to create 1 rle with old method : 0.010990142822265625 time for calcul the mask position with numpy : 0.028729915618896484 nb_pixel_total : 4742 time to create 1 rle with old method : 0.005424022674560547 time for calcul the mask position with numpy : 0.028884172439575195 nb_pixel_total : 24172 time to create 1 rle with old method : 0.027168750762939453 time for calcul the mask position with numpy : 0.0289309024810791 nb_pixel_total : 3974 time to create 1 rle with old method : 0.0044896602630615234 time for calcul the mask position with numpy : 0.02916741371154785 nb_pixel_total : 18446 time to create 1 rle with old method : 0.020548343658447266 create new chi : 4.133793592453003 time to delete rle : 0.02408456802368164 batch 1 Loaded 87 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20512 TO DO : save crop sub photo not yet done ! save time : 1.5967638492584229 nb_obj : 40 nb_hashtags : 5 time to prepare the origin masks : 4.181286334991455 time for calcul the mask position with numpy : 0.21878647804260254 nb_pixel_total : 5689057 time to create 1 rle with new method : 1.039867877960205 time for calcul the mask position with numpy : 0.03334212303161621 nb_pixel_total : 10013 time to create 1 rle with old method : 0.018806934356689453 time for calcul the mask position with numpy : 0.030879497528076172 nb_pixel_total : 10642 time to create 1 rle with old method : 0.017766952514648438 time for calcul the mask position with numpy : 0.028576135635375977 nb_pixel_total : 18900 time to create 1 rle with old method : 0.020598888397216797 time for calcul the mask position with numpy : 0.030254125595092773 nb_pixel_total : 219606 time to create 1 rle with new method : 0.6351883411407471 time for calcul the mask position with numpy : 0.02758932113647461 nb_pixel_total : 16701 time to create 1 rle with old method : 0.01701951026916504 time for calcul the mask position with numpy : 0.026458740234375 nb_pixel_total : 2020 time to create 1 rle with old method : 0.002199411392211914 time for calcul the mask position with numpy : 0.02828049659729004 nb_pixel_total : 256463 time to create 1 rle with new method : 0.5927300453186035 time for calcul the mask position with numpy : 0.027231216430664062 nb_pixel_total : 12362 time to create 1 rle with old method : 0.012725830078125 time for calcul the mask position with numpy : 0.0274202823638916 nb_pixel_total : 609 time to create 1 rle with old method : 0.0008652210235595703 time for calcul the mask position with numpy : 0.02750110626220703 nb_pixel_total : 33594 time to create 1 rle with old method : 0.03507733345031738 time for calcul the mask position with numpy : 0.02725529670715332 nb_pixel_total : 20425 time to create 1 rle with old method : 0.02171802520751953 time for calcul the mask position with numpy : 0.029225587844848633 nb_pixel_total : 37104 time to create 1 rle with old method : 0.04106926918029785 time for calcul the mask position with numpy : 0.031004667282104492 nb_pixel_total : 14252 time to create 1 rle with old method : 0.01807093620300293 time for calcul the mask position with numpy : 0.029868125915527344 nb_pixel_total : 30426 time to create 1 rle with old method : 0.046149492263793945 time for calcul the mask position with numpy : 0.03395867347717285 nb_pixel_total : 61246 time to create 1 rle with old method : 0.07186746597290039 time for calcul the mask position with numpy : 0.03233814239501953 nb_pixel_total : 6444 time to create 1 rle with old method : 0.007048368453979492 time for calcul the mask position with numpy : 0.028117895126342773 nb_pixel_total : 12466 time to create 1 rle with old method : 0.013007879257202148 time for calcul the mask position with numpy : 0.027803659439086914 nb_pixel_total : 37258 time to create 1 rle with old method : 0.04000973701477051 time for calcul the mask position with numpy : 0.02977752685546875 nb_pixel_total : 75174 time to create 1 rle with old method : 0.08301520347595215 time for calcul the mask position with numpy : 0.0292205810546875 nb_pixel_total : 123351 time to create 1 rle with old method : 0.13111019134521484 time for calcul the mask position with numpy : 0.028055667877197266 nb_pixel_total : 6930 time to create 1 rle with old method : 0.007348299026489258 time for calcul the mask position with numpy : 0.02799248695373535 nb_pixel_total : 31126 time to create 1 rle with old method : 0.03319430351257324 time for calcul the mask position with numpy : 0.02824091911315918 nb_pixel_total : 2843 time to create 1 rle with old method : 0.003210783004760742 time for calcul the mask position with numpy : 0.02962660789489746 nb_pixel_total : 38439 time to create 1 rle with old method : 0.04207563400268555 time for calcul the mask position with numpy : 0.028288841247558594 nb_pixel_total : 26718 time to create 1 rle with old method : 0.028799772262573242 time for calcul the mask position with numpy : 0.029747962951660156 nb_pixel_total : 10117 time to create 1 rle with old method : 0.016454696655273438 time for calcul the mask position with numpy : 0.03264641761779785 nb_pixel_total : 33707 time to create 1 rle with old method : 0.04955625534057617 time for calcul the mask position with numpy : 0.03060317039489746 nb_pixel_total : 10676 time to create 1 rle with old method : 0.01773977279663086 time for calcul the mask position with numpy : 0.03333473205566406 nb_pixel_total : 13745 time to create 1 rle with old method : 0.0171048641204834 time for calcul the mask position with numpy : 0.028163909912109375 nb_pixel_total : 14192 time to create 1 rle with old method : 0.01544046401977539 time for calcul the mask position with numpy : 0.028376340866088867 nb_pixel_total : 12193 time to create 1 rle with old method : 0.012886762619018555 time for calcul the mask position with numpy : 0.027725696563720703 nb_pixel_total : 23847 time to create 1 rle with old method : 0.025231361389160156 time for calcul the mask position with numpy : 0.02829909324645996 nb_pixel_total : 15423 time to create 1 rle with old method : 0.01743912696838379 time for calcul the mask position with numpy : 0.029684066772460938 nb_pixel_total : 23592 time to create 1 rle with old method : 0.029015064239501953 time for calcul the mask position with numpy : 0.02989363670349121 nb_pixel_total : 13394 time to create 1 rle with old method : 0.015122175216674805 time for calcul the mask position with numpy : 0.030220508575439453 nb_pixel_total : 30532 time to create 1 rle with old method : 0.034180402755737305 time for calcul the mask position with numpy : 0.029296875 nb_pixel_total : 26742 time to create 1 rle with old method : 0.03164219856262207 time for calcul the mask position with numpy : 0.03168129920959473 nb_pixel_total : 7759 time to create 1 rle with old method : 0.008402824401855469 time for calcul the mask position with numpy : 0.02801227569580078 nb_pixel_total : 11668 time to create 1 rle with old method : 0.012827396392822266 time for calcul the mask position with numpy : 0.028653383255004883 nb_pixel_total : 8484 time to create 1 rle with old method : 0.009224653244018555 create new chi : 4.796530723571777 time to delete rle : 0.0032491683959960938 batch 1 Loaded 82 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19096 TO DO : save crop sub photo not yet done ! save time : 1.6743018627166748 nb_obj : 11 nb_hashtags : 3 time to prepare the origin masks : 5.626704692840576 time for calcul the mask position with numpy : 0.5070390701293945 nb_pixel_total : 6195546 time to create 1 rle with new method : 0.8405001163482666 time for calcul the mask position with numpy : 0.026134490966796875 nb_pixel_total : 32744 time to create 1 rle with old method : 0.03595566749572754 time for calcul the mask position with numpy : 0.024238109588623047 nb_pixel_total : 15321 time to create 1 rle with old method : 0.016085386276245117 time for calcul the mask position with numpy : 0.022520780563354492 nb_pixel_total : 11497 time to create 1 rle with old method : 0.01224827766418457 time for calcul the mask position with numpy : 0.022322893142700195 nb_pixel_total : 17119 time to create 1 rle with old method : 0.017993688583374023 time for calcul the mask position with numpy : 0.03959465026855469 nb_pixel_total : 14751 time to create 1 rle with old method : 0.015988588333129883 time for calcul the mask position with numpy : 0.03978610038757324 nb_pixel_total : 42534 time to create 1 rle with old method : 0.04599261283874512 time for calcul the mask position with numpy : 0.04613995552062988 nb_pixel_total : 552265 time to create 1 rle with new method : 0.890843391418457 time for calcul the mask position with numpy : 0.03788280487060547 nb_pixel_total : 22139 time to create 1 rle with old method : 0.02356553077697754 time for calcul the mask position with numpy : 0.0341336727142334 nb_pixel_total : 27578 time to create 1 rle with old method : 0.02968430519104004 time for calcul the mask position with numpy : 0.02607893943786621 nb_pixel_total : 68483 time to create 1 rle with old method : 0.07445001602172852 time for calcul the mask position with numpy : 0.029428720474243164 nb_pixel_total : 50263 time to create 1 rle with old method : 0.05604147911071777 create new chi : 2.9819698333740234 time to delete rle : 0.0014603137969970703 batch 1 Loaded 23 chid ids of type : 3594 +++++++++++Number RLEs to save : 7757 TO DO : save crop sub photo not yet done ! save time : 0.8216557502746582 nb_obj : 18 nb_hashtags : 3 time to prepare the origin masks : 6.386321067810059 time for calcul the mask position with numpy : 0.41818809509277344 nb_pixel_total : 5372233 time to create 1 rle with new method : 1.0952088832855225 time for calcul the mask position with numpy : 0.03624582290649414 nb_pixel_total : 137757 time to create 1 rle with old method : 0.17776250839233398 time for calcul the mask position with numpy : 0.03554701805114746 nb_pixel_total : 101382 time to create 1 rle with old method : 0.11196041107177734 time for calcul the mask position with numpy : 0.03651285171508789 nb_pixel_total : 82845 time to create 1 rle with old method : 0.09021663665771484 time for calcul the mask position with numpy : 0.03341960906982422 nb_pixel_total : 12073 time to create 1 rle with old method : 0.013031244277954102 time for calcul the mask position with numpy : 0.03363204002380371 nb_pixel_total : 34304 time to create 1 rle with old method : 0.05577683448791504 time for calcul the mask position with numpy : 0.03969216346740723 nb_pixel_total : 85601 time to create 1 rle with old method : 0.09745335578918457 time for calcul the mask position with numpy : 0.03713083267211914 nb_pixel_total : 51901 time to create 1 rle with old method : 0.05825090408325195 time for calcul the mask position with numpy : 0.035166025161743164 nb_pixel_total : 28353 time to create 1 rle with old method : 0.031438350677490234 time for calcul the mask position with numpy : 0.03643655776977539 nb_pixel_total : 33709 time to create 1 rle with old method : 0.040077924728393555 time for calcul the mask position with numpy : 0.04411458969116211 nb_pixel_total : 237714 time to create 1 rle with new method : 0.8889071941375732 time for calcul the mask position with numpy : 0.03464388847351074 nb_pixel_total : 221939 time to create 1 rle with new method : 0.9517731666564941 time for calcul the mask position with numpy : 0.03422212600708008 nb_pixel_total : 53961 time to create 1 rle with old method : 0.05990457534790039 time for calcul the mask position with numpy : 0.02783679962158203 nb_pixel_total : 344703 time to create 1 rle with new method : 0.7877902984619141 time for calcul the mask position with numpy : 0.021949291229248047 nb_pixel_total : 42625 time to create 1 rle with old method : 0.049035072326660156 time for calcul the mask position with numpy : 0.02290034294128418 nb_pixel_total : 33393 time to create 1 rle with old method : 0.03735470771789551 time for calcul the mask position with numpy : 0.027952909469604492 nb_pixel_total : 89095 time to create 1 rle with old method : 0.09922003746032715 time for calcul the mask position with numpy : 0.03597521781921387 nb_pixel_total : 66023 time to create 1 rle with old method : 0.09966063499450684 time for calcul the mask position with numpy : 0.03676581382751465 nb_pixel_total : 20629 time to create 1 rle with old method : 0.023198366165161133 create new chi : 5.905958890914917 time to delete rle : 0.003469228744506836 batch 1 Loaded 37 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++Number RLEs to save : 15575 TO DO : save crop sub photo not yet done ! save time : 4.953397512435913 nb_obj : 15 nb_hashtags : 3 time to prepare the origin masks : 8.077801942825317 time for calcul the mask position with numpy : 0.4860055446624756 nb_pixel_total : 5748495 time to create 1 rle with new method : 0.44382429122924805 time for calcul the mask position with numpy : 0.029985666275024414 nb_pixel_total : 88043 time to create 1 rle with old method : 0.09523224830627441 time for calcul the mask position with numpy : 0.022223472595214844 nb_pixel_total : 40368 time to create 1 rle with old method : 0.0445401668548584 time for calcul the mask position with numpy : 0.023041963577270508 nb_pixel_total : 137028 time to create 1 rle with old method : 0.15268754959106445 time for calcul the mask position with numpy : 0.022158145904541016 nb_pixel_total : 14298 time to create 1 rle with old method : 0.016022205352783203 time for calcul the mask position with numpy : 0.02188897132873535 nb_pixel_total : 15923 time to create 1 rle with old method : 0.017615795135498047 time for calcul the mask position with numpy : 0.023027658462524414 nb_pixel_total : 116104 time to create 1 rle with old method : 0.1285865306854248 time for calcul the mask position with numpy : 0.02255392074584961 nb_pixel_total : 114228 time to create 1 rle with old method : 0.12599921226501465 time for calcul the mask position with numpy : 0.0211641788482666 nb_pixel_total : 13757 time to create 1 rle with old method : 0.015526771545410156 time for calcul the mask position with numpy : 0.022440671920776367 nb_pixel_total : 107014 time to create 1 rle with old method : 0.11725974082946777 time for calcul the mask position with numpy : 0.021559953689575195 nb_pixel_total : 63202 time to create 1 rle with old method : 0.06884527206420898 time for calcul the mask position with numpy : 0.022758007049560547 nb_pixel_total : 12891 time to create 1 rle with old method : 0.013821601867675781 time for calcul the mask position with numpy : 0.02322244644165039 nb_pixel_total : 195401 time to create 1 rle with new method : 1.0619168281555176 time for calcul the mask position with numpy : 0.02127838134765625 nb_pixel_total : 105383 time to create 1 rle with old method : 0.11334466934204102 time for calcul the mask position with numpy : 0.020671367645263672 nb_pixel_total : 61778 time to create 1 rle with old method : 0.06918883323669434 time for calcul the mask position with numpy : 0.024155855178833008 nb_pixel_total : 216327 time to create 1 rle with new method : 0.6892755031585693 create new chi : 4.085384368896484 time to delete rle : 0.0030219554901123047 batch 1 Loaded 31 chid ids of type : 3594 ++++++++++++++++++Number RLEs to save : 13295 TO DO : save crop sub photo not yet done ! save time : 9.060381174087524 nb_obj : 24 nb_hashtags : 4 time to prepare the origin masks : 11.132210969924927 time for calcul the mask position with numpy : 0.9153132438659668 nb_pixel_total : 5730799 time to create 1 rle with new method : 0.6594526767730713 time for calcul the mask position with numpy : 0.03479361534118652 nb_pixel_total : 73153 time to create 1 rle with old method : 0.08211684226989746 time for calcul the mask position with numpy : 0.021241188049316406 nb_pixel_total : 7319 time to create 1 rle with old method : 0.008332014083862305 time for calcul the mask position with numpy : 0.023444414138793945 nb_pixel_total : 77491 time to create 1 rle with old method : 0.08608770370483398 time for calcul the mask position with numpy : 0.023627042770385742 nb_pixel_total : 19517 time to create 1 rle with old method : 0.021983623504638672 time for calcul the mask position with numpy : 0.023484230041503906 nb_pixel_total : 63424 time to create 1 rle with old method : 0.07038474082946777 time for calcul the mask position with numpy : 0.021696090698242188 nb_pixel_total : 38805 time to create 1 rle with old method : 0.04330086708068848 time for calcul the mask position with numpy : 0.021895408630371094 nb_pixel_total : 65059 time to create 1 rle with old method : 0.07209157943725586 time for calcul the mask position with numpy : 0.02238917350769043 nb_pixel_total : 17827 time to create 1 rle with old method : 0.0200960636138916 time for calcul the mask position with numpy : 0.02297210693359375 nb_pixel_total : 44904 time to create 1 rle with old method : 0.04938912391662598 time for calcul the mask position with numpy : 0.022028446197509766 nb_pixel_total : 17381 time to create 1 rle with old method : 0.019410371780395508 time for calcul the mask position with numpy : 0.022641897201538086 nb_pixel_total : 19267 time to create 1 rle with old method : 0.021628618240356445 time for calcul the mask position with numpy : 0.02240276336669922 nb_pixel_total : 53062 time to create 1 rle with old method : 0.059720516204833984 time for calcul the mask position with numpy : 0.022774934768676758 nb_pixel_total : 65033 time to create 1 rle with old method : 0.07290410995483398 time for calcul the mask position with numpy : 0.02420639991760254 nb_pixel_total : 14115 time to create 1 rle with old method : 0.015819311141967773 time for calcul the mask position with numpy : 0.02467966079711914 nb_pixel_total : 190266 time to create 1 rle with new method : 0.8989753723144531 time for calcul the mask position with numpy : 0.02332472801208496 nb_pixel_total : 18929 time to create 1 rle with old method : 0.02115607261657715 time for calcul the mask position with numpy : 0.02430248260498047 nb_pixel_total : 245128 time to create 1 rle with new method : 0.7333705425262451 time for calcul the mask position with numpy : 0.024786710739135742 nb_pixel_total : 21580 time to create 1 rle with old method : 0.026331663131713867 time for calcul the mask position with numpy : 0.023372411727905273 nb_pixel_total : 44287 time to create 1 rle with old method : 0.04942679405212402 time for calcul the mask position with numpy : 0.022221088409423828 nb_pixel_total : 26407 time to create 1 rle with old method : 0.029369831085205078 time for calcul the mask position with numpy : 0.021803855895996094 nb_pixel_total : 51595 time to create 1 rle with old method : 0.0576634407043457 time for calcul the mask position with numpy : 0.022475719451904297 nb_pixel_total : 69814 time to create 1 rle with old method : 0.0791621208190918 time for calcul the mask position with numpy : 0.023395299911499023 nb_pixel_total : 49486 time to create 1 rle with old method : 0.055402517318725586 time for calcul the mask position with numpy : 0.022577285766601562 nb_pixel_total : 25592 time to create 1 rle with old method : 0.028686046600341797 create new chi : 4.8489062786102295 time to delete rle : 0.004165172576904297 batch 1 Loaded 49 chid ids of type : 3594 ++++++++++++++++++++++++++++Number RLEs to save : 15421 TO DO : save crop sub photo not yet done ! save time : 3.870295763015747 nb_obj : 38 nb_hashtags : 3 time to prepare the origin masks : 3.9362497329711914 time for calcul the mask position with numpy : 1.240450143814087 nb_pixel_total : 6329255 time to create 1 rle with new method : 1.3334174156188965 time for calcul the mask position with numpy : 0.028866052627563477 nb_pixel_total : 14343 time to create 1 rle with old method : 0.016193628311157227 time for calcul the mask position with numpy : 0.029133319854736328 nb_pixel_total : 8745 time to create 1 rle with old method : 0.013036727905273438 time for calcul the mask position with numpy : 0.04087257385253906 nb_pixel_total : 67040 time to create 1 rle with old method : 0.08111739158630371 time for calcul the mask position with numpy : 0.02917623519897461 nb_pixel_total : 8025 time to create 1 rle with old method : 0.010346651077270508 time for calcul the mask position with numpy : 0.02892470359802246 nb_pixel_total : 10824 time to create 1 rle with old method : 0.014636754989624023 time for calcul the mask position with numpy : 0.028453588485717773 nb_pixel_total : 54574 time to create 1 rle with old method : 0.06070685386657715 time for calcul the mask position with numpy : 0.029682636260986328 nb_pixel_total : 27794 time to create 1 rle with old method : 0.031301021575927734 time for calcul the mask position with numpy : 0.028799772262573242 nb_pixel_total : 17559 time to create 1 rle with old method : 0.020023584365844727 time for calcul the mask position with numpy : 0.029509544372558594 nb_pixel_total : 30495 time to create 1 rle with old method : 0.03474926948547363 time for calcul the mask position with numpy : 0.0284578800201416 nb_pixel_total : 22784 time to create 1 rle with old method : 0.02550792694091797 time for calcul the mask position with numpy : 0.028378963470458984 nb_pixel_total : 29643 time to create 1 rle with old method : 0.03345131874084473 time for calcul the mask position with numpy : 0.028667211532592773 nb_pixel_total : 13761 time to create 1 rle with old method : 0.015890836715698242 time for calcul the mask position with numpy : 0.03244447708129883 nb_pixel_total : 24367 time to create 1 rle with old method : 0.03133511543273926 time for calcul the mask position with numpy : 0.0302731990814209 nb_pixel_total : 5567 time to create 1 rle with old method : 0.006179332733154297 time for calcul the mask position with numpy : 0.028197288513183594 nb_pixel_total : 12631 time to create 1 rle with old method : 0.01373291015625 time for calcul the mask position with numpy : 0.028262615203857422 nb_pixel_total : 45699 time to create 1 rle with old method : 0.06972599029541016 time for calcul the mask position with numpy : 0.032071590423583984 nb_pixel_total : 8937 time to create 1 rle with old method : 0.009843111038208008 time for calcul the mask position with numpy : 0.027451038360595703 nb_pixel_total : 15005 time to create 1 rle with old method : 0.015950441360473633 time for calcul the mask position with numpy : 0.027684926986694336 nb_pixel_total : 11252 time to create 1 rle with old method : 0.01205134391784668 time for calcul the mask position with numpy : 0.026557207107543945 nb_pixel_total : 9935 time to create 1 rle with old method : 0.010437726974487305 time for calcul the mask position with numpy : 0.027105331420898438 nb_pixel_total : 13309 time to create 1 rle with old method : 0.014375925064086914 time for calcul the mask position with numpy : 0.027320384979248047 nb_pixel_total : 19642 time to create 1 rle with old method : 0.020944595336914062 time for calcul the mask position with numpy : 0.027812719345092773 nb_pixel_total : 14407 time to create 1 rle with old method : 0.015751361846923828 time for calcul the mask position with numpy : 0.027444124221801758 nb_pixel_total : 8447 time to create 1 rle with old method : 0.009113788604736328 time for calcul the mask position with numpy : 0.027850866317749023 nb_pixel_total : 4722 time to create 1 rle with old method : 0.00526738166809082 time for calcul the mask position with numpy : 0.027608633041381836 nb_pixel_total : 8408 time to create 1 rle with old method : 0.009342670440673828 time for calcul the mask position with numpy : 0.028237581253051758 nb_pixel_total : 21871 time to create 1 rle with old method : 0.023485660552978516 time for calcul the mask position with numpy : 0.02730107307434082 nb_pixel_total : 12184 time to create 1 rle with old method : 0.012753486633300781 time for calcul the mask position with numpy : 0.027159929275512695 nb_pixel_total : 25727 time to create 1 rle with old method : 0.0318753719329834 time for calcul the mask position with numpy : 0.03128981590270996 nb_pixel_total : 8704 time to create 1 rle with old method : 0.009424448013305664 time for calcul the mask position with numpy : 0.0297849178314209 nb_pixel_total : 22995 time to create 1 rle with old method : 0.025223970413208008 time for calcul the mask position with numpy : 0.02874922752380371 nb_pixel_total : 8907 time to create 1 rle with old method : 0.010141134262084961 time for calcul the mask position with numpy : 0.028876781463623047 nb_pixel_total : 12439 time to create 1 rle with old method : 0.014023780822753906 time for calcul the mask position with numpy : 0.0288846492767334 nb_pixel_total : 7648 time to create 1 rle with old method : 0.008555412292480469 time for calcul the mask position with numpy : 0.02910590171813965 nb_pixel_total : 44737 time to create 1 rle with old method : 0.04991626739501953 time for calcul the mask position with numpy : 0.0290524959564209 nb_pixel_total : 21971 time to create 1 rle with old method : 0.024306058883666992 time for calcul the mask position with numpy : 0.027865886688232422 nb_pixel_total : 11964 time to create 1 rle with old method : 0.013093948364257812 time for calcul the mask position with numpy : 0.027594804763793945 nb_pixel_total : 13923 time to create 1 rle with old method : 0.014748096466064453 create new chi : 4.553075075149536 time to delete rle : 0.002324819564819336 batch 1 Loaded 77 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 14962 TO DO : save crop sub photo not yet done ! save time : 1.2261574268341064 nb_obj : 30 nb_hashtags : 4 time to prepare the origin masks : 3.733577013015747 time for calcul the mask position with numpy : 0.9316418170928955 nb_pixel_total : 6193349 time to create 1 rle with new method : 1.4013774394989014 time for calcul the mask position with numpy : 0.02864217758178711 nb_pixel_total : 4385 time to create 1 rle with old method : 0.005161285400390625 time for calcul the mask position with numpy : 0.028612852096557617 nb_pixel_total : 28123 time to create 1 rle with old method : 0.030855655670166016 time for calcul the mask position with numpy : 0.02854132652282715 nb_pixel_total : 73004 time to create 1 rle with old method : 0.07982945442199707 time for calcul the mask position with numpy : 0.028417348861694336 nb_pixel_total : 13536 time to create 1 rle with old method : 0.015012502670288086 time for calcul the mask position with numpy : 0.029146432876586914 nb_pixel_total : 26334 time to create 1 rle with old method : 0.029591083526611328 time for calcul the mask position with numpy : 0.029144287109375 nb_pixel_total : 30143 time to create 1 rle with old method : 0.03400135040283203 time for calcul the mask position with numpy : 0.02924180030822754 nb_pixel_total : 23599 time to create 1 rle with old method : 0.02665233612060547 time for calcul the mask position with numpy : 0.02880072593688965 nb_pixel_total : 25012 time to create 1 rle with old method : 0.0280764102935791 time for calcul the mask position with numpy : 0.028795957565307617 nb_pixel_total : 74369 time to create 1 rle with old method : 0.08271217346191406 time for calcul the mask position with numpy : 0.028475284576416016 nb_pixel_total : 19936 time to create 1 rle with old method : 0.02188587188720703 time for calcul the mask position with numpy : 0.02890181541442871 nb_pixel_total : 12784 time to create 1 rle with old method : 0.014441728591918945 time for calcul the mask position with numpy : 0.02902984619140625 nb_pixel_total : 17324 time to create 1 rle with old method : 0.019384145736694336 time for calcul the mask position with numpy : 0.02914714813232422 nb_pixel_total : 48158 time to create 1 rle with old method : 0.0539088249206543 time for calcul the mask position with numpy : 0.028769969940185547 nb_pixel_total : 31049 time to create 1 rle with old method : 0.03762412071228027 time for calcul the mask position with numpy : 0.02797555923461914 nb_pixel_total : 35602 time to create 1 rle with old method : 0.0388486385345459 time for calcul the mask position with numpy : 0.03080272674560547 nb_pixel_total : 24442 time to create 1 rle with old method : 0.027066707611083984 time for calcul the mask position with numpy : 0.028763771057128906 nb_pixel_total : 61076 time to create 1 rle with old method : 0.06643939018249512 time for calcul the mask position with numpy : 0.028490543365478516 nb_pixel_total : 9717 time to create 1 rle with old method : 0.010873794555664062 time for calcul the mask position with numpy : 0.028652429580688477 nb_pixel_total : 9787 time to create 1 rle with old method : 0.01099538803100586 time for calcul the mask position with numpy : 0.028765439987182617 nb_pixel_total : 56391 time to create 1 rle with old method : 0.06290864944458008 time for calcul the mask position with numpy : 0.029102802276611328 nb_pixel_total : 15362 time to create 1 rle with old method : 0.01792168617248535 time for calcul the mask position with numpy : 0.029018878936767578 nb_pixel_total : 23615 time to create 1 rle with old method : 0.02608489990234375 time for calcul the mask position with numpy : 0.0278012752532959 nb_pixel_total : 6728 time to create 1 rle with old method : 0.0071964263916015625 time for calcul the mask position with numpy : 0.02802729606628418 nb_pixel_total : 6867 time to create 1 rle with old method : 0.00739741325378418 time for calcul the mask position with numpy : 0.02865123748779297 nb_pixel_total : 67761 time to create 1 rle with old method : 0.07373309135437012 time for calcul the mask position with numpy : 0.02818155288696289 nb_pixel_total : 24775 time to create 1 rle with old method : 0.026813030242919922 time for calcul the mask position with numpy : 0.027742624282836914 nb_pixel_total : 59769 time to create 1 rle with old method : 0.0616452693939209 time for calcul the mask position with numpy : 0.026236295700073242 nb_pixel_total : 7842 time to create 1 rle with old method : 0.008230924606323242 time for calcul the mask position with numpy : 0.027442455291748047 nb_pixel_total : 14003 time to create 1 rle with old method : 0.015338659286499023 time for calcul the mask position with numpy : 0.02737593650817871 nb_pixel_total : 5398 time to create 1 rle with old method : 0.005734443664550781 create new chi : 4.173874378204346 time to delete rle : 0.0021905899047851562 batch 1 Loaded 61 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 14546 TO DO : save crop sub photo not yet done ! save time : 6.5649285316467285 nb_obj : 39 nb_hashtags : 4 time to prepare the origin masks : 3.7669224739074707 time for calcul the mask position with numpy : 0.5068628787994385 nb_pixel_total : 5938641 time to create 1 rle with new method : 0.739856481552124 time for calcul the mask position with numpy : 0.03282046318054199 nb_pixel_total : 29464 time to create 1 rle with old method : 0.04123258590698242 time for calcul the mask position with numpy : 0.028745174407958984 nb_pixel_total : 35419 time to create 1 rle with old method : 0.039148569107055664 time for calcul the mask position with numpy : 0.02861189842224121 nb_pixel_total : 69800 time to create 1 rle with old method : 0.07707595825195312 time for calcul the mask position with numpy : 0.027998685836791992 nb_pixel_total : 14312 time to create 1 rle with old method : 0.015764951705932617 time for calcul the mask position with numpy : 0.028214216232299805 nb_pixel_total : 66181 time to create 1 rle with old method : 0.07478976249694824 time for calcul the mask position with numpy : 0.029093027114868164 nb_pixel_total : 18909 time to create 1 rle with old method : 0.02121257781982422 time for calcul the mask position with numpy : 0.029079198837280273 nb_pixel_total : 63845 time to create 1 rle with old method : 0.07244110107421875 time for calcul the mask position with numpy : 0.028356552124023438 nb_pixel_total : 20336 time to create 1 rle with old method : 0.022646188735961914 time for calcul the mask position with numpy : 0.028725147247314453 nb_pixel_total : 46599 time to create 1 rle with old method : 0.051798343658447266 time for calcul the mask position with numpy : 0.028738021850585938 nb_pixel_total : 9828 time to create 1 rle with old method : 0.010766029357910156 time for calcul the mask position with numpy : 0.028594017028808594 nb_pixel_total : 29568 time to create 1 rle with old method : 0.032532691955566406 time for calcul the mask position with numpy : 0.028181076049804688 nb_pixel_total : 11434 time to create 1 rle with old method : 0.012766599655151367 time for calcul the mask position with numpy : 0.028429508209228516 nb_pixel_total : 7281 time to create 1 rle with old method : 0.008152961730957031 time for calcul the mask position with numpy : 0.02874135971069336 nb_pixel_total : 23040 time to create 1 rle with old method : 0.025668621063232422 time for calcul the mask position with numpy : 0.02814650535583496 nb_pixel_total : 18884 time to create 1 rle with old method : 0.02044510841369629 time for calcul the mask position with numpy : 0.028473854064941406 nb_pixel_total : 60405 time to create 1 rle with old method : 0.06662631034851074 time for calcul the mask position with numpy : 0.02842879295349121 nb_pixel_total : 26510 time to create 1 rle with old method : 0.029583215713500977 time for calcul the mask position with numpy : 0.028786897659301758 nb_pixel_total : 20254 time to create 1 rle with old method : 0.022774934768676758 time for calcul the mask position with numpy : 0.02898883819580078 nb_pixel_total : 20900 time to create 1 rle with old method : 0.025284528732299805 time for calcul the mask position with numpy : 0.029124021530151367 nb_pixel_total : 64676 time to create 1 rle with old method : 0.07102799415588379 time for calcul the mask position with numpy : 0.028374910354614258 nb_pixel_total : 13370 time to create 1 rle with old method : 0.01478719711303711 time for calcul the mask position with numpy : 0.027648448944091797 nb_pixel_total : 14213 time to create 1 rle with old method : 0.015118598937988281 time for calcul the mask position with numpy : 0.027587890625 nb_pixel_total : 11965 time to create 1 rle with old method : 0.012520313262939453 time for calcul the mask position with numpy : 0.027575254440307617 nb_pixel_total : 30383 time to create 1 rle with old method : 0.03305482864379883 time for calcul the mask position with numpy : 0.026913166046142578 nb_pixel_total : 11610 time to create 1 rle with old method : 0.012600421905517578 time for calcul the mask position with numpy : 0.027637481689453125 nb_pixel_total : 20657 time to create 1 rle with old method : 0.022409439086914062 time for calcul the mask position with numpy : 0.02802109718322754 nb_pixel_total : 43532 time to create 1 rle with old method : 0.04712653160095215 time for calcul the mask position with numpy : 0.028192758560180664 nb_pixel_total : 12893 time to create 1 rle with old method : 0.014577150344848633 time for calcul the mask position with numpy : 0.027995586395263672 nb_pixel_total : 16036 time to create 1 rle with old method : 0.01747584342956543 time for calcul the mask position with numpy : 0.027915239334106445 nb_pixel_total : 25280 time to create 1 rle with old method : 0.027614355087280273 time for calcul the mask position with numpy : 0.028400897979736328 nb_pixel_total : 5525 time to create 1 rle with old method : 0.00625157356262207 time for calcul the mask position with numpy : 0.029806137084960938 nb_pixel_total : 100012 time to create 1 rle with old method : 0.11570405960083008 time for calcul the mask position with numpy : 0.029090404510498047 nb_pixel_total : 86710 time to create 1 rle with old method : 0.09619736671447754 time for calcul the mask position with numpy : 0.028498172760009766 nb_pixel_total : 7415 time to create 1 rle with old method : 0.008245706558227539 time for calcul the mask position with numpy : 0.028175830841064453 nb_pixel_total : 15205 time to create 1 rle with old method : 0.016733884811401367 time for calcul the mask position with numpy : 0.02881169319152832 nb_pixel_total : 6226 time to create 1 rle with old method : 0.00714564323425293 time for calcul the mask position with numpy : 0.02886033058166504 nb_pixel_total : 4387 time to create 1 rle with old method : 0.004951000213623047 time for calcul the mask position with numpy : 0.028844833374023438 nb_pixel_total : 19884 time to create 1 rle with old method : 0.022330045700073242 time for calcul the mask position with numpy : 0.028754472732543945 nb_pixel_total : 8651 time to create 1 rle with old method : 0.009737730026245117 create new chi : 3.6467127799987793 time to delete rle : 0.004908084869384766 batch 1 Loaded 79 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21832 TO DO : save crop sub photo not yet done ! save time : 2.2359697818756104 nb_obj : 19 nb_hashtags : 3 time to prepare the origin masks : 8.21411919593811 time for calcul the mask position with numpy : 0.8147969245910645 nb_pixel_total : 5899350 time to create 1 rle with new method : 0.8546268939971924 time for calcul the mask position with numpy : 0.019591331481933594 nb_pixel_total : 35587 time to create 1 rle with old method : 0.036965131759643555 time for calcul the mask position with numpy : 0.019884586334228516 nb_pixel_total : 963 time to create 1 rle with old method : 0.0012087821960449219 time for calcul the mask position with numpy : 0.021413803100585938 nb_pixel_total : 127225 time to create 1 rle with old method : 0.13460493087768555 time for calcul the mask position with numpy : 0.021056175231933594 nb_pixel_total : 116857 time to create 1 rle with old method : 0.12394094467163086 time for calcul the mask position with numpy : 0.019382476806640625 nb_pixel_total : 14537 time to create 1 rle with old method : 0.015327930450439453 time for calcul the mask position with numpy : 0.020478487014770508 nb_pixel_total : 35455 time to create 1 rle with old method : 0.0370335578918457 time for calcul the mask position with numpy : 0.0207366943359375 nb_pixel_total : 83505 time to create 1 rle with old method : 0.09041571617126465 time for calcul the mask position with numpy : 0.0225679874420166 nb_pixel_total : 62739 time to create 1 rle with old method : 0.06826019287109375 time for calcul the mask position with numpy : 0.02039337158203125 nb_pixel_total : 28444 time to create 1 rle with old method : 0.03145647048950195 time for calcul the mask position with numpy : 0.021407604217529297 nb_pixel_total : 18558 time to create 1 rle with old method : 0.020799636840820312 time for calcul the mask position with numpy : 0.02528834342956543 nb_pixel_total : 104297 time to create 1 rle with old method : 0.11156535148620605 time for calcul the mask position with numpy : 0.020635128021240234 nb_pixel_total : 115217 time to create 1 rle with old method : 0.12138724327087402 time for calcul the mask position with numpy : 0.021386146545410156 nb_pixel_total : 35705 time to create 1 rle with old method : 0.03831124305725098 time for calcul the mask position with numpy : 0.020596742630004883 nb_pixel_total : 50721 time to create 1 rle with old method : 0.05424761772155762 time for calcul the mask position with numpy : 0.02059030532836914 nb_pixel_total : 9316 time to create 1 rle with old method : 0.010025501251220703 time for calcul the mask position with numpy : 0.021097898483276367 nb_pixel_total : 81498 time to create 1 rle with old method : 0.08737564086914062 time for calcul the mask position with numpy : 0.02128911018371582 nb_pixel_total : 100906 time to create 1 rle with old method : 0.10956740379333496 time for calcul the mask position with numpy : 0.023258447647094727 nb_pixel_total : 93022 time to create 1 rle with old method : 0.09946393966674805 time for calcul the mask position with numpy : 0.021062612533569336 nb_pixel_total : 36338 time to create 1 rle with old method : 0.038744211196899414 create new chi : 3.333096742630005 time to delete rle : 0.002042531967163086 batch 1 Loaded 39 chid ids of type : 3594 ++++++++++++++++++++++Number RLEs to save : 14733 TO DO : save crop sub photo not yet done ! save time : 1.413701057434082 nb_obj : 15 nb_hashtags : 3 time to prepare the origin masks : 7.963194131851196 time for calcul the mask position with numpy : 1.1560676097869873 nb_pixel_total : 5958634 time to create 1 rle with new method : 0.7621119022369385 time for calcul the mask position with numpy : 0.020298242568969727 nb_pixel_total : 26452 time to create 1 rle with old method : 0.029705524444580078 time for calcul the mask position with numpy : 0.02200150489807129 nb_pixel_total : 58509 time to create 1 rle with old method : 0.06540584564208984 time for calcul the mask position with numpy : 0.021717548370361328 nb_pixel_total : 26572 time to create 1 rle with old method : 0.02965068817138672 time for calcul the mask position with numpy : 0.02689504623413086 nb_pixel_total : 62682 time to create 1 rle with old method : 0.07108926773071289 time for calcul the mask position with numpy : 0.020984411239624023 nb_pixel_total : 62051 time to create 1 rle with old method : 0.06879568099975586 time for calcul the mask position with numpy : 0.021547794342041016 nb_pixel_total : 40964 time to create 1 rle with old method : 0.04422163963317871 time for calcul the mask position with numpy : 0.020517587661743164 nb_pixel_total : 15986 time to create 1 rle with old method : 0.017261743545532227 time for calcul the mask position with numpy : 0.02411675453186035 nb_pixel_total : 314500 time to create 1 rle with new method : 0.6726417541503906 time for calcul the mask position with numpy : 0.027748823165893555 nb_pixel_total : 16254 time to create 1 rle with old method : 0.021915197372436523 time for calcul the mask position with numpy : 0.021587848663330078 nb_pixel_total : 73016 time to create 1 rle with old method : 0.07768559455871582 time for calcul the mask position with numpy : 0.02121901512145996 nb_pixel_total : 44192 time to create 1 rle with old method : 0.050415754318237305 time for calcul the mask position with numpy : 0.024979591369628906 nb_pixel_total : 27834 time to create 1 rle with old method : 0.03127908706665039 time for calcul the mask position with numpy : 0.0222012996673584 nb_pixel_total : 84779 time to create 1 rle with old method : 0.0912318229675293 time for calcul the mask position with numpy : 0.021861553192138672 nb_pixel_total : 131487 time to create 1 rle with old method : 0.13998007774353027 time for calcul the mask position with numpy : 0.021746158599853516 nb_pixel_total : 106328 time to create 1 rle with old method : 0.11378788948059082 create new chi : 3.855664014816284 time to delete rle : 0.0019092559814453125 batch 1 Loaded 31 chid ids of type : 3594 ++++++++++++++++++++++++++++++Number RLEs to save : 13288 TO DO : save crop sub photo not yet done ! save time : 1.0196905136108398 nb_obj : 12 nb_hashtags : 3 time to prepare the origin masks : 5.114835262298584 time for calcul the mask position with numpy : 0.3238029479980469 nb_pixel_total : 4838975 time to create 1 rle with new method : 0.9753503799438477 time for calcul the mask position with numpy : 0.020679712295532227 nb_pixel_total : 11027 time to create 1 rle with old method : 0.01210641860961914 time for calcul the mask position with numpy : 0.020919084548950195 nb_pixel_total : 60581 time to create 1 rle with old method : 0.06783437728881836 time for calcul the mask position with numpy : 0.027658939361572266 nb_pixel_total : 503663 time to create 1 rle with new method : 1.1913819313049316 time for calcul the mask position with numpy : 0.020587921142578125 nb_pixel_total : 32501 time to create 1 rle with old method : 0.0343625545501709 time for calcul the mask position with numpy : 0.01991415023803711 nb_pixel_total : 123215 time to create 1 rle with old method : 0.15691351890563965 time for calcul the mask position with numpy : 0.02091193199157715 nb_pixel_total : 57764 time to create 1 rle with old method : 0.05904102325439453 time for calcul the mask position with numpy : 0.02006077766418457 nb_pixel_total : 17867 time to create 1 rle with old method : 0.01838970184326172 time for calcul the mask position with numpy : 0.019741058349609375 nb_pixel_total : 20754 time to create 1 rle with old method : 0.021884441375732422 time for calcul the mask position with numpy : 0.021287202835083008 nb_pixel_total : 255134 time to create 1 rle with new method : 0.9606037139892578 time for calcul the mask position with numpy : 0.02254319190979004 nb_pixel_total : 104909 time to create 1 rle with old method : 0.14600110054016113 time for calcul the mask position with numpy : 0.023520231246948242 nb_pixel_total : 115129 time to create 1 rle with old method : 0.14855551719665527 time for calcul the mask position with numpy : 0.031026840209960938 nb_pixel_total : 908721 time to create 1 rle with new method : 0.7395682334899902 create new chi : 5.22674298286438 time to delete rle : 0.002257823944091797 batch 1 Loaded 25 chid ids of type : 3594 ++++++++++++++++++++++++++++++Number RLEs to save : 14481 TO DO : save crop sub photo not yet done ! save time : 1.2218983173370361 nb_obj : 28 nb_hashtags : 4 time to prepare the origin masks : 4.5023887157440186 time for calcul the mask position with numpy : 0.7327361106872559 nb_pixel_total : 5066096 time to create 1 rle with new method : 0.5778334140777588 time for calcul the mask position with numpy : 0.029216766357421875 nb_pixel_total : 2752 time to create 1 rle with old method : 0.0031921863555908203 time for calcul the mask position with numpy : 0.029356002807617188 nb_pixel_total : 17271 time to create 1 rle with old method : 0.01949000358581543 time for calcul the mask position with numpy : 0.029485702514648438 nb_pixel_total : 133253 time to create 1 rle with old method : 0.14562201499938965 time for calcul the mask position with numpy : 0.02833390235900879 nb_pixel_total : 17996 time to create 1 rle with old method : 0.019620418548583984 time for calcul the mask position with numpy : 0.028020381927490234 nb_pixel_total : 23647 time to create 1 rle with old method : 0.025250911712646484 time for calcul the mask position with numpy : 0.02786707878112793 nb_pixel_total : 29834 time to create 1 rle with old method : 0.03171944618225098 time for calcul the mask position with numpy : 0.027664899826049805 nb_pixel_total : 25301 time to create 1 rle with old method : 0.027562618255615234 time for calcul the mask position with numpy : 0.0274965763092041 nb_pixel_total : 19240 time to create 1 rle with old method : 0.020287752151489258 time for calcul the mask position with numpy : 0.02704644203186035 nb_pixel_total : 9332 time to create 1 rle with old method : 0.010210752487182617 time for calcul the mask position with numpy : 0.028262853622436523 nb_pixel_total : 6481 time to create 1 rle with old method : 0.007212162017822266 time for calcul the mask position with numpy : 0.029009580612182617 nb_pixel_total : 20749 time to create 1 rle with old method : 0.02684473991394043 time for calcul the mask position with numpy : 0.034935712814331055 nb_pixel_total : 150012 time to create 1 rle with new method : 0.3753190040588379 time for calcul the mask position with numpy : 0.02951979637145996 nb_pixel_total : 208999 time to create 1 rle with new method : 0.5515899658203125 time for calcul the mask position with numpy : 0.03149080276489258 nb_pixel_total : 12331 time to create 1 rle with old method : 0.013767480850219727 time for calcul the mask position with numpy : 0.028655529022216797 nb_pixel_total : 182487 time to create 1 rle with new method : 0.6014513969421387 time for calcul the mask position with numpy : 0.029663562774658203 nb_pixel_total : 48088 time to create 1 rle with old method : 0.05629992485046387 time for calcul the mask position with numpy : 0.029366731643676758 nb_pixel_total : 232151 time to create 1 rle with new method : 0.3570375442504883 time for calcul the mask position with numpy : 0.032503604888916016 nb_pixel_total : 136314 time to create 1 rle with old method : 0.16512322425842285 time for calcul the mask position with numpy : 0.028824329376220703 nb_pixel_total : 49117 time to create 1 rle with old method : 0.05384063720703125 time for calcul the mask position with numpy : 0.029996871948242188 nb_pixel_total : 31529 time to create 1 rle with old method : 0.05054306983947754 time for calcul the mask position with numpy : 0.03193020820617676 nb_pixel_total : 40967 time to create 1 rle with old method : 0.056852102279663086 time for calcul the mask position with numpy : 0.03540611267089844 nb_pixel_total : 349157 time to create 1 rle with new method : 0.333787202835083 time for calcul the mask position with numpy : 0.027968168258666992 nb_pixel_total : 38434 time to create 1 rle with old method : 0.042382001876831055 time for calcul the mask position with numpy : 0.028844833374023438 nb_pixel_total : 3172 time to create 1 rle with old method : 0.0037643909454345703 time for calcul the mask position with numpy : 0.02824258804321289 nb_pixel_total : 3173 time to create 1 rle with old method : 0.0036437511444091797 time for calcul the mask position with numpy : 0.02837395668029785 nb_pixel_total : 66251 time to create 1 rle with old method : 0.07377505302429199 time for calcul the mask position with numpy : 0.02848982810974121 nb_pixel_total : 5844 time to create 1 rle with old method : 0.006613016128540039 time for calcul the mask position with numpy : 0.029117345809936523 nb_pixel_total : 120262 time to create 1 rle with old method : 0.13672351837158203 create new chi : 5.529462099075317 time to delete rle : 0.002953767776489258 batch 1 Loaded 57 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18338 TO DO : save crop sub photo not yet done ! save time : 1.6807231903076172 map_output_result : {1350815765: (0.0, 'Should be the crop_list due to order', 0), 1350815760: (0.0, 'Should be the crop_list due to order', 0), 1350815755: (0.0, 'Should be the crop_list due to order', 0), 1350815746: (0.0, 'Should be the crop_list due to order', 0), 1350815654: (0.0, 'Should be the crop_list due to order', 0), 1350815508: (0.0, 'Should be the crop_list due to order', 0), 1350815501: (0.0, 'Should be the crop_list due to order', 0), 1350815493: (0.0, 'Should be the crop_list due to order', 0), 1350815491: (0.0, 'Should be the crop_list due to order', 0), 1350815489: (0.0, 'Should be the crop_list due to order', 0), 1350815420: (0.0, 'Should be the crop_list due to order', 0), 1350815414: (0.0, 'Should be the crop_list due to order', 0), 1350815411: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1350815765, 1350815760, 1350815755, 1350815746, 1350815654, 1350815508, 1350815501, 1350815493, 1350815491, 1350815489, 1350815420, 1350815414, 1350815411] Looping around the photos to save general results len do output : 13 /1350815765.Didn't retrieve data . /1350815760.Didn't retrieve data . /1350815755.Didn't retrieve data . /1350815746.Didn't retrieve data . /1350815654.Didn't retrieve data . /1350815508.Didn't retrieve data . /1350815501.Didn't retrieve data . /1350815493.Didn't retrieve data . /1350815491.Didn't retrieve data . /1350815489.Didn't retrieve data . /1350815420.Didn't retrieve data . /1350815414.Didn't retrieve data . /1350815411.Didn't retrieve data . before output type Used above Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815765', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815760', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815755', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815746', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815654', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815508', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815501', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815493', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815491', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815489', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815420', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815414', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815411', None, None, None, None, None, '2735840') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 39 time used for this insertion : 0.017367124557495117 save_final save missing photos in datou_result : time spend for datou_step_exec : 172.513325214386 time spend to save output : 0.017851591110229492 total time spend for step 3 : 172.53117680549622 step4:ventilate_hashtags_in_portfolio Wed Apr 9 19:39:37 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 ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 22175831 get user id for portfolio 22175831 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22175831 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','background','pet_fonce','metal','autre','mal_croppe','carton','environnement','pehd','pet_clair','flou')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22175831 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','background','pet_fonce','metal','autre','mal_croppe','carton','environnement','pehd','pet_clair','flou')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22175831 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','background','pet_fonce','metal','autre','mal_croppe','carton','environnement','pehd','pet_clair','flou')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22176739,22176740,22176741,22176742,22176743,22176744,22176745,22176746,22176747,22176748,22176749?tags=papier,background,pet_fonce,metal,autre,mal_croppe,carton,environnement,pehd,pet_clair,flou Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350815765, 1350815760, 1350815755, 1350815746, 1350815654, 1350815508, 1350815501, 1350815493, 1350815491, 1350815489, 1350815420, 1350815414, 1350815411] Looping around the photos to save general results len do output : 1 /22175831. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815765', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815760', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815755', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815746', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815654', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815508', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815501', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815493', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815491', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815489', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815420', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815414', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815411', None, None, None, None, None, '2735840') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 14 time used for this insertion : 0.017592668533325195 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.9692461490631104 time spend to save output : 0.01786184310913086 total time spend for step 4 : 1.9871079921722412 step5:final Wed Apr 9 19:39:39 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 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 ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1350815765: ('0.18248340045598005',), 1350815760: ('0.18248340045598005',), 1350815755: ('0.18248340045598005',), 1350815746: ('0.18248340045598005',), 1350815654: ('0.18248340045598005',), 1350815508: ('0.18248340045598005',), 1350815501: ('0.18248340045598005',), 1350815493: ('0.18248340045598005',), 1350815491: ('0.18248340045598005',), 1350815489: ('0.18248340045598005',), 1350815420: ('0.18248340045598005',), 1350815414: ('0.18248340045598005',), 1350815411: ('0.18248340045598005',)} new output for save of step final : {1350815765: ('0.18248340045598005',), 1350815760: ('0.18248340045598005',), 1350815755: ('0.18248340045598005',), 1350815746: ('0.18248340045598005',), 1350815654: ('0.18248340045598005',), 1350815508: ('0.18248340045598005',), 1350815501: ('0.18248340045598005',), 1350815493: ('0.18248340045598005',), 1350815491: ('0.18248340045598005',), 1350815489: ('0.18248340045598005',), 1350815420: ('0.18248340045598005',), 1350815414: ('0.18248340045598005',), 1350815411: ('0.18248340045598005',)} [1350815765, 1350815760, 1350815755, 1350815746, 1350815654, 1350815508, 1350815501, 1350815493, 1350815491, 1350815489, 1350815420, 1350815414, 1350815411] Looping around the photos to save general results len do output : 13 /1350815765.Didn't retrieve data . /1350815760.Didn't retrieve data . /1350815755.Didn't retrieve data . /1350815746.Didn't retrieve data . /1350815654.Didn't retrieve data . /1350815508.Didn't retrieve data . /1350815501.Didn't retrieve data . /1350815493.Didn't retrieve data . /1350815491.Didn't retrieve data . /1350815489.Didn't retrieve data . /1350815420.Didn't retrieve data . /1350815414.Didn't retrieve data . /1350815411.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815765', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815760', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815755', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815746', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815654', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815508', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815501', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815493', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815491', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815489', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815420', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815414', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815411', None, None, None, None, None, '2735840') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 39 time used for this insertion : 0.015399456024169922 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.12723398208618164 time spend to save output : 0.01601123809814453 total time spend for step 5 : 0.14324522018432617 step6:blur_detection Wed Apr 9 19:39:39 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 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 inside step blur_detection methode: ratio et variance treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89.jpg resize: (2160, 3264) 1350815765 -4.443839766506872 treat image : temp/1744219829_2624324_1350815760_efb7739269c6583d0516d692445f8efe.jpg resize: (2160, 3264) 1350815760 -5.292409950903133 treat image : temp/1744219829_2624324_1350815755_82385424b275c03aa4fd5b613de295f6.jpg resize: (2160, 3264) 1350815755 -2.0321995396284973 treat image : temp/1744219829_2624324_1350815746_8e9b216fcdea138054dc040529e269d7.jpg resize: (2160, 3264) 1350815746 -2.554594735083435 treat image : temp/1744219829_2624324_1350815654_92008edbff6b4f2a43fb6230ce414c52.jpg resize: (2160, 3264) 1350815654 0.19839653507397276 treat image : temp/1744219829_2624324_1350815508_97c07d88ef125823f81220f6f03f662f.jpg resize: (2160, 3264) 1350815508 -2.0815219559713056 treat image : temp/1744219829_2624324_1350815501_d39a2da4cceb297d3d94d50581cdea2d.jpg resize: (2160, 3264) 1350815501 -4.2778812870839475 treat image : temp/1744219829_2624324_1350815493_ab9c4487d5aa21bd65eed2cf958450d6.jpg resize: (2160, 3264) 1350815493 -4.74723305257341 treat image : temp/1744219829_2624324_1350815491_adf0b18f5c5fdd962240fe2295f11ad6.jpg resize: (2160, 3264) 1350815491 -2.263603372171772 treat image : temp/1744219829_2624324_1350815489_524f8b59298de83bd1b918550fdc4195.jpg resize: (2160, 3264) 1350815489 -3.0686136811460494 treat image : temp/1744219829_2624324_1350815420_5b3927beb4b362b6986aeb8349ac75f6.jpg resize: (2160, 3264) 1350815420 -3.792082838517895 treat image : temp/1744219829_2624324_1350815414_8a5409f06c705c1f7f57868e93888439.jpg resize: (2160, 3264) 1350815414 -1.350112972201128 treat image : temp/1744219829_2624324_1350815411_73e048a15944c9a6c81c41a225bf3702.jpg resize: (2160, 3264) 1350815411 -3.474646811467 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921238_0.png resize: (421, 144) 1350842839 -0.8804371501741535 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921245_0.png resize: (338, 299) 1350842842 -2.1318296554468503 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921251_0.png resize: (190, 185) 1350842844 -1.987787131391278 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921241_0.png resize: (165, 131) 1350842846 -1.7914996397664185 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921278_0.png resize: (237, 255) 1350842847 -2.9561057505404107 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921249_0.png resize: (79, 94) 1350842849 1.1566792668071841 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921271_0.png resize: (141, 94) 1350842851 -1.8737138023132596 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921240_0.png resize: (214, 171) 1350842852 -1.540571641663037 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921279_0.png resize: (244, 279) 1350842854 -3.0805584644220656 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921274_0.png resize: (103, 91) 1350842856 -1.5337609030174963 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921267_0.png resize: (132, 218) 1350842857 -2.7931812422629103 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921246_0.png resize: (93, 405) 1350842859 -3.893195715680077 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921239_0.png resize: (179, 245) 1350842861 -3.148445287189165 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921263_0.png resize: (332, 230) 1350842862 -3.4362893413829454 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921265_0.png resize: (128, 143) 1350842865 -2.2109690668932935 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921261_0.png resize: (359, 475) 1350842866 -2.000535965676392 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921264_0.png resize: (283, 267) 1350842868 -2.567477131285576 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921242_0.png resize: (182, 134) 1350842870 -2.551790872398514 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921253_0.png resize: (182, 153) 1350842871 -3.864531037519362 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921277_0.png resize: (208, 166) 1350842874 -2.0416889958132245 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921258_0.png resize: (109, 199) 1350842878 -2.39726345248797 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921255_0.png resize: (238, 204) 1350842880 -2.5020635786428493 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921275_0.png resize: (148, 180) 1350842882 -4.212591532800394 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921273_0.png resize: (130, 146) 1350842884 -1.1259033130999907 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921250_0.png resize: (239, 513) 1350842885 -3.0858078890555016 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921237_0.png resize: (121, 101) 1350842887 -1.9495713097925387 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921257_0.png resize: (113, 72) 1350842889 -3.2484673652144815 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921256_0.png resize: (131, 98) 1350842890 -2.4191047566552766 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921252_0.png resize: (117, 97) 1350842891 3.7533961983510724 treat image : temp/1744219829_2624324_1350815765_cbc854e000b2eb1c29f4392bf0f81b89_rle_crop_3751921254_0.png resize: (96, 103) 1350842896 -1.1221984087869237 treat image : 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temp/1744219829_2624324_1350815411_73e048a15944c9a6c81c41a225bf3702_rle_crop_3751921560_0.png resize: (282, 177) 1350843737 -2.998296849798678 treat image : temp/1744219829_2624324_1350815411_73e048a15944c9a6c81c41a225bf3702_rle_crop_3751921564_0.png resize: (67, 126) 1350843741 1.4526705461164544 treat image : temp/1744219829_2624324_1350815760_efb7739269c6583d0516d692445f8efe_rle_crop_3751921305_0.png resize: (199, 182) 1350843751 -3.306841657973983 Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 346 time used for this insertion : 0.03621649742126465 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 346 time used for this insertion : 3.4923107624053955 save missing photos in datou_result : time spend for datou_step_exec : 56.61038851737976 time spend to save output : 3.5370452404022217 total time spend for step 6 : 60.14743375778198 step7:brightness Wed Apr 9 19:40:40 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 ! 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temp/1744219829_2624324_1350815411_73e048a15944c9a6c81c41a225bf3702_rle_crop_3751921564_0.png treat image : temp/1744219829_2624324_1350815760_efb7739269c6583d0516d692445f8efe_rle_crop_3751921305_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 346 time used for this insertion : 0.029107093811035156 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 346 time used for this insertion : 0.06814980506896973 save missing photos in datou_result : time spend for datou_step_exec : 12.876168966293335 time spend to save output : 0.10442662239074707 total time spend for step 7 : 12.980595588684082 step8:velours_tree Wed Apr 9 19:40:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.10164070129394531 time spend to save output : 7.414817810058594e-05 total time spend for step 8 : 0.1017148494720459 step9:send_mail_cod Wed Apr 9 19:40:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 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 dans la step send mail cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P22175831_09-04-2025_19_40_53.pdf 22176739 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221767391744220453 22176740 imagette221767401744220454 22176741 change filename to text .imagette221767411744220454 22176742 change filename to text .change filename to text .imagette221767421744220454 22176743 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221767431744220454 22176744 imagette221767441744220455 22176745 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221767451744220455 22176747 imagette221767471744220456 22176748 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221767481744220456 22176749 imagette221767491744220458 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22175831 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22176739,22176740,22176741,22176742,22176743,22176744,22176745,22176746,22176747,22176748,22176749?tags=papier,background,pet_fonce,metal,autre,mal_croppe,carton,environnement,pehd,pet_clair,flou args[1350815765] : ((1350815765, -4.443839766506872, 492609224), (1350815765, -0.014209298152823577, 2107752395), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815760] : ((1350815760, -5.292409950903133, 492609224), (1350815760, -0.17235166499589488, 496442774), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815755] : ((1350815755, -2.0321995396284973, 492609224), (1350815755, 0.2701018356868452, 2107752395), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815746] : ((1350815746, -2.554594735083435, 492609224), (1350815746, -0.09920346591162471, 496442774), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815654] : ((1350815654, 0.19839653507397276, 492688767), (1350815654, 0.18760468535078892, 2107752395), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815508] : ((1350815508, -2.0815219559713056, 492609224), (1350815508, -0.16634941517994786, 496442774), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815501] : ((1350815501, -4.2778812870839475, 492609224), (1350815501, 0.08512493246926964, 2107752395), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815493] : ((1350815493, -4.74723305257341, 492609224), (1350815493, -0.2968483564270006, 496442774), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815491] : ((1350815491, -2.263603372171772, 492609224), (1350815491, -0.03640537805256306, 2107752395), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815489] : ((1350815489, -3.0686136811460494, 492609224), (1350815489, -0.04852829424502102, 2107752395), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815420] : ((1350815420, -3.792082838517895, 492609224), (1350815420, 0.07560078569204767, 2107752395), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815414] : ((1350815414, -1.350112972201128, 492688767), (1350815414, 0.34642002216684425, 2107752395), '0.18248340045598005') We are sending mail with results at report@fotonower.com args[1350815411] : ((1350815411, -3.474646811467, 492609224), (1350815411, -0.1486359219911226, 496442774), '0.18248340045598005') We are sending mail with results at report@fotonower.com refus_total : 0.18248340045598005 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=22175831 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350815420,1350815411,1350815493,1350815746,1350815765,1350815414,1350815489,1350815491,1350815501,1350815508,1350815654,1350815755,1350815760) Found this number of photos: 13 begin to download photo : 1350815420 begin to download photo : 1350815746 begin to download photo : 1350815489 begin to download photo : 1350815508 begin to download photo : 1350815760 download finish for photo 1350815508 begin to download photo : 1350815654 download finish for photo 1350815746 begin to download photo : 1350815765 download finish for photo 1350815489 begin to download photo : 1350815491 download finish for photo 1350815760 download finish for photo 1350815420 begin to download photo : 1350815411 download finish for photo 1350815491 begin to download photo : 1350815501 download finish for photo 1350815765 begin to download photo : 1350815414 download finish for photo 1350815411 begin to download photo : 1350815493 download finish for photo 1350815654 begin to download photo : 1350815755 download finish for photo 1350815501 download finish for photo 1350815493 download finish for photo 1350815414 download finish for photo 1350815755 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22175831_09-04-2025_19_40_53.pdf results_Auto_P22175831_09-04-2025_19_40_53.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22175831_09-04-2025_19_40_53.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3318','22175831','results_Auto_P22175831_09-04-2025_19_40_53.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22175831_09-04-2025_19_40_53.pdf','pdf','','0.81','0.18248340045598005') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22175831

https://www.fotonower.com/image?json=false&list_photos_id=1350815765
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815760
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815755
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815746
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815654
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815508
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815501
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815493
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815491
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815489
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815420
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815414
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350815411
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 18.25%
Veuillez trouver les photos des contaminants.

exemples de contaminants: papier: https://www.fotonower.com/view/22176739?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22176741?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22176742?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22176743?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22176745?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22176748?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22175831_09-04-2025_19_40_53.pdf.

Lien vers velours :https://www.fotonower.com/velours/22176739,22176740,22176741,22176742,22176743,22176744,22176745,22176746,22176747,22176748,22176749?tags=papier,background,pet_fonce,metal,autre,mal_croppe,carton,environnement,pehd,pet_clair,flou.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 17:41:02 GMT Content-Length: 0 Connection: close X-Message-Id: _h09uRp-RNu1PGXQR4HJag Access-Control-Allow-Origin: https://sendgrid.api-docs.io Access-Control-Allow-Methods: POST Access-Control-Allow-Headers: Authorization, Content-Type, On-behalf-of, x-sg-elas-acl Access-Control-Max-Age: 600 X-No-CORS-Reason: https://sendgrid.com/docs/Classroom/Basics/API/cors.html Strict-Transport-Security: max-age=31536000; includeSubDomains Content-Security-Policy: frame-ancestors 'none' Cache-Control: no-cache X-Content-Type-Options: no-sniff Referrer-Policy: strict-origin-when-cross-origin Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1350815765, 1350815760, 1350815755, 1350815746, 1350815654, 1350815508, 1350815501, 1350815493, 1350815491, 1350815489, 1350815420, 1350815414, 1350815411] Looping around the photos to save general results len do output : 0 before output type Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815765', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815760', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815755', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815746', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815654', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815508', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815501', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815493', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815491', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815489', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815420', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815414', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815411', None, None, None, None, None, '2735840') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.015744686126708984 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.020152807235718 time spend to save output : 0.01606130599975586 total time spend for step 9 : 9.036214113235474 step10:split_time_score Wed Apr 9 19:41:02 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 ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('17', 13),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 09042025 22175831 Nombre de photos uploadées : 13 / 23040 (0%) 09042025 22175831 Nombre de photos taguées (types de déchets): 0 / 13 (0%) 09042025 22175831 Nombre de photos taguées (volume) : 0 / 13 (0%) elapsed_time : load_data_split_time_score 1.6689300537109375e-06 elapsed_time : order_list_meta_photo_and_scores 3.5762786865234375e-06 ????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0005495548248291016 elapsed_time : insert_dashboard_record_day_entry 0.026279211044311523 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.24394142078851233 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161064_09-04-2025_12_55_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161064 order by id desc limit 1 # 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161064 AND mptpi.`type`=3594 To do Qualite : 0.04609045052441947 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161067_09-04-2025_12_22_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161067 order by id desc limit 1 # 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 ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161067 AND mptpi.`type`=3726 To do Qualite : 0.1886047378056162 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161073_09-04-2025_12_52_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161073 order by id desc limit 1 # 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161073 AND mptpi.`type`=3594 To do Qualite : 0.22379874656749282 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161075_09-04-2025_12_27_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161075 order by id desc limit 1 # 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161075 AND mptpi.`type`=3594 To do Qualite : 0.0944780203179495 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161654_09-04-2025_13_05_02.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161654 order by id desc limit 1 # 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 ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161654 AND mptpi.`type`=3726 To do Qualite : 0.23583165253948815 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163334_09-04-2025_14_48_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22163334 order by id desc limit 1 # 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22163334 AND mptpi.`type`=3594 To do Qualite : 0.16103178757035222 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163336_09-04-2025_14_36_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22163336 order by id desc limit 1 # 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22163336 AND mptpi.`type`=3594 To do Qualite : 0.2097409504444585 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22172049_09-04-2025_18_16_26.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22172049 order by id desc limit 1 # 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22172049 AND mptpi.`type`=3594 To do Qualite : 0.18248340045598005 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22175831_09-04-2025_19_40_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22175831 order by id desc limit 1 # 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 ! 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 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22175831 AND mptpi.`type`=3594 To do Qualite : 0.0746421277879351 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22175833_09-04-2025_19_38_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22175833 order by id desc limit 1 # 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 ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22175833 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'09042025': {'nb_upload': 13, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1350815765, 1350815760, 1350815755, 1350815746, 1350815654, 1350815508, 1350815501, 1350815493, 1350815491, 1350815489, 1350815420, 1350815414, 1350815411] Looping around the photos to save general results len do output : 1 /22175831Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815765', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815760', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815755', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815746', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815654', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815508', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815501', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815493', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815491', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815489', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815420', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815414', None, None, None, None, None, '2735840') ('3318', None, None, None, None, None, None, None, '2735840') ('3318', '22175831', '1350815411', None, None, None, None, None, '2735840') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 14 time used for this insertion : 0.017920732498168945 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.9228007793426514 time spend to save output : 0.018161535263061523 total time spend for step 10 : 0.9409623146057129 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 13 set_done_treatment 283.73user 175.72system 10:37.40elapsed 72%CPU (0avgtext+0avgdata 7738260maxresident)k 748568inputs+194568outputs (12084major+24471593minor)pagefaults 0swaps