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 : 945173 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 : ['2734297'] with mtr_portfolio_ids : ['22161075'] and first list_photo_ids : [] new path : /proc/945173/ 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 , BFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads 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 12:20:43.974347: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 12:20:44.178238: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 12:20:45.528931: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.529005: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.535415: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.535444: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.585918: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.585989: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.626748: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.626786: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.671600: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.671641: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-09 12:20:45.673701: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to alocal 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 : 8 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 : 46 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 : 42 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 : 64 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 : 48 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 : 58 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: 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 945534 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 30 tf kernel not reseted sub process len(results) : 8 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 8 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 : 2956 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 : 0.0017075538635253906 nb_pixel_total : 25295 time to create 1 rle with old method : 0.02985858917236328 length of segment : 169 time for calcul the mask position with numpy : 0.01185464859008789 nb_pixel_total : 268020 time to create 1 rle with new method : 0.01592397689819336 length of segment : 797 time for calcul the mask position with numpy : 0.0024785995483398438 nb_pixel_total : 84436 time to create 1 rle with old method : 0.09598922729492188 length of segment : 369 time for calcul the mask position with numpy : 0.003745555877685547 nb_pixel_total : 138899 time to create 1 rle with old method : 0.15519475936889648 length of segment : 718 time for calcul the mask position with numpy : 0.0005202293395996094 nb_pixel_total : 20742 time to create 1 rle with old method : 0.02449512481689453 length of segment : 269 time for calcul the mask position with numpy : 0.0011022090911865234 nb_pixel_total : 16440 time to create 1 rle with old method : 0.019685745239257812 length of segment : 140 time for calcul the mask position with numpy : 0.0012674331665039062 nb_pixel_total : 35430 time to create 1 rle with old method : 0.040543556213378906 length of segment : 277 time for calcul the mask position with numpy : 0.0012204647064208984 nb_pixel_total : 44349 time to create 1 rle with old method : 0.05080151557922363 length of segment : 273 time for calcul the mask position with numpy : 0.0004527568817138672 nb_pixel_total : 11395 time to create 1 rle with old method : 0.013252973556518555 length of segment : 122 time for calcul the mask position with numpy : 0.0052585601806640625 nb_pixel_total : 40490 time to create 1 rle with old method : 0.046158790588378906 length of segment : 206 time for calcul the mask position with numpy : 0.005430459976196289 nb_pixel_total : 219200 time to create 1 rle with new method : 0.014958381652832031 length of segment : 524 time for calcul the mask position with numpy : 0.0007028579711914062 nb_pixel_total : 25482 time to create 1 rle with old method : 0.034453630447387695 length of segment : 170 time for calcul the mask position with numpy : 0.0005927085876464844 nb_pixel_total : 25511 time to create 1 rle with old method : 0.028740406036376953 length of segment : 243 time for calcul the mask position with numpy : 0.0005564689636230469 nb_pixel_total : 14996 time to create 1 rle with old method : 0.016756772994995117 length of segment : 194 time for calcul the mask position with numpy : 0.0021369457244873047 nb_pixel_total : 84869 time to create 1 rle with old method : 0.10232686996459961 length of segment : 448 time for calcul the mask position with numpy : 0.0017552375793457031 nb_pixel_total : 68055 time to create 1 rle with old method : 0.08664274215698242 length of segment : 285 time for calcul the mask position with numpy : 0.002975940704345703 nb_pixel_total : 120577 time to create 1 rle with old method : 0.16395020484924316 length of segment : 420 time for calcul the mask position with numpy : 0.003916025161743164 nb_pixel_total : 111846 time to create 1 rle with old method : 0.12662053108215332 length of segment : 524 time for calcul the mask position with numpy : 0.00257110595703125 nb_pixel_total : 57385 time to create 1 rle with old method : 0.07109379768371582 length of segment : 435 time for calcul the mask position with numpy : 0.0017316341400146484 nb_pixel_total : 35868 time to create 1 rle with old method : 0.040685415267944336 length of segment : 200 time for calcul the mask position with numpy : 0.0029535293579101562 nb_pixel_total : 45221 time to create 1 rle with old method : 0.05110311508178711 length of segment : 212 time for calcul the mask position with numpy : 0.003329038619995117 nb_pixel_total : 83715 time to create 1 rle with old method : 0.09565401077270508 length of segment : 363 time for calcul the mask position with numpy : 0.009240865707397461 nb_pixel_total : 264742 time to create 1 rle with new method : 0.013539791107177734 length of segment : 781 time for calcul the mask position with numpy : 0.0020389556884765625 nb_pixel_total : 47242 time to create 1 rle with old method : 0.05270886421203613 length of segment : 321 time for calcul the mask position with numpy : 0.0025734901428222656 nb_pixel_total : 38249 time to create 1 rle with old method : 0.04245352745056152 length of segment : 450 time for calcul the mask position with numpy : 0.005131959915161133 nb_pixel_total : 145917 time to create 1 rle with old method : 0.16811919212341309 length of segment : 461 time for calcul the mask position with numpy : 0.012573003768920898 nb_pixel_total : 165757 time to create 1 rle with new method : 0.03272843360900879 length of segment : 988 time for calcul the mask position with numpy : 0.0012443065643310547 nb_pixel_total : 25028 time to create 1 rle with old method : 0.02979588508605957 length of segment : 159 time for calcul the mask position with numpy : 0.00025963783264160156 nb_pixel_total : 7748 time to create 1 rle with old method : 0.009148120880126953 length of segment : 130 time for calcul the mask position with numpy : 0.006627559661865234 nb_pixel_total : 210723 time to create 1 rle with new method : 0.011833667755126953 length of segment : 498 time for calcul the mask position with numpy : 0.0008797645568847656 nb_pixel_total : 18820 time to create 1 rle with old method : 0.025537729263305664 length of segment : 256 time for calcul the mask position with numpy : 0.0051500797271728516 nb_pixel_total : 127631 time to create 1 rle with old method : 0.13370227813720703 length of segment : 805 time for calcul the mask position with numpy : 0.002037525177001953 nb_pixel_total : 61017 time to create 1 rle with old method : 0.07194924354553223 length of segment : 437 time for calcul the mask position with numpy : 0.0006265640258789062 nb_pixel_total : 14776 time to create 1 rle with old method : 0.0166170597076416 length of segment : 196 time for calcul the mask position with numpy : 0.0025832653045654297 nb_pixel_total : 75784 time to create 1 rle with old method : 0.08320999145507812 length of segment : 349 time for calcul the mask position with numpy : 0.008394241333007812 nb_pixel_total : 221907 time to create 1 rle with new method : 0.01646709442138672 length of segment : 543 time for calcul the mask position with numpy : 0.0006034374237060547 nb_pixel_total : 9624 time to create 1 rle with old method : 0.0111236572265625 length of segment : 120 time for calcul the mask position with numpy : 0.0014905929565429688 nb_pixel_total : 44723 time to create 1 rle with old method : 0.050110578536987305 length of segment : 292 time for calcul the mask position with numpy : 0.0010344982147216797 nb_pixel_total : 29324 time to create 1 rle with old method : 0.03224492073059082 length of segment : 185 time for calcul the mask position with numpy : 0.0010714530944824219 nb_pixel_total : 42306 time to create 1 rle with old method : 0.046270132064819336 length of segment : 226 time for calcul the mask position with numpy : 0.0005002021789550781 nb_pixel_total : 11002 time to create 1 rle with old method : 0.01238703727722168 length of segment : 184 time for calcul the mask position with numpy : 0.002002239227294922 nb_pixel_total : 55516 time to create 1 rle with old method : 0.06646156311035156 length of segment : 393 time for calcul the mask position with numpy : 0.0048253536224365234 nb_pixel_total : 103244 time to create 1 rle with old method : 0.11456418037414551 length of segment : 615 time for calcul the mask position with numpy : 0.010367155075073242 nb_pixel_total : 308849 time to create 1 rle with new method : 0.030231952667236328 length of segment : 1223 time for calcul the mask position with numpy : 0.000986337661743164 nb_pixel_total : 15190 time to create 1 rle with old method : 0.0252535343170166 length of segment : 135 time for calcul the mask position with numpy : 0.0010228157043457031 nb_pixel_total : 18434 time to create 1 rle with old method : 0.02124929428100586 length of segment : 156 time for calcul the mask position with numpy : 0.004086494445800781 nb_pixel_total : 122162 time to create 1 rle with old method : 0.13721156120300293 length of segment : 367 time for calcul the mask position with numpy : 0.00029087066650390625 nb_pixel_total : 6092 time to create 1 rle with old method : 0.0071718692779541016 length of segment : 76 time for calcul the mask position with numpy : 0.0011239051818847656 nb_pixel_total : 26583 time to create 1 rle with old method : 0.031073570251464844 length of segment : 299 time for calcul the mask position with numpy : 0.0010135173797607422 nb_pixel_total : 19601 time to create 1 rle with old method : 0.022468090057373047 length of segment : 276 time for calcul the mask position with numpy : 0.003522157669067383 nb_pixel_total : 93398 time to create 1 rle with old method : 0.10369682312011719 length of segment : 348 time for calcul the mask position with numpy : 0.0020966529846191406 nb_pixel_total : 35405 time to create 1 rle with old method : 0.03877115249633789 length of segment : 507 time for calcul the mask position with numpy : 0.0004794597625732422 nb_pixel_total : 15188 time to create 1 rle with old method : 0.01657700538635254 length of segment : 166 time for calcul the mask position with numpy : 0.0007815361022949219 nb_pixel_total : 16606 time to create 1 rle with old method : 0.019070863723754883 length of segment : 161 time for calcul the mask position with numpy : 0.0011456012725830078 nb_pixel_total : 24515 time to create 1 rle with old method : 0.02737259864807129 length of segment : 290 time for calcul the mask position with numpy : 0.0019021034240722656 nb_pixel_total : 54014 time to create 1 rle with old method : 0.061908721923828125 length of segment : 368 time for calcul the mask position with numpy : 0.0003349781036376953 nb_pixel_total : 8054 time to create 1 rle with old method : 0.008915185928344727 length of segment : 131 time for calcul the mask position with numpy : 0.001878499984741211 nb_pixel_total : 13008 time to create 1 rle with old method : 0.01549839973449707 length of segment : 182 time for calcul the mask position with numpy : 0.0023043155670166016 nb_pixel_total : 45373 time to create 1 rle with old method : 0.05173468589782715 length of segment : 356 time for calcul the mask position with numpy : 0.0004024505615234375 nb_pixel_total : 12004 time to create 1 rle with old method : 0.014502286911010742 length of segment : 109 time for calcul the mask position with numpy : 0.0007414817810058594 nb_pixel_total : 17583 time to create 1 rle with old method : 0.01982855796813965 length of segment : 179 time for calcul the mask position with numpy : 0.00121307373046875 nb_pixel_total : 24786 time to create 1 rle with old method : 0.028183937072753906 length of segment : 177 time for calcul the mask position with numpy : 0.0006573200225830078 nb_pixel_total : 14254 time to create 1 rle with old method : 0.016042470932006836 length of segment : 160 time for calcul the mask position with numpy : 0.00043702125549316406 nb_pixel_total : 6400 time to create 1 rle with old method : 0.007716655731201172 length of segment : 124 time for calcul the mask position with numpy : 0.0001766681671142578 nb_pixel_total : 3046 time to create 1 rle with old method : 0.003572225570678711 length of segment : 61 time for calcul the mask position with numpy : 0.0011110305786132812 nb_pixel_total : 34221 time to create 1 rle with old method : 0.03848743438720703 length of segment : 150 time for calcul the mask position with numpy : 0.0052945613861083984 nb_pixel_total : 140546 time to create 1 rle with old method : 0.1526036262512207 length of segment : 648 time for calcul the mask position with numpy : 0.0008625984191894531 nb_pixel_total : 9935 time to create 1 rle with old method : 0.01624894142150879 length of segment : 142 time for calcul the mask position with numpy : 0.0005586147308349609 nb_pixel_total : 7376 time to create 1 rle with old method : 0.009346246719360352 length of segment : 75 time for calcul the mask position with numpy : 0.0007386207580566406 nb_pixel_total : 20334 time to create 1 rle with old method : 0.024347782135009766 length of segment : 161 time for calcul the mask position with numpy : 0.0012218952178955078 nb_pixel_total : 29518 time to create 1 rle with old method : 0.0323331356048584 length of segment : 324 time for calcul the mask position with numpy : 0.006989479064941406 nb_pixel_total : 145284 time to create 1 rle with old method : 0.16015982627868652 length of segment : 637 time for calcul the mask position with numpy : 0.0034487247467041016 nb_pixel_total : 97971 time to create 1 rle with old method : 0.10923624038696289 length of segment : 278 time for calcul the mask position with numpy : 0.0013628005981445312 nb_pixel_total : 23910 time to create 1 rle with old method : 0.02672290802001953 length of segment : 412 time for calcul the mask position with numpy : 0.0018262863159179688 nb_pixel_total : 30995 time to create 1 rle with old method : 0.0345611572265625 length of segment : 377 time for calcul the mask position with numpy : 0.003394603729248047 nb_pixel_total : 72401 time to create 1 rle with old method : 0.07908034324645996 length of segment : 335 time for calcul the mask position with numpy : 0.0015294551849365234 nb_pixel_total : 26865 time to create 1 rle with old method : 0.031024932861328125 length of segment : 248 time for calcul the mask position with numpy : 0.0028586387634277344 nb_pixel_total : 49618 time to create 1 rle with old method : 0.056180477142333984 length of segment : 342 time for calcul the mask position with numpy : 0.0015497207641601562 nb_pixel_total : 27530 time to create 1 rle with old method : 0.03107166290283203 length of segment : 235 time for calcul the mask position with numpy : 0.0002574920654296875 nb_pixel_total : 3388 time to create 1 rle with old method : 0.003990650177001953 length of segment : 79 time for calcul the mask position with numpy : 0.001112222671508789 nb_pixel_total : 26199 time to create 1 rle with old method : 0.029987335205078125 length of segment : 161 time for calcul the mask position with numpy : 0.0016715526580810547 nb_pixel_total : 34107 time to create 1 rle with old method : 0.0392603874206543 length of segment : 180 time for calcul the mask position with numpy : 0.003955364227294922 nb_pixel_total : 67171 time to create 1 rle with old method : 0.07512187957763672 length of segment : 466 time for calcul the mask position with numpy : 0.0011920928955078125 nb_pixel_total : 19964 time to create 1 rle with old method : 0.02317023277282715 length of segment : 206 time for calcul the mask position with numpy : 0.0005915164947509766 nb_pixel_total : 7998 time to create 1 rle with old method : 0.009158849716186523 length of segment : 132 time for calcul the mask position with numpy : 0.00039005279541015625 nb_pixel_total : 4972 time to create 1 rle with old method : 0.005956888198852539 length of segment : 77 time for calcul the mask position with numpy : 0.0005679130554199219 nb_pixel_total : 13958 time to create 1 rle with old method : 0.016007184982299805 length of segment : 144 time for calcul the mask position with numpy : 0.0006105899810791016 nb_pixel_total : 10520 time to create 1 rle with old method : 0.011974811553955078 length of segment : 132 time for calcul the mask position with numpy : 0.0005106925964355469 nb_pixel_total : 9663 time to create 1 rle with old method : 0.011003732681274414 length of segment : 130 time for calcul the mask position with numpy : 0.00025844573974609375 nb_pixel_total : 3128 time to create 1 rle with old method : 0.003576040267944336 length of segment : 81 time for calcul the mask position with numpy : 0.0014040470123291016 nb_pixel_total : 24403 time to create 1 rle with old method : 0.027013778686523438 length of segment : 296 time for calcul the mask position with numpy : 0.0017910003662109375 nb_pixel_total : 34410 time to create 1 rle with old method : 0.038446664810180664 length of segment : 313 time for calcul the mask position with numpy : 0.0013816356658935547 nb_pixel_total : 26106 time to create 1 rle with old method : 0.028295516967773438 length of segment : 285 time for calcul the mask position with numpy : 0.0007681846618652344 nb_pixel_total : 12164 time to create 1 rle with old method : 0.01371312141418457 length of segment : 103 time for calcul the mask position with numpy : 0.0011429786682128906 nb_pixel_total : 21267 time to create 1 rle with old method : 0.023946046829223633 length of segment : 191 time for calcul the mask position with numpy : 0.003137350082397461 nb_pixel_total : 77229 time to create 1 rle with old method : 0.0867307186126709 length of segment : 512 time for calcul the mask position with numpy : 0.003900766372680664 nb_pixel_total : 83788 time to create 1 rle with old method : 0.09504532814025879 length of segment : 255 time for calcul the mask position with numpy : 0.0009963512420654297 nb_pixel_total : 17073 time to create 1 rle with old method : 0.019571542739868164 length of segment : 105 time for calcul the mask position with numpy : 0.0008661746978759766 nb_pixel_total : 13664 time to create 1 rle with old method : 0.015558242797851562 length of segment : 231 time for calcul the mask position with numpy : 0.0014731884002685547 nb_pixel_total : 11504 time to create 1 rle with old method : 0.013343334197998047 length of segment : 263 time for calcul the mask position with numpy : 0.0006656646728515625 nb_pixel_total : 14293 time to create 1 rle with old method : 0.01587820053100586 length of segment : 260 time for calcul the mask position with numpy : 0.000293731689453125 nb_pixel_total : 4839 time to create 1 rle with old method : 0.005548238754272461 length of segment : 92 time for calcul the mask position with numpy : 0.0002033710479736328 nb_pixel_total : 4208 time to create 1 rle with old method : 0.0049779415130615234 length of segment : 150 time for calcul the mask position with numpy : 0.003217458724975586 nb_pixel_total : 86717 time to create 1 rle with old method : 0.09385442733764648 length of segment : 387 time for calcul the mask position with numpy : 0.03040623664855957 nb_pixel_total : 501866 time to create 1 rle with new method : 0.1575314998626709 length of segment : 1360 time for calcul the mask position with numpy : 0.0011234283447265625 nb_pixel_total : 24612 time to create 1 rle with old method : 0.027466297149658203 length of segment : 164 time for calcul the mask position with numpy : 0.008904695510864258 nb_pixel_total : 275818 time to create 1 rle with new method : 0.012517452239990234 length of segment : 788 time for calcul the mask position with numpy : 0.00653386116027832 nb_pixel_total : 210079 time to create 1 rle with new method : 0.014978170394897461 length of segment : 497 time for calcul the mask position with numpy : 0.002656221389770508 nb_pixel_total : 69555 time to create 1 rle with old method : 0.07762789726257324 length of segment : 540 time for calcul the mask position with numpy : 0.001024484634399414 nb_pixel_total : 22424 time to create 1 rle with old method : 0.025185823440551758 length of segment : 228 time for calcul the mask position with numpy : 0.0019237995147705078 nb_pixel_total : 57880 time to create 1 rle with old method : 0.06386733055114746 length of segment : 409 time for calcul the mask position with numpy : 0.0007443428039550781 nb_pixel_total : 13993 time to create 1 rle with old method : 0.01555180549621582 length of segment : 188 time for calcul the mask position with numpy : 0.0009446144104003906 nb_pixel_total : 20199 time to create 1 rle with old method : 0.022442340850830078 length of segment : 268 time for calcul the mask position with numpy : 0.0013723373413085938 nb_pixel_total : 35176 time to create 1 rle with old method : 0.03889894485473633 length of segment : 254 time for calcul the mask position with numpy : 0.009151697158813477 nb_pixel_total : 260698 time to create 1 rle with new method : 0.014401674270629883 length of segment : 612 time for calcul the mask position with numpy : 0.0023050308227539062 nb_pixel_total : 53622 time to create 1 rle with old method : 0.06045246124267578 length of segment : 400 time for calcul the mask position with numpy : 0.0016052722930908203 nb_pixel_total : 35106 time to create 1 rle with old method : 0.03925895690917969 length of segment : 193 time for calcul the mask position with numpy : 0.0011703968048095703 nb_pixel_total : 25096 time to create 1 rle with old method : 0.02835988998413086 length of segment : 176 time for calcul the mask position with numpy : 0.004116535186767578 nb_pixel_total : 36898 time to create 1 rle with old method : 0.04122209548950195 length of segment : 493 time for calcul the mask position with numpy : 0.0006301403045654297 nb_pixel_total : 12024 time to create 1 rle with old method : 0.013820886611938477 length of segment : 140 time for calcul the mask position with numpy : 0.004323482513427734 nb_pixel_total : 91922 time to create 1 rle with old method : 0.10184955596923828 length of segment : 607 time for calcul the mask position with numpy : 0.0002892017364501953 nb_pixel_total : 7461 time to create 1 rle with old method : 0.008461952209472656 length of segment : 140 time for calcul the mask position with numpy : 0.0016467571258544922 nb_pixel_total : 29310 time to create 1 rle with old method : 0.033872127532958984 length of segment : 189 time for calcul the mask position with numpy : 0.0013353824615478516 nb_pixel_total : 39200 time to create 1 rle with old method : 0.04419970512390137 length of segment : 215 time for calcul the mask position with numpy : 0.0004515647888183594 nb_pixel_total : 12372 time to create 1 rle with old method : 0.014886140823364258 length of segment : 131 time for calcul the mask position with numpy : 0.0005586147308349609 nb_pixel_total : 11136 time to create 1 rle with old method : 0.012833118438720703 length of segment : 176 time for calcul the mask position with numpy : 0.0014636516571044922 nb_pixel_total : 36555 time to create 1 rle with old method : 0.04184889793395996 length of segment : 251 time for calcul the mask position with numpy : 0.0011682510375976562 nb_pixel_total : 23568 time to create 1 rle with old method : 0.026942729949951172 length of segment : 253 time for calcul the mask position with numpy : 0.00020551681518554688 nb_pixel_total : 6038 time to create 1 rle with old method : 0.007474422454833984 length of segment : 137 time for calcul the mask position with numpy : 0.0018193721771240234 nb_pixel_total : 37054 time to create 1 rle with old method : 0.0415346622467041 length of segment : 263 time for calcul the mask position with numpy : 0.0025756359100341797 nb_pixel_total : 47192 time to create 1 rle with old method : 0.053162574768066406 length of segment : 319 time for calcul the mask position with numpy : 0.0018129348754882812 nb_pixel_total : 32001 time to create 1 rle with old method : 0.03605484962463379 length of segment : 119 time for calcul the mask position with numpy : 0.005155324935913086 nb_pixel_total : 107495 time to create 1 rle with old method : 0.1183013916015625 length of segment : 427 time for calcul the mask position with numpy : 0.01171255111694336 nb_pixel_total : 268807 time to create 1 rle with new method : 0.018355607986450195 length of segment : 593 time for calcul the mask position with numpy : 0.0003032684326171875 nb_pixel_total : 11743 time to create 1 rle with old method : 0.01337289810180664 length of segment : 116 time for calcul the mask position with numpy : 0.002221822738647461 nb_pixel_total : 40417 time to create 1 rle with old method : 0.0446932315826416 length of segment : 249 time for calcul the mask position with numpy : 0.0015888214111328125 nb_pixel_total : 30811 time to create 1 rle with old method : 0.036448001861572266 length of segment : 197 time for calcul the mask position with numpy : 0.002218961715698242 nb_pixel_total : 22733 time to create 1 rle with old method : 0.026867151260375977 length of segment : 255 time for calcul the mask position with numpy : 0.006563663482666016 nb_pixel_total : 169983 time to create 1 rle with new method : 0.009117364883422852 length of segment : 476 time for calcul the mask position with numpy : 0.002644062042236328 nb_pixel_total : 37754 time to create 1 rle with old method : 0.04380035400390625 length of segment : 276 time for calcul the mask position with numpy : 0.0031578540802001953 nb_pixel_total : 63004 time to create 1 rle with old method : 0.07153034210205078 length of segment : 465 time for calcul the mask position with numpy : 0.0011444091796875 nb_pixel_total : 10103 time to create 1 rle with old method : 0.011667728424072266 length of segment : 259 time for calcul the mask position with numpy : 0.0021820068359375 nb_pixel_total : 35850 time to create 1 rle with old method : 0.04028582572937012 length of segment : 440 time for calcul the mask position with numpy : 0.0018153190612792969 nb_pixel_total : 47754 time to create 1 rle with old method : 0.0554041862487793 length of segment : 213 time for calcul the mask position with numpy : 0.0014865398406982422 nb_pixel_total : 9864 time to create 1 rle with old method : 0.012433767318725586 length of segment : 138 time for calcul the mask position with numpy : 0.0011980533599853516 nb_pixel_total : 16149 time to create 1 rle with old method : 0.026377201080322266 length of segment : 216 time for calcul the mask position with numpy : 0.0030612945556640625 nb_pixel_total : 50498 time to create 1 rle with old method : 0.061722755432128906 length of segment : 345 time for calcul the mask position with numpy : 0.006304502487182617 nb_pixel_total : 70567 time to create 1 rle with old method : 0.07733845710754395 length of segment : 523 time for calcul the mask position with numpy : 0.006064891815185547 nb_pixel_total : 133860 time to create 1 rle with old method : 0.1478724479675293 length of segment : 519 time for calcul the mask position with numpy : 0.0005750656127929688 nb_pixel_total : 6239 time to create 1 rle with old method : 0.009889841079711914 length of segment : 99 time for calcul the mask position with numpy : 0.004281759262084961 nb_pixel_total : 85988 time to create 1 rle with old method : 0.11256790161132812 length of segment : 424 time for calcul the mask position with numpy : 0.0011036396026611328 nb_pixel_total : 32360 time to create 1 rle with old method : 0.03621721267700195 length of segment : 149 time for calcul the mask position with numpy : 0.0015413761138916016 nb_pixel_total : 23340 time to create 1 rle with old method : 0.026953458786010742 length of segment : 262 time for calcul the mask position with numpy : 0.0016601085662841797 nb_pixel_total : 25867 time to create 1 rle with old method : 0.02961874008178711 length of segment : 205 time for calcul the mask position with numpy : 0.024180889129638672 nb_pixel_total : 536459 time to create 1 rle with new method : 0.051245927810668945 length of segment : 829 time for calcul the mask position with numpy : 0.0027048587799072266 nb_pixel_total : 31716 time to create 1 rle with old method : 0.03678464889526367 length of segment : 266 time for calcul the mask position with numpy : 0.004415273666381836 nb_pixel_total : 107211 time to create 1 rle with old method : 0.11755132675170898 length of segment : 792 time for calcul the mask position with numpy : 0.004250526428222656 nb_pixel_total : 110646 time to create 1 rle with old method : 0.12115478515625 length of segment : 588 time for calcul the mask position with numpy : 0.0005342960357666016 nb_pixel_total : 15802 time to create 1 rle with old method : 0.01834845542907715 length of segment : 117 time for calcul the mask position with numpy : 0.004507780075073242 nb_pixel_total : 81972 time to create 1 rle with old method : 0.09154295921325684 length of segment : 490 time for calcul the mask position with numpy : 0.012858152389526367 nb_pixel_total : 279497 time to create 1 rle with new method : 0.30226945877075195 length of segment : 846 time for calcul the mask position with numpy : 0.0031714439392089844 nb_pixel_total : 45943 time to create 1 rle with old method : 0.05447244644165039 length of segment : 283 time for calcul the mask position with numpy : 0.0011785030364990234 nb_pixel_total : 16082 time to create 1 rle with old method : 0.01817631721496582 length of segment : 262 time for calcul the mask position with numpy : 0.011631250381469727 nb_pixel_total : 150052 time to create 1 rle with new method : 0.01729583740234375 length of segment : 540 time for calcul the mask position with numpy : 0.00042319297790527344 nb_pixel_total : 7113 time to create 1 rle with old method : 0.008136987686157227 length of segment : 68 time for calcul the mask position with numpy : 0.004141330718994141 nb_pixel_total : 70155 time to create 1 rle with old method : 0.07686996459960938 length of segment : 357 time for calcul the mask position with numpy : 0.01365971565246582 nb_pixel_total : 246954 time to create 1 rle with new method : 0.016618967056274414 length of segment : 765 time for calcul the mask position with numpy : 0.0051915645599365234 nb_pixel_total : 105396 time to create 1 rle with old method : 0.11684632301330566 length of segment : 339 time for calcul the mask position with numpy : 0.0015118122100830078 nb_pixel_total : 38780 time to create 1 rle with old method : 0.043087005615234375 length of segment : 234 time for calcul the mask position with numpy : 0.0023272037506103516 nb_pixel_total : 43790 time to create 1 rle with old method : 0.04835939407348633 length of segment : 408 time for calcul the mask position with numpy : 0.0015730857849121094 nb_pixel_total : 25005 time to create 1 rle with old method : 0.027507305145263672 length of segment : 255 time for calcul the mask position with numpy : 0.006869792938232422 nb_pixel_total : 124598 time to create 1 rle with old method : 0.13754558563232422 length of segment : 717 time for calcul the mask position with numpy : 0.0006279945373535156 nb_pixel_total : 15327 time to create 1 rle with old method : 0.01730203628540039 length of segment : 182 time for calcul the mask position with numpy : 0.007123708724975586 nb_pixel_total : 152569 time to create 1 rle with new method : 0.014935016632080078 length of segment : 477 time for calcul the mask position with numpy : 0.006303071975708008 nb_pixel_total : 123591 time to create 1 rle with old method : 0.13682794570922852 length of segment : 421 time for calcul the mask position with numpy : 0.0009553432464599609 nb_pixel_total : 13032 time to create 1 rle with old method : 0.014873266220092773 length of segment : 246 time for calcul the mask position with numpy : 0.0006871223449707031 nb_pixel_total : 12406 time to create 1 rle with old method : 0.014268159866333008 length of segment : 111 time for calcul the mask position with numpy : 0.0033478736877441406 nb_pixel_total : 57255 time to create 1 rle with old method : 0.06358122825622559 length of segment : 361 time for calcul the mask position with numpy : 0.003985404968261719 nb_pixel_total : 76725 time to create 1 rle with old method : 0.08483552932739258 length of segment : 391 time for calcul the mask position with numpy : 0.005053281784057617 nb_pixel_total : 113989 time to create 1 rle with old method : 0.12939453125 length of segment : 446 time for calcul the mask position with numpy : 0.00025725364685058594 nb_pixel_total : 11235 time to create 1 rle with old method : 0.013183355331420898 length of segment : 82 time for calcul the mask position with numpy : 0.0009088516235351562 nb_pixel_total : 16226 time to create 1 rle with old method : 0.018476247787475586 length of segment : 143 time for calcul the mask position with numpy : 0.00030422210693359375 nb_pixel_total : 3749 time to create 1 rle with old method : 0.004608869552612305 length of segment : 70 time for calcul the mask position with numpy : 0.00018405914306640625 nb_pixel_total : 6267 time to create 1 rle with old method : 0.0076143741607666016 length of segment : 87 time for calcul the mask position with numpy : 0.0003514289855957031 nb_pixel_total : 4084 time to create 1 rle with old method : 0.005057334899902344 length of segment : 199 time for calcul the mask position with numpy : 0.00041866302490234375 nb_pixel_total : 16785 time to create 1 rle with old method : 0.019168615341186523 length of segment : 138 time for calcul the mask position with numpy : 0.002223491668701172 nb_pixel_total : 31256 time to create 1 rle with old method : 0.03568220138549805 length of segment : 263 time for calcul the mask position with numpy : 0.00074005126953125 nb_pixel_total : 12729 time to create 1 rle with old method : 0.014757156372070312 length of segment : 166 time for calcul the mask position with numpy : 0.004343748092651367 nb_pixel_total : 62153 time to create 1 rle with old method : 0.08403563499450684 length of segment : 371 time for calcul the mask position with numpy : 0.0003173351287841797 nb_pixel_total : 6554 time to create 1 rle with old method : 0.007666587829589844 length of segment : 120 time for calcul the mask position with numpy : 0.003840208053588867 nb_pixel_total : 88379 time to create 1 rle with old method : 0.10349225997924805 length of segment : 400 time for calcul the mask position with numpy : 0.0006368160247802734 nb_pixel_total : 17303 time to create 1 rle with old method : 0.020329713821411133 length of segment : 243 time for calcul the mask position with numpy : 0.0028603076934814453 nb_pixel_total : 65023 time to create 1 rle with old method : 0.07487821578979492 length of segment : 322 time for calcul the mask position with numpy : 0.0006723403930664062 nb_pixel_total : 7491 time to create 1 rle with old method : 0.00903463363647461 length of segment : 126 time for calcul the mask position with numpy : 0.0006661415100097656 nb_pixel_total : 12016 time to create 1 rle with old method : 0.013960599899291992 length of segment : 134 time for calcul the mask position with numpy : 0.0010800361633300781 nb_pixel_total : 15519 time to create 1 rle with old method : 0.019237756729125977 length of segment : 168 time for calcul the mask position with numpy : 0.0012462139129638672 nb_pixel_total : 20240 time to create 1 rle with old method : 0.023532867431640625 length of segment : 194 time for calcul the mask position with numpy : 0.0010275840759277344 nb_pixel_total : 15243 time to create 1 rle with old method : 0.01789116859436035 length of segment : 220 time for calcul the mask position with numpy : 0.004952430725097656 nb_pixel_total : 81321 time to create 1 rle with old method : 0.09201955795288086 length of segment : 408 time for calcul the mask position with numpy : 0.0015435218811035156 nb_pixel_total : 17934 time to create 1 rle with old method : 0.020833253860473633 length of segment : 202 time for calcul the mask position with numpy : 0.00048041343688964844 nb_pixel_total : 6340 time to create 1 rle with old method : 0.007643222808837891 length of segment : 104 time for calcul the mask position with numpy : 0.0018107891082763672 nb_pixel_total : 37557 time to create 1 rle with old method : 0.06171917915344238 length of segment : 175 time for calcul the mask position with numpy : 0.003000497817993164 nb_pixel_total : 47492 time to create 1 rle with old method : 0.05355048179626465 length of segment : 462 time for calcul the mask position with numpy : 0.004598140716552734 nb_pixel_total : 71850 time to create 1 rle with old method : 0.08169364929199219 length of segment : 302 time for calcul the mask position with numpy : 0.0008170604705810547 nb_pixel_total : 21782 time to create 1 rle with old method : 0.02721118927001953 length of segment : 215 time for calcul the mask position with numpy : 0.0031180381774902344 nb_pixel_total : 48954 time to create 1 rle with old method : 0.05792641639709473 length of segment : 383 time for calcul the mask position with numpy : 0.0009670257568359375 nb_pixel_total : 12591 time to create 1 rle with old method : 0.014784574508666992 length of segment : 176 time for calcul the mask position with numpy : 0.0010776519775390625 nb_pixel_total : 16795 time to create 1 rle with old method : 0.019591569900512695 length of segment : 161 time for calcul the mask position with numpy : 0.0007028579711914062 nb_pixel_total : 11879 time to create 1 rle with old method : 0.01386570930480957 length of segment : 128 time for calcul the mask position with numpy : 0.00027942657470703125 nb_pixel_total : 7330 time to create 1 rle with old method : 0.012512922286987305 length of segment : 122 time for calcul the mask position with numpy : 0.005731821060180664 nb_pixel_total : 109927 time to create 1 rle with old method : 0.13053464889526367 length of segment : 634 time for calcul the mask position with numpy : 0.0011851787567138672 nb_pixel_total : 16855 time to create 1 rle with old method : 0.021930932998657227 length of segment : 187 time for calcul the mask position with numpy : 0.0027704238891601562 nb_pixel_total : 66635 time to create 1 rle with old method : 0.07552719116210938 length of segment : 292 time for calcul the mask position with numpy : 0.0014009475708007812 nb_pixel_total : 29427 time to create 1 rle with old method : 0.0334169864654541 length of segment : 411 time for calcul the mask position with numpy : 0.007426261901855469 nb_pixel_total : 139719 time to create 1 rle with old method : 0.15579605102539062 length of segment : 523 time for calcul the mask position with numpy : 0.00482487678527832 nb_pixel_total : 96568 time to create 1 rle with old method : 0.1091303825378418 length of segment : 392 time for calcul the mask position with numpy : 0.0017421245574951172 nb_pixel_total : 43018 time to create 1 rle with old method : 0.04936957359313965 length of segment : 315 time for calcul the mask position with numpy : 0.003036022186279297 nb_pixel_total : 99643 time to create 1 rle with old method : 0.11335134506225586 length of segment : 463 time for calcul the mask position with numpy : 0.0006306171417236328 nb_pixel_total : 7831 time to create 1 rle with old method : 0.009207725524902344 length of segment : 128 time for calcul the mask position with numpy : 0.0007586479187011719 nb_pixel_total : 24027 time to create 1 rle with old method : 0.027654409408569336 length of segment : 201 time for calcul the mask position with numpy : 0.0018160343170166016 nb_pixel_total : 45618 time to create 1 rle with old method : 0.0505216121673584 length of segment : 354 time for calcul the mask position with numpy : 0.0009930133819580078 nb_pixel_total : 22652 time to create 1 rle with old method : 0.025604248046875 length of segment : 168 time for calcul the mask position with numpy : 0.00039958953857421875 nb_pixel_total : 17968 time to create 1 rle with old method : 0.02051234245300293 length of segment : 202 time for calcul the mask position with numpy : 0.0014445781707763672 nb_pixel_total : 17666 time to create 1 rle with old method : 0.020418405532836914 length of segment : 213 time spent for convertir_results : 21.10452151298523 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 224 chid ids of type : 3594 Number RLEs to save : 69107 save missing photos in datou_result : time spend for datou_step_exec : 95.58851909637451 time spend to save output : 4.758382081985474 total time spend for step 1 : 100.34690117835999 step2:crop_condition Wed Apr 9 12:22:11 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 : 8 ! batch 1 Loaded 224 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 ! map_result returned by crop_photo_return_map_crop : length : 158 About to insert : list_path_to_insert length 158 new photo from crops ! About to upload 158 photos upload in portfolio : 3736932 init cache_photo without model_param we have 158 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744194166_945173 we have uploaded 158 photos in the portfolio 3736932 time of upload the photos Elapsed time : 46.47894525527954 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 ! map_result returned by crop_photo_return_map_crop : length : 33 About to insert : list_path_to_insert length 33 new photo from crops ! About to upload 33 photos upload in portfolio : 3736932 init cache_photo without model_param we have 33 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744194224_945173 we have uploaded 33 photos in the portfolio 3736932 time of upload the photos Elapsed time : 9.459035158157349 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 ! 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/1744194235_945173 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.8606512546539307 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 ! map_result returned by crop_photo_return_map_crop : length : 30 About to insert : list_path_to_insert length 30 new photo from crops ! About to upload 30 photos upload in portfolio : 3736932 init cache_photo without model_param we have 30 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744194254_945173 we have uploaded 30 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.953890085220337 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 ! 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/1744194264_945173 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6167819499969482 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/1744194267_945173 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.5602695941925049 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350740015, 1350740010, 1350739831, 1350739798, 1350739779, 1350739530, 1350739299, 1350739216] Looping around the photos to save general results len do output : 224 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None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739831', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739798', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739779', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739530', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739299', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739216', None, None, None, None, None, '2734297') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 680 time used for this insertion : 0.04337191581726074 save_final save missing photos in datou_result : time spend for datou_step_exec : 136.2615246772766 time spend to save output : 0.050940752029418945 total time spend for step 2 : 136.31246542930603 step3:rle_unique_nms_with_priority Wed Apr 9 12:24:27 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 224 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 21 nb_hashtags : 4 time to prepare the origin masks : 9.895642042160034 time for calcul the mask position with numpy : 0.5988039970397949 nb_pixel_total : 5589490 time to create 1 rle with new method : 0.8025214672088623 time for calcul the mask position with numpy : 0.029222726821899414 nb_pixel_total : 34934 time to create 1 rle with old method : 0.04640650749206543 time for calcul the mask position with numpy : 0.024579286575317383 nb_pixel_total : 35868 time to create 1 rle with old method : 0.04174375534057617 time for calcul the mask position with numpy : 0.022238492965698242 nb_pixel_total : 57385 time to create 1 rle with old method : 0.06482243537902832 time for calcul the mask position with numpy : 0.022945404052734375 nb_pixel_total : 111846 time to create 1 rle with old method : 0.1298844814300537 time for calcul the mask position with numpy : 0.02226424217224121 nb_pixel_total : 97108 time to create 1 rle with old method : 0.11479496955871582 time for calcul the mask position with numpy : 0.024912118911743164 nb_pixel_total : 68055 time to create 1 rle with old method : 0.07644152641296387 time for calcul the mask position with numpy : 0.02387213706970215 nb_pixel_total : 84869 time to create 1 rle with old method : 0.09982800483703613 time for calcul the mask position with numpy : 0.02328038215637207 nb_pixel_total : 14996 time to create 1 rle with old method : 0.017235755920410156 time for calcul the mask position with numpy : 0.02332162857055664 nb_pixel_total : 25511 time to create 1 rle with old method : 0.028481006622314453 time for calcul the mask position with numpy : 0.021309614181518555 nb_pixel_total : 25482 time to create 1 rle with old method : 0.02837538719177246 time for calcul the mask position with numpy : 0.022894620895385742 nb_pixel_total : 219200 time to create 1 rle with new method : 1.0882108211517334 time for calcul the mask position with numpy : 0.02200484275817871 nb_pixel_total : 40490 time to create 1 rle with old method : 0.04501152038574219 time for calcul the mask position with numpy : 0.022455692291259766 nb_pixel_total : 11395 time to create 1 rle with old method : 0.01300501823425293 time for calcul the mask position with numpy : 0.0234529972076416 nb_pixel_total : 44349 time to create 1 rle with old method : 0.0494229793548584 time for calcul the mask position with numpy : 0.024308204650878906 nb_pixel_total : 35430 time to create 1 rle with old method : 0.03966951370239258 time for calcul the mask position with numpy : 0.023610353469848633 nb_pixel_total : 16440 time to create 1 rle with old method : 0.01851177215576172 time for calcul the mask position with numpy : 0.023407697677612305 nb_pixel_total : 20742 time to create 1 rle with old method : 0.023214340209960938 time for calcul the mask position with numpy : 0.024796247482299805 nb_pixel_total : 138899 time to create 1 rle with old method : 0.1616816520690918 time for calcul the mask position with numpy : 0.0365447998046875 nb_pixel_total : 84436 time to create 1 rle with old method : 0.09410691261291504 time for calcul the mask position with numpy : 0.03509211540222168 nb_pixel_total : 268020 time to create 1 rle with new method : 0.3457350730895996 time for calcul the mask position with numpy : 0.034644365310668945 nb_pixel_total : 25295 time to create 1 rle with old method : 0.029593944549560547 create new chi : 4.571805953979492 time to delete rle : 0.022657155990600586 batch 1 Loaded 43 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 15989 TO DO : save crop sub photo not yet done ! save time : 2.6235506534576416 nb_obj : 24 nb_hashtags : 3 time to prepare the origin masks : 13.233299255371094 time for calcul the mask position with numpy : 0.4629034996032715 nb_pixel_total : 4953835 time to create 1 rle with new method : 1.0125114917755127 time for calcul the mask position with numpy : 0.02936840057373047 nb_pixel_total : 15190 time to create 1 rle with old method : 0.0169217586517334 time for calcul the mask position with numpy : 0.023125171661376953 nb_pixel_total : 306600 time to create 1 rle with new method : 0.8531012535095215 time for calcul the mask position with numpy : 0.02455925941467285 nb_pixel_total : 103244 time to create 1 rle with old method : 0.11643433570861816 time for calcul the mask position with numpy : 0.024416685104370117 nb_pixel_total : 55516 time to create 1 rle with old method : 0.07087922096252441 time for calcul the mask position with numpy : 0.029444217681884766 nb_pixel_total : 11002 time to create 1 rle with old method : 0.012456417083740234 time for calcul the mask position with numpy : 0.027564048767089844 nb_pixel_total : 40859 time to create 1 rle with old method : 0.0459904670715332 time for calcul the mask position with numpy : 0.028020143508911133 nb_pixel_total : 29324 time to create 1 rle with old method : 0.03332328796386719 time for calcul the mask position with numpy : 0.023073673248291016 nb_pixel_total : 44723 time to create 1 rle with old method : 0.04963350296020508 time for calcul the mask position with numpy : 0.023195266723632812 nb_pixel_total : 9624 time to create 1 rle with old method : 0.012555599212646484 time for calcul the mask position with numpy : 0.022399425506591797 nb_pixel_total : 221907 time to create 1 rle with new method : 1.0861923694610596 time for calcul the mask position with numpy : 0.021048307418823242 nb_pixel_total : 75784 time to create 1 rle with old method : 0.08295845985412598 time for calcul the mask position with numpy : 0.02049875259399414 nb_pixel_total : 14776 time to create 1 rle with old method : 0.01604294776916504 time for calcul the mask position with numpy : 0.020929813385009766 nb_pixel_total : 61017 time to create 1 rle with old method : 0.09611988067626953 time for calcul the mask position with numpy : 0.02477407455444336 nb_pixel_total : 98898 time to create 1 rle with old method : 0.11085796356201172 time for calcul the mask position with numpy : 0.020513057708740234 nb_pixel_total : 18820 time to create 1 rle with old method : 0.02104020118713379 time for calcul the mask position with numpy : 0.023411273956298828 nb_pixel_total : 210723 time to create 1 rle with new method : 0.6871709823608398 time for calcul the mask position with numpy : 0.02262139320373535 nb_pixel_total : 7748 time to create 1 rle with old method : 0.008705615997314453 time for calcul the mask position with numpy : 0.02191948890686035 nb_pixel_total : 25028 time to create 1 rle with old method : 0.02919745445251465 time for calcul the mask position with numpy : 0.022679805755615234 nb_pixel_total : 165757 time to create 1 rle with new method : 0.4270763397216797 time for calcul the mask position with numpy : 0.022244691848754883 nb_pixel_total : 145917 time to create 1 rle with old method : 0.1610403060913086 time for calcul the mask position with numpy : 0.021915435791015625 nb_pixel_total : 38249 time to create 1 rle with old method : 0.04248452186584473 time for calcul the mask position with numpy : 0.022870302200317383 nb_pixel_total : 47242 time to create 1 rle with old method : 0.07531356811523438 time for calcul the mask position with numpy : 0.03641104698181152 nb_pixel_total : 264742 time to create 1 rle with new method : 0.8545761108398438 time for calcul the mask position with numpy : 0.03547525405883789 nb_pixel_total : 83715 time to create 1 rle with old method : 0.09371685981750488 create new chi : 7.223076105117798 time to delete rle : 0.0034475326538085938 batch 1 Loaded 49 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++Number RLEs to save : 21528 TO DO : save crop sub photo not yet done ! save time : 1.5482072830200195 nb_obj : 29 nb_hashtags : 4 time to prepare the origin masks : 4.006138563156128 time for calcul the mask position with numpy : 0.721853494644165 nb_pixel_total : 5974437 time to create 1 rle with new method : 0.9108805656433105 time for calcul the mask position with numpy : 0.028670310974121094 nb_pixel_total : 20334 time to create 1 rle with old method : 0.022002220153808594 time for calcul the mask position with numpy : 0.028580188751220703 nb_pixel_total : 24515 time to create 1 rle with old method : 0.027150869369506836 time for calcul the mask position with numpy : 0.028645992279052734 nb_pixel_total : 24786 time to create 1 rle with old method : 0.026606082916259766 time for calcul the mask position with numpy : 0.027652502059936523 nb_pixel_total : 3046 time to create 1 rle with old method : 0.00333404541015625 time for calcul the mask position with numpy : 0.02832174301147461 nb_pixel_total : 145284 time to create 1 rle with old method : 0.15758252143859863 time for calcul the mask position with numpy : 0.028284311294555664 nb_pixel_total : 140546 time to create 1 rle with old method : 0.1524343490600586 time for calcul the mask position with numpy : 0.027736902236938477 nb_pixel_total : 35405 time to create 1 rle with old method : 0.0381922721862793 time for calcul the mask position with numpy : 0.028121232986450195 nb_pixel_total : 9935 time to create 1 rle with old method : 0.010695457458496094 time for calcul the mask position with numpy : 0.02823472023010254 nb_pixel_total : 34221 time to create 1 rle with old method : 0.038048505783081055 time for calcul the mask position with numpy : 0.029419422149658203 nb_pixel_total : 97971 time to create 1 rle with old method : 0.10792350769042969 time for calcul the mask position with numpy : 0.02881765365600586 nb_pixel_total : 14254 time to create 1 rle with old method : 0.015827655792236328 time for calcul the mask position with numpy : 0.02876114845275879 nb_pixel_total : 17583 time to create 1 rle with old method : 0.019442319869995117 time for calcul the mask position with numpy : 0.028804302215576172 nb_pixel_total : 13008 time to create 1 rle with old method : 0.014599084854125977 time for calcul the mask position with numpy : 0.028885364532470703 nb_pixel_total : 45373 time to create 1 rle with old method : 0.04997968673706055 time for calcul the mask position with numpy : 0.0289309024810791 nb_pixel_total : 16606 time to create 1 rle with old method : 0.01856398582458496 time for calcul the mask position with numpy : 0.029100656509399414 nb_pixel_total : 93398 time to create 1 rle with old method : 0.1040492057800293 time for calcul the mask position with numpy : 0.029296159744262695 nb_pixel_total : 7376 time to create 1 rle with old method : 0.00799107551574707 time for calcul the mask position with numpy : 0.027993202209472656 nb_pixel_total : 2206 time to create 1 rle with old method : 0.002794027328491211 time for calcul the mask position with numpy : 0.0281832218170166 nb_pixel_total : 29518 time to create 1 rle with old method : 0.032048702239990234 time for calcul the mask position with numpy : 0.02809882164001465 nb_pixel_total : 6400 time to create 1 rle with old method : 0.007016181945800781 time for calcul the mask position with numpy : 0.02797532081604004 nb_pixel_total : 18434 time to create 1 rle with old method : 0.01972794532775879 time for calcul the mask position with numpy : 0.027132272720336914 nb_pixel_total : 8054 time to create 1 rle with old method : 0.00882267951965332 time for calcul the mask position with numpy : 0.02767658233642578 nb_pixel_total : 23910 time to create 1 rle with old method : 0.02543783187866211 time for calcul the mask position with numpy : 0.027939796447753906 nb_pixel_total : 54014 time to create 1 rle with old method : 0.058332204818725586 time for calcul the mask position with numpy : 0.028110265731811523 nb_pixel_total : 122162 time to create 1 rle with old method : 0.13072466850280762 time for calcul the mask position with numpy : 0.02780294418334961 nb_pixel_total : 26583 time to create 1 rle with old method : 0.028367996215820312 time for calcul the mask position with numpy : 0.027418851852416992 nb_pixel_total : 15188 time to create 1 rle with old method : 0.0166776180267334 time for calcul the mask position with numpy : 0.02778792381286621 nb_pixel_total : 19601 time to create 1 rle with old method : 0.021218538284301758 time for calcul the mask position with numpy : 0.02740192413330078 nb_pixel_total : 6092 time to create 1 rle with old method : 0.0065386295318603516 create new chi : 3.662592649459839 time to delete rle : 0.004302978515625 batch 1 Loaded 59 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16724 TO DO : save crop sub photo not yet done ! save time : 1.1612961292266846 nb_obj : 29 nb_hashtags : 3 time to prepare the origin masks : 3.5438084602355957 time for calcul the mask position with numpy : 0.9017660617828369 nb_pixel_total : 6381448 time to create 1 rle with new method : 1.63478422164917 time for calcul the mask position with numpy : 0.02835679054260254 nb_pixel_total : 26865 time to create 1 rle with old method : 0.029056310653686523 time for calcul the mask position with numpy : 0.0280454158782959 nb_pixel_total : 10520 time to create 1 rle with old method : 0.011362075805664062 time for calcul the mask position with numpy : 0.03300285339355469 nb_pixel_total : 24403 time to create 1 rle with old method : 0.039224863052368164 time for calcul the mask position with numpy : 0.030916690826416016 nb_pixel_total : 4839 time to create 1 rle with old method : 0.005430698394775391 time for calcul the mask position with numpy : 0.0288543701171875 nb_pixel_total : 34410 time to create 1 rle with old method : 0.03717780113220215 time for calcul the mask position with numpy : 0.028060197830200195 nb_pixel_total : 13664 time to create 1 rle with old method : 0.014697790145874023 time for calcul the mask position with numpy : 0.02799391746520996 nb_pixel_total : 21267 time to create 1 rle with old method : 0.02269911766052246 time for calcul the mask position with numpy : 0.02872776985168457 nb_pixel_total : 14293 time to create 1 rle with old method : 0.015474319458007812 time for calcul the mask position with numpy : 0.028334856033325195 nb_pixel_total : 49618 time to create 1 rle with old method : 0.05609393119812012 time for calcul the mask position with numpy : 0.028960227966308594 nb_pixel_total : 19964 time to create 1 rle with old method : 0.023776531219482422 time for calcul the mask position with numpy : 0.030727863311767578 nb_pixel_total : 3388 time to create 1 rle with old method : 0.0037539005279541016 time for calcul the mask position with numpy : 0.02851128578186035 nb_pixel_total : 10343 time to create 1 rle with old method : 0.011285066604614258 time for calcul the mask position with numpy : 0.02826690673828125 nb_pixel_total : 30995 time to create 1 rle with old method : 0.03472590446472168 time for calcul the mask position with numpy : 0.030067920684814453 nb_pixel_total : 17073 time to create 1 rle with old method : 0.022230148315429688 time for calcul the mask position with numpy : 0.029929161071777344 nb_pixel_total : 72401 time to create 1 rle with old method : 0.08184981346130371 time for calcul the mask position with numpy : 0.029274702072143555 nb_pixel_total : 26199 time to create 1 rle with old method : 0.030486106872558594 time for calcul the mask position with numpy : 0.030106544494628906 nb_pixel_total : 9663 time to create 1 rle with old method : 0.011275768280029297 time for calcul the mask position with numpy : 0.028965234756469727 nb_pixel_total : 34107 time to create 1 rle with old method : 0.03905439376831055 time for calcul the mask position with numpy : 0.028375864028930664 nb_pixel_total : 12164 time to create 1 rle with old method : 0.013963699340820312 time for calcul the mask position with numpy : 0.032875776290893555 nb_pixel_total : 4208 time to create 1 rle with old method : 0.007003307342529297 time for calcul the mask position with numpy : 0.03960919380187988 nb_pixel_total : 4972 time to create 1 rle with old method : 0.007187843322753906 time for calcul the mask position with numpy : 0.02986311912536621 nb_pixel_total : 27530 time to create 1 rle with old method : 0.032042503356933594 time for calcul the mask position with numpy : 0.02921271324157715 nb_pixel_total : 7998 time to create 1 rle with old method : 0.008922100067138672 time for calcul the mask position with numpy : 0.028747081756591797 nb_pixel_total : 4436 time to create 1 rle with old method : 0.0054972171783447266 time for calcul the mask position with numpy : 0.028995990753173828 nb_pixel_total : 58946 time to create 1 rle with old method : 0.0656425952911377 time for calcul the mask position with numpy : 0.0291445255279541 nb_pixel_total : 26106 time to create 1 rle with old method : 0.0295255184173584 time for calcul the mask position with numpy : 0.0293581485748291 nb_pixel_total : 11504 time to create 1 rle with old method : 0.01297760009765625 time for calcul the mask position with numpy : 0.0308072566986084 nb_pixel_total : 83788 time to create 1 rle with old method : 0.09893202781677246 time for calcul the mask position with numpy : 0.031394243240356445 nb_pixel_total : 3128 time to create 1 rle with old method : 0.003543853759765625 create new chi : 4.213862419128418 time to delete rle : 0.0033555030822753906 batch 1 Loaded 59 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 14098 TO DO : save crop sub photo not yet done ! save time : 2.778106927871704 nb_obj : 26 nb_hashtags : 3 time to prepare the origin masks : 4.593273878097534 time for calcul the mask position with numpy : 0.7387163639068604 nb_pixel_total : 5089099 time to create 1 rle with new method : 1.0093166828155518 time for calcul the mask position with numpy : 0.02927231788635254 nb_pixel_total : 12372 time to create 1 rle with old method : 0.013854026794433594 time for calcul the mask position with numpy : 0.0295712947845459 nb_pixel_total : 57654 time to create 1 rle with old method : 0.06780552864074707 time for calcul the mask position with numpy : 0.02946186065673828 nb_pixel_total : 86717 time to create 1 rle with old method : 0.09845495223999023 time for calcul the mask position with numpy : 0.0290224552154541 nb_pixel_total : 36898 time to create 1 rle with old method : 0.0414578914642334 time for calcul the mask position with numpy : 0.02904510498046875 nb_pixel_total : 57958 time to create 1 rle with old method : 0.06451892852783203 time for calcul the mask position with numpy : 0.029035091400146484 nb_pixel_total : 69555 time to create 1 rle with old method : 0.0772542953491211 time for calcul the mask position with numpy : 0.03556108474731445 nb_pixel_total : 501866 time to create 1 rle with new method : 0.7946004867553711 time for calcul the mask position with numpy : 0.028996706008911133 nb_pixel_total : 11136 time to create 1 rle with old method : 0.012950897216796875 time for calcul the mask position with numpy : 0.03146219253540039 nb_pixel_total : 275818 time to create 1 rle with new method : 0.8003811836242676 time for calcul the mask position with numpy : 0.030061006546020508 nb_pixel_total : 35106 time to create 1 rle with old method : 0.039000511169433594 time for calcul the mask position with numpy : 0.02952885627746582 nb_pixel_total : 35176 time to create 1 rle with old method : 0.0391542911529541 time for calcul the mask position with numpy : 0.029366493225097656 nb_pixel_total : 2132 time to create 1 rle with old method : 0.0024220943450927734 time for calcul the mask position with numpy : 0.030640125274658203 nb_pixel_total : 210079 time to create 1 rle with new method : 0.9859409332275391 time for calcul the mask position with numpy : 0.03315258026123047 nb_pixel_total : 25096 time to create 1 rle with old method : 0.0323331356048584 time for calcul the mask position with numpy : 0.03234553337097168 nb_pixel_total : 260698 time to create 1 rle with new method : 0.8479709625244141 time for calcul the mask position with numpy : 0.027591466903686523 nb_pixel_total : 24612 time to create 1 rle with old method : 0.02700662612915039 time for calcul the mask position with numpy : 0.028422117233276367 nb_pixel_total : 36555 time to create 1 rle with old method : 0.04004502296447754 time for calcul the mask position with numpy : 0.02836441993713379 nb_pixel_total : 13993 time to create 1 rle with old method : 0.015631675720214844 time for calcul the mask position with numpy : 0.029116153717041016 nb_pixel_total : 29310 time to create 1 rle with old method : 0.03258204460144043 time for calcul the mask position with numpy : 0.02906942367553711 nb_pixel_total : 23568 time to create 1 rle with old method : 0.02628612518310547 time for calcul the mask position with numpy : 0.02914285659790039 nb_pixel_total : 39112 time to create 1 rle with old method : 0.04401373863220215 time for calcul the mask position with numpy : 0.02938699722290039 nb_pixel_total : 53622 time to create 1 rle with old method : 0.06195950508117676 time for calcul the mask position with numpy : 0.02876901626586914 nb_pixel_total : 20199 time to create 1 rle with old method : 0.0222017765045166 time for calcul the mask position with numpy : 0.028231382369995117 nb_pixel_total : 22424 time to create 1 rle with old method : 0.02399158477783203 time for calcul the mask position with numpy : 0.028303146362304688 nb_pixel_total : 7461 time to create 1 rle with old method : 0.008340120315551758 time for calcul the mask position with numpy : 0.028482913970947266 nb_pixel_total : 12024 time to create 1 rle with old method : 0.01342630386352539 create new chi : 6.895227909088135 time to delete rle : 0.0028171539306640625 batch 1 Loaded 53 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++Number RLEs to save : 20071 TO DO : save crop sub photo not yet done ! save time : 1.487708330154419 nb_obj : 33 nb_hashtags : 3 time to prepare the origin masks : 5.0384202003479 time for calcul the mask position with numpy : 0.6134669780731201 nb_pixel_total : 4448820 time to create 1 rle with new method : 1.2158269882202148 time for calcul the mask position with numpy : 0.03020930290222168 nb_pixel_total : 169983 time to create 1 rle with new method : 1.3435990810394287 time for calcul the mask position with numpy : 0.028847694396972656 nb_pixel_total : 9864 time to create 1 rle with old method : 0.01098489761352539 time for calcul the mask position with numpy : 0.028734207153320312 nb_pixel_total : 10103 time to create 1 rle with old method : 0.011343717575073242 time for calcul the mask position with numpy : 0.02849411964416504 nb_pixel_total : 85988 time to create 1 rle with old method : 0.09546041488647461 time for calcul the mask position with numpy : 0.028835773468017578 nb_pixel_total : 35850 time to create 1 rle with old method : 0.04001617431640625 time for calcul the mask position with numpy : 0.02911663055419922 nb_pixel_total : 107211 time to create 1 rle with old method : 0.11983346939086914 time for calcul the mask position with numpy : 0.028738021850585938 nb_pixel_total : 32001 time to create 1 rle with old method : 0.035491228103637695 time for calcul the mask position with numpy : 0.028921842575073242 nb_pixel_total : 16149 time to create 1 rle with old method : 0.018132448196411133 time for calcul the mask position with numpy : 0.028934001922607422 nb_pixel_total : 3333 time to create 1 rle with old method : 0.003931760787963867 time for calcul the mask position with numpy : 0.029357433319091797 nb_pixel_total : 133860 time to create 1 rle with old method : 0.15330862998962402 time for calcul the mask position with numpy : 0.028557777404785156 nb_pixel_total : 32292 time to create 1 rle with old method : 0.03551149368286133 time for calcul the mask position with numpy : 0.028797149658203125 nb_pixel_total : 47192 time to create 1 rle with old method : 0.052324771881103516 time for calcul the mask position with numpy : 0.028868675231933594 nb_pixel_total : 47754 time to create 1 rle with old method : 0.05253934860229492 time for calcul the mask position with numpy : 0.03005385398864746 nb_pixel_total : 279497 time to create 1 rle with new method : 1.1763858795166016 time for calcul the mask position with numpy : 0.029433012008666992 nb_pixel_total : 107495 time to create 1 rle with old method : 0.11960792541503906 time for calcul the mask position with numpy : 0.02971172332763672 nb_pixel_total : 30811 time to create 1 rle with old method : 0.03478884696960449 time for calcul the mask position with numpy : 0.03327775001525879 nb_pixel_total : 536459 time to create 1 rle with new method : 1.0099191665649414 time for calcul the mask position with numpy : 0.03220009803771973 nb_pixel_total : 31716 time to create 1 rle with old method : 0.035559892654418945 time for calcul the mask position with numpy : 0.030205488204956055 nb_pixel_total : 268807 time to create 1 rle with new method : 0.7073140144348145 time for calcul the mask position with numpy : 0.029331445693969727 nb_pixel_total : 110646 time to create 1 rle with old method : 0.12238025665283203 time for calcul the mask position with numpy : 0.029108047485351562 nb_pixel_total : 70567 time to create 1 rle with old method : 0.07854127883911133 time for calcul the mask position with numpy : 0.029057979583740234 nb_pixel_total : 25867 time to create 1 rle with old method : 0.028766393661499023 time for calcul the mask position with numpy : 0.02910780906677246 nb_pixel_total : 79359 time to create 1 rle with old method : 0.0878300666809082 time for calcul the mask position with numpy : 0.029163599014282227 nb_pixel_total : 40417 time to create 1 rle with old method : 0.0449976921081543 time for calcul the mask position with numpy : 0.02955913543701172 nb_pixel_total : 37754 time to create 1 rle with old method : 0.04196786880493164 time for calcul the mask position with numpy : 0.029109954833984375 nb_pixel_total : 63004 time to create 1 rle with old method : 0.06975221633911133 time for calcul the mask position with numpy : 0.029265403747558594 nb_pixel_total : 45943 time to create 1 rle with old method : 0.051367759704589844 time for calcul the mask position with numpy : 0.0290679931640625 nb_pixel_total : 23340 time to create 1 rle with old method : 0.025927066802978516 time for calcul the mask position with numpy : 0.029315948486328125 nb_pixel_total : 50498 time to create 1 rle with old method : 0.05532193183898926 time for calcul the mask position with numpy : 0.029191017150878906 nb_pixel_total : 22733 time to create 1 rle with old method : 0.02514195442199707 time for calcul the mask position with numpy : 0.029077768325805664 nb_pixel_total : 6239 time to create 1 rle with old method : 0.0071256160736083984 time for calcul the mask position with numpy : 0.029199838638305664 nb_pixel_total : 37054 time to create 1 rle with old method : 0.04130744934082031 time for calcul the mask position with numpy : 0.029216766357421875 nb_pixel_total : 1634 time to create 1 rle with old method : 0.0019099712371826172 create new chi : 8.674468517303467 time to delete rle : 0.005812168121337891 batch 1 Loaded 67 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25335 TO DO : save crop sub photo not yet done ! save time : 1.5068962574005127 nb_obj : 34 nb_hashtags : 3 time to prepare the origin masks : 4.368529319763184 time for calcul the mask position with numpy : 0.8642430305480957 nb_pixel_total : 5313159 time to create 1 rle with new method : 0.9167578220367432 time for calcul the mask position with numpy : 0.02920079231262207 nb_pixel_total : 13032 time to create 1 rle with old method : 0.014909744262695312 time for calcul the mask position with numpy : 0.028974056243896484 nb_pixel_total : 12729 time to create 1 rle with old method : 0.014408588409423828 time for calcul the mask position with numpy : 0.02993917465209961 nb_pixel_total : 16082 time to create 1 rle with old method : 0.018218517303466797 time for calcul the mask position with numpy : 0.02899003028869629 nb_pixel_total : 6554 time to create 1 rle with old method : 0.007395267486572266 time for calcul the mask position with numpy : 0.029482603073120117 nb_pixel_total : 62153 time to create 1 rle with old method : 0.07334232330322266 time for calcul the mask position with numpy : 0.03057694435119629 nb_pixel_total : 16226 time to create 1 rle with old method : 0.021646738052368164 time for calcul the mask position with numpy : 0.029732465744018555 nb_pixel_total : 31256 time to create 1 rle with old method : 0.03511524200439453 time for calcul the mask position with numpy : 0.03135251998901367 nb_pixel_total : 246954 time to create 1 rle with new method : 0.6963200569152832 time for calcul the mask position with numpy : 0.030814647674560547 nb_pixel_total : 113989 time to create 1 rle with old method : 0.12794828414916992 time for calcul the mask position with numpy : 0.030553817749023438 nb_pixel_total : 70155 time to create 1 rle with old method : 0.07813382148742676 time for calcul the mask position with numpy : 0.029507160186767578 nb_pixel_total : 88379 time to create 1 rle with old method : 0.09745025634765625 time for calcul the mask position with numpy : 0.029577255249023438 nb_pixel_total : 123591 time to create 1 rle with old method : 0.1368393898010254 time for calcul the mask position with numpy : 0.034438133239746094 nb_pixel_total : 76725 time to create 1 rle with old method : 0.0862283706665039 time for calcul the mask position with numpy : 0.029230356216430664 nb_pixel_total : 38780 time to create 1 rle with old method : 0.04851341247558594 time for calcul the mask position with numpy : 0.029443740844726562 nb_pixel_total : 15519 time to create 1 rle with old method : 0.019033193588256836 time for calcul the mask position with numpy : 0.02911067008972168 nb_pixel_total : 12406 time to create 1 rle with old method : 0.015542268753051758 time for calcul the mask position with numpy : 0.02977466583251953 nb_pixel_total : 65023 time to create 1 rle with old method : 0.07281494140625 time for calcul the mask position with numpy : 0.029369831085205078 nb_pixel_total : 12016 time to create 1 rle with old method : 0.013592720031738281 time for calcul the mask position with numpy : 0.029842615127563477 nb_pixel_total : 124598 time to create 1 rle with old method : 0.13975095748901367 time for calcul the mask position with numpy : 0.029274702072143555 nb_pixel_total : 7491 time to create 1 rle with old method : 0.008516788482666016 time for calcul the mask position with numpy : 0.029912948608398438 nb_pixel_total : 105396 time to create 1 rle with old method : 0.13504672050476074 time for calcul the mask position with numpy : 0.03434896469116211 nb_pixel_total : 16785 time to create 1 rle with old method : 0.01926255226135254 time for calcul the mask position with numpy : 0.03103160858154297 nb_pixel_total : 43790 time to create 1 rle with old method : 0.05043911933898926 time for calcul the mask position with numpy : 0.03023052215576172 nb_pixel_total : 1166 time to create 1 rle with old method : 0.0015575885772705078 time for calcul the mask position with numpy : 0.03083014488220215 nb_pixel_total : 2936 time to create 1 rle with old method : 0.0036067962646484375 time for calcul the mask position with numpy : 0.0340428352355957 nb_pixel_total : 15036 time to create 1 rle with old method : 0.0170285701751709 time for calcul the mask position with numpy : 0.030060768127441406 nb_pixel_total : 1358 time to create 1 rle with old method : 0.0016169548034667969 time for calcul the mask position with numpy : 0.03310728073120117 nb_pixel_total : 150052 time to create 1 rle with new method : 2.12929630279541 time for calcul the mask position with numpy : 0.02941417694091797 nb_pixel_total : 7113 time to create 1 rle with old method : 0.011553287506103516 time for calcul the mask position with numpy : 0.032857418060302734 nb_pixel_total : 2652 time to create 1 rle with old method : 0.003058195114135742 time for calcul the mask position with numpy : 0.029254674911499023 nb_pixel_total : 57255 time to create 1 rle with old method : 0.06411504745483398 time for calcul the mask position with numpy : 0.02939772605895996 nb_pixel_total : 2310 time to create 1 rle with old method : 0.002763032913208008 time for calcul the mask position with numpy : 0.03013896942138672 nb_pixel_total : 152569 time to create 1 rle with new method : 1.4115595817565918 time for calcul the mask position with numpy : 0.029326677322387695 nb_pixel_total : 25005 time to create 1 rle with old method : 0.02793097496032715 create new chi : 8.53537917137146 time to delete rle : 0.004499197006225586 batch 1 Loaded 69 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20627 TO DO : save crop sub photo not yet done ! save time : 1.2239408493041992 nb_obj : 28 nb_hashtags : 4 time to prepare the origin masks : 3.8779938220977783 time for calcul the mask position with numpy : 1.6069746017456055 nb_pixel_total : 6028953 time to create 1 rle with new method : 0.6773092746734619 time for calcul the mask position with numpy : 0.03138327598571777 nb_pixel_total : 15243 time to create 1 rle with old method : 0.017284393310546875 time for calcul the mask position with numpy : 0.03010249137878418 nb_pixel_total : 7831 time to create 1 rle with old method : 0.01021885871887207 time for calcul the mask position with numpy : 0.029712438583374023 nb_pixel_total : 22652 time to create 1 rle with old method : 0.025554418563842773 time for calcul the mask position with numpy : 0.030096769332885742 nb_pixel_total : 20240 time to create 1 rle with old method : 0.02465963363647461 time for calcul the mask position with numpy : 0.030164241790771484 nb_pixel_total : 66635 time to create 1 rle with old method : 0.07492852210998535 time for calcul the mask position with numpy : 0.029636383056640625 nb_pixel_total : 45618 time to create 1 rle with old method : 0.052497148513793945 time for calcul the mask position with numpy : 0.02947258949279785 nb_pixel_total : 71850 time to create 1 rle with old method : 0.08248591423034668 time for calcul the mask position with numpy : 0.029610395431518555 nb_pixel_total : 29427 time to create 1 rle with old method : 0.0340275764465332 time for calcul the mask position with numpy : 0.029146909713745117 nb_pixel_total : 1494 time to create 1 rle with old method : 0.0018587112426757812 time for calcul the mask position with numpy : 0.029390335083007812 nb_pixel_total : 47492 time to create 1 rle with old method : 0.0634305477142334 time for calcul the mask position with numpy : 0.029896259307861328 nb_pixel_total : 37557 time to create 1 rle with old method : 0.04668545722961426 time for calcul the mask position with numpy : 0.02940845489501953 nb_pixel_total : 48954 time to create 1 rle with old method : 0.058171987533569336 time for calcul the mask position with numpy : 0.031658172607421875 nb_pixel_total : 17666 time to create 1 rle with old method : 0.021776199340820312 time for calcul the mask position with numpy : 0.03107309341430664 nb_pixel_total : 109927 time to create 1 rle with old method : 0.1496114730834961 time for calcul the mask position with numpy : 0.030204057693481445 nb_pixel_total : 11879 time to create 1 rle with old method : 0.013818025588989258 time for calcul the mask position with numpy : 0.03043818473815918 nb_pixel_total : 5209 time to create 1 rle with old method : 0.006758689880371094 time for calcul the mask position with numpy : 0.03072047233581543 nb_pixel_total : 96568 time to create 1 rle with old method : 0.1113741397857666 time for calcul the mask position with numpy : 0.030043601989746094 nb_pixel_total : 21782 time to create 1 rle with old method : 0.03004002571105957 time for calcul the mask position with numpy : 0.031986236572265625 nb_pixel_total : 139719 time to create 1 rle with old method : 0.15736651420593262 time for calcul the mask position with numpy : 0.02936720848083496 nb_pixel_total : 17934 time to create 1 rle with old method : 0.021068811416625977 time for calcul the mask position with numpy : 0.03130459785461426 nb_pixel_total : 81321 time to create 1 rle with old method : 0.09631562232971191 time for calcul the mask position with numpy : 0.02976822853088379 nb_pixel_total : 6972 time to create 1 rle with old method : 0.009122133255004883 time for calcul the mask position with numpy : 0.03070354461669922 nb_pixel_total : 43018 time to create 1 rle with old method : 0.04744744300842285 time for calcul the mask position with numpy : 0.03499412536621094 nb_pixel_total : 6340 time to create 1 rle with old method : 0.007300376892089844 time for calcul the mask position with numpy : 0.02957749366760254 nb_pixel_total : 12591 time to create 1 rle with old method : 0.01420140266418457 time for calcul the mask position with numpy : 0.02912139892578125 nb_pixel_total : 16855 time to create 1 rle with old method : 0.01863265037536621 time for calcul the mask position with numpy : 0.029073476791381836 nb_pixel_total : 16795 time to create 1 rle with old method : 0.01873040199279785 time for calcul the mask position with numpy : 0.029285907745361328 nb_pixel_total : 1718 time to create 1 rle with old method : 0.0024712085723876953 create new chi : 4.388000965118408 time to delete rle : 0.002545595169067383 batch 1 Loaded 57 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16736 TO DO : save crop sub photo not yet done ! save time : 1.353032112121582 map_output_result : {1350740015: (0.0, 'Should be the crop_list due to order', 0), 1350740010: (0.0, 'Should be the crop_list due to order', 0), 1350739831: (0.0, 'Should be the crop_list due to order', 0), 1350739798: (0.0, 'Should be the crop_list due to order', 0), 1350739779: (0.0, 'Should be the crop_list due to order', 0), 1350739530: (0.0, 'Should be the crop_list due to order', 0), 1350739299: (0.0, 'Should be the crop_list due to order', 0), 1350739216: (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 [1350740015, 1350740010, 1350739831, 1350739798, 1350739779, 1350739530, 1350739299, 1350739216] Looping around the photos to save general results len do output : 8 /1350740015.Didn't retrieve data . /1350740010.Didn't retrieve data . /1350739831.Didn't retrieve data . /1350739798.Didn't retrieve data . /1350739779.Didn't retrieve data . /1350739530.Didn't retrieve data . /1350739299.Didn't retrieve data . /1350739216.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, '2734297') ('3318', '22161075', '1350740015', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350740010', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739831', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739798', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739779', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739530', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739299', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739216', None, None, None, None, None, '2734297') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.01584649085998535 save_final save missing photos in datou_result : time spend for datou_step_exec : 111.52772784233093 time spend to save output : 0.016292572021484375 total time spend for step 3 : 111.54402041435242 step4:ventilate_hashtags_in_portfolio Wed Apr 9 12:26:19 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 : 22161075 get user id for portfolio 22161075 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 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','autre','mal_croppe','carton','environnement','flou','metal','background','pet_clair','pet_fonce','pehd')) 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`=22161075 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','autre','mal_croppe','carton','environnement','flou','metal','background','pet_clair','pet_fonce','pehd')) 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`=22161075 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','autre','mal_croppe','carton','environnement','flou','metal','background','pet_clair','pet_fonce','pehd')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22161107,22161108,22161109,22161110,22161111,22161112,22161113,22161114,22161115,22161116,22161117?tags=papier,autre,mal_croppe,carton,environnement,flou,metal,background,pet_clair,pet_fonce,pehd Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350740015, 1350740010, 1350739831, 1350739798, 1350739779, 1350739530, 1350739299, 1350739216] Looping around the photos to save general results len do output : 1 /22161075. 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, '2734297') ('3318', '22161075', '1350740015', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350740010', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739831', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739798', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739779', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739530', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739299', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739216', None, None, None, None, None, '2734297') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.013731718063354492 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.2091429233551025 time spend to save output : 0.013982772827148438 total time spend for step 4 : 2.223125696182251 step5:final Wed Apr 9 12:26:21 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 : {1350740015: ('0.22379874656749282',), 1350740010: ('0.22379874656749282',), 1350739831: ('0.22379874656749282',), 1350739798: ('0.22379874656749282',), 1350739779: ('0.22379874656749282',), 1350739530: ('0.22379874656749282',), 1350739299: ('0.22379874656749282',), 1350739216: ('0.22379874656749282',)} new output for save of step final : {1350740015: ('0.22379874656749282',), 1350740010: ('0.22379874656749282',), 1350739831: ('0.22379874656749282',), 1350739798: ('0.22379874656749282',), 1350739779: ('0.22379874656749282',), 1350739530: ('0.22379874656749282',), 1350739299: ('0.22379874656749282',), 1350739216: ('0.22379874656749282',)} [1350740015, 1350740010, 1350739831, 1350739798, 1350739779, 1350739530, 1350739299, 1350739216] Looping around the photos to save general results len do output : 8 /1350740015.Didn't retrieve data . /1350740010.Didn't retrieve data . /1350739831.Didn't retrieve data . /1350739798.Didn't retrieve data . /1350739779.Didn't retrieve data . /1350739530.Didn't retrieve data . /1350739299.Didn't retrieve data . /1350739216.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, '2734297') ('3318', '22161075', '1350740015', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350740010', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739831', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739798', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739779', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739530', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739299', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739216', None, None, None, None, None, '2734297') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.012796640396118164 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.14430546760559082 time spend to save output : 0.013229131698608398 total time spend for step 5 : 0.15753459930419922 step6:blur_detection Wed Apr 9 12:26:21 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/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3.jpg resize: (2160, 3264) 1350740015 -3.003825846190097 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167.jpg resize: (2160, 3264) 1350740010 -4.962107124983854 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994.jpg resize: (2160, 3264) 1350739831 -3.527654692240668 treat image : temp/1744194029_945173_1350739798_2ec57efe57e48d81377d9d134b0c2806.jpg resize: (2160, 3264) 1350739798 -5.087776269670823 treat image : temp/1744194029_945173_1350739779_11f7ad0a92300d96dc67fc3cda3d6f79.jpg resize: (2160, 3264) 1350739779 -3.8898282849703034 treat image : temp/1744194029_945173_1350739530_908726047a5015ce139fa0cb905fd2ec.jpg resize: (2160, 3264) 1350739530 -6.607251481730466 treat image : temp/1744194029_945173_1350739299_5564dbca74abb32bee61c32b63fb1c3f.jpg resize: (2160, 3264) 1350739299 -4.590354718109789 treat image : temp/1744194029_945173_1350739216_c1a6cad42cdb5e5b51c84f9dc81e383e.jpg resize: (2160, 3264) 1350739216 -6.627987668358766 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411111_0.png resize: (259, 201) 1350753404 -1.6283845174727574 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411116_0.png resize: (170, 194) 1350753405 -3.0329025381346306 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411121_0.png resize: (326, 452) 1350753406 -3.532805392059289 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411118_0.png resize: (194, 103) 1350753407 -1.9822951771851405 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411117_0.png resize: (243, 170) 1350753408 -2.200005137695848 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411105_0.png resize: (169, 236) 1350753409 -1.6728186340910303 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411110_0.png resize: (140, 268) 1350753410 -2.590774152425189 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411120_0.png resize: (282, 406) 1350753411 0.5143514419475979 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411113_0.png resize: (121, 161) 1350753412 -2.066794023189555 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411107_0.png resize: (368, 341) 1350753413 -0.697711768572981 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411119_0.png resize: (448, 242) 1350753414 -0.8103346881387573 treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411109_0.png resize: (252, 121) 1350753415 -2.3493347427086104 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411142_0.png resize: (243, 367) 1350753416 -2.8518233602766427 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411130_0.png resize: (349, 570) 1350753417 -3.520964218603688 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411138_0.png resize: (188, 105) 1350753418 -2.4856572337996825 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411129_0.png resize: (443, 219) 1350753419 -1.2026758741315502 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411145_0.png resize: (184, 76) 1350753420 -2.3991746527208107 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411132_0.png resize: (159, 242) 1350753421 -2.495606939602481 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411149_0.png resize: (134, 251) 1350753422 -3.3691288073881736 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411131_0.png resize: (900, 497) 1350753423 -3.8861647505460613 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411126_0.png resize: (361, 339) 1350753424 -2.4762648431417644 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411147_0.png resize: (525, 415) 1350753425 -2.6967145193948694 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411128_0.png resize: (312, 247) 1350753426 -2.697384882785904 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411144_0.png resize: (223, 432) 1350753427 -2.3632819380360806 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411141_0.png resize: (130, 119) 1350753428 -2.849646643617214 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411146_0.png resize: (390, 212) 1350753429 -1.9698297571745047 treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167_rle_crop_3751411135_0.png resize: (242, 113) 1350753430 -3.5424916384790994 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411152_0.png resize: (76, 103) 1350753431 -0.7613283924020287 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411175_0.png resize: (189, 243) 1350753432 -3.028364088140733 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411172_0.png resize: (142, 142) 1350753433 -4.170010233044214 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411167_0.png resize: (135, 147) 1350753434 -0.694737437196972 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411155_0.png resize: (346, 390) 1350753435 -3.0223378725080856 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411151_0.png resize: (365, 463) 1350753436 -0.9800499696124202 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411165_0.png resize: (176, 152) 1350753437 -1.8295033315868863 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411164_0.png resize: (109, 278) 1350753438 -2.266321286828592 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411162_0.png resize: (210, 372) 1350753439 -0.7810968458953393 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411168_0.png resize: (105, 194) 1350753440 -3.551209577131793 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411154_0.png resize: (254, 121) 1350753441 -2.0389306646262546 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411158_0.png resize: (161, 157) 1350753442 -1.2170125954290925 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411163_0.png resize: (348, 182) 1350753443 -2.7921649894794185 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411159_0.png resize: (290, 125) 1350753444 -2.104821995337513 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411171_0.png resize: (648, 361) 1350753445 -1.9333247110418965 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411174_0.png resize: (155, 208) 1350753446 -2.497104643361124 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411166_0.png resize: (164, 254) 1350753447 -3.2428621437319802 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411157_0.png resize: (153, 144) 1350753448 -1.4769416491552716 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411150_0.png resize: (156, 225) 1350753449 -1.0679684581126416 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411153_0.png resize: (215, 218) 1350753450 -2.0716510977455935 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411161_0.png resize: (131, 81) 1350753451 -0.08335840749860265 treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411156_0.png resize: (463, 155) 1350753452 -2.137675090737292 treat image : temp/1744194029_945173_1350739798_2ec57efe57e48d81377d9d134b0c2806_rle_crop_3751411197_0.png resize: (269, 149) 1350753453 -1.7520238766584832 treat image : temp/1744194029_945173_1350739798_2ec57efe57e48d81377d9d134b0c2806_rle_crop_3751411200_0.png resize: (317, 529) 1350753454 -2.3494885985525857 treat image : temp/1744194029_945173_1350739798_2ec57efe57e48d81377d9d134b0c2806_rle_crop_3751411202_0.png resize: (97, 244) 1350753455 -3.012452799375019 treat image : temp/1744194029_945173_1350739798_2ec57efe57e48d81377d9d134b0c2806_rle_crop_3751411183_0.png resize: (235, 185) 1350753456 -2.213819015961772 treat image : 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38.836334228515625 step7:brightness Wed Apr 9 12:27:00 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 calcul brightness treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3.jpg treat image : temp/1744194029_945173_1350740010_6a797b89c28efed7963b8a3ea333a167.jpg treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994.jpg treat image : temp/1744194029_945173_1350739798_2ec57efe57e48d81377d9d134b0c2806.jpg treat image : temp/1744194029_945173_1350739779_11f7ad0a92300d96dc67fc3cda3d6f79.jpg treat image : temp/1744194029_945173_1350739530_908726047a5015ce139fa0cb905fd2ec.jpg treat image : temp/1744194029_945173_1350739299_5564dbca74abb32bee61c32b63fb1c3f.jpg treat image : temp/1744194029_945173_1350739216_c1a6cad42cdb5e5b51c84f9dc81e383e.jpg treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411111_0.png treat image : temp/1744194029_945173_1350740015_ad4605b2977c4132c8ef6d1ee356a0c3_rle_crop_3751411116_0.png treat 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temp/1744194029_945173_1350739530_908726047a5015ce139fa0cb905fd2ec_rle_crop_3751411251_0.png treat image : temp/1744194029_945173_1350739299_5564dbca74abb32bee61c32b63fb1c3f_rle_crop_3751411282_0.png treat image : temp/1744194029_945173_1350739299_5564dbca74abb32bee61c32b63fb1c3f_rle_crop_3751411279_0.png treat image : temp/1744194029_945173_1350739216_c1a6cad42cdb5e5b51c84f9dc81e383e_rle_crop_3751411326_0.png treat image : temp/1744194029_945173_1350739831_da0794231dbc0951285401b3b51fe994_rle_crop_3751411169_0.png treat image : temp/1744194029_945173_1350739216_c1a6cad42cdb5e5b51c84f9dc81e383e_rle_crop_3751411303_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 : 232 time used for this insertion : 0.019547700881958008 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 232 time used for this insertion : 0.045513153076171875 save missing photos in datou_result : time spend for datou_step_exec : 9.058562755584717 time spend to save output : 0.07054615020751953 total time spend for step 7 : 9.129108905792236 step8:velours_tree Wed Apr 9 12:27:09 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 : 0.18855977058410645 time spend to save output : 4.6253204345703125e-05 total time spend for step 8 : 0.18860602378845215 step9:send_mail_cod Wed Apr 9 12:27:09 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_P22161075_09-04-2025_12_27_09.pdf 22161107 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 .imagette221611071744194429 22161108 change filename to text .imagette221611081744194431 22161109 imagette221611091744194431 22161110 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 .imagette221611101744194431 22161112 imagette221611121744194432 22161113 change filename to text .imagette221611131744194432 22161114 imagette221611141744194432 22161115 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 .imagette221611151744194432 22161116 change filename to text .imagette221611161744194434 22161117 imagette221611171744194434 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22161075 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22161107,22161108,22161109,22161110,22161111,22161112,22161113,22161114,22161115,22161116,22161117?tags=papier,autre,mal_croppe,carton,environnement,flou,metal,background,pet_clair,pet_fonce,pehd args[1350740015] : ((1350740015, -3.003825846190097, 492609224), (1350740015, -0.1381632143843302, 496442774), '0.22379874656749282') We are sending mail with results at report@fotonower.com args[1350740010] : ((1350740010, -4.962107124983854, 492609224), (1350740010, -0.11492098776492742, 496442774), '0.22379874656749282') We are sending mail with results at report@fotonower.com args[1350739831] : ((1350739831, -3.527654692240668, 492609224), (1350739831, -0.1557671490255127, 496442774), '0.22379874656749282') We are sending mail with results at report@fotonower.com args[1350739798] : ((1350739798, -5.087776269670823, 492609224), (1350739798, -0.21837887023739172, 496442774), '0.22379874656749282') We are sending mail with results at report@fotonower.com args[1350739779] : ((1350739779, -3.8898282849703034, 492609224), (1350739779, -0.2943008116255984, 496442774), '0.22379874656749282') We are sending mail with results at report@fotonower.com args[1350739530] : ((1350739530, -6.607251481730466, 492609224), (1350739530, -0.022352946943107927, 2107752395), '0.22379874656749282') We are sending mail with results at report@fotonower.com args[1350739299] : ((1350739299, -4.590354718109789, 492609224), (1350739299, -0.027481869079396003, 2107752395), '0.22379874656749282') We are sending mail with results at report@fotonower.com args[1350739216] : ((1350739216, -6.627987668358766, 492609224), (1350739216, -0.1087991759830014, 496442774), '0.22379874656749282') We are sending mail with results at report@fotonower.com refus_total : 0.22379874656749282 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=22161075 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350739216,1350739299,1350739798,1350739530,1350739779,1350739831,1350740010,1350740015) Found this number of photos: 8 begin to download photo : 1350739216 begin to download photo : 1350739798 begin to download photo : 1350739779 begin to download photo : 1350740010 download finish for photo 1350739779 begin to download photo : 1350739831 download finish for photo 1350739798 begin to download photo : 1350739530 download finish for photo 1350739216 begin to download photo : 1350739299 download finish for photo 1350739530 download finish for photo 1350740010 begin to download photo : 1350740015 download finish for photo 1350739299 download finish for photo 1350739831 download finish for photo 1350740015 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161075_09-04-2025_12_27_09.pdf results_Auto_P22161075_09-04-2025_12_27_09.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161075_09-04-2025_12_27_09.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','22161075','results_Auto_P22161075_09-04-2025_12_27_09.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161075_09-04-2025_12_27_09.pdf','pdf','','0.85','0.22379874656749282') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22161075

https://www.fotonower.com/image?json=false&list_photos_id=1350740015
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350740010
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350739831
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350739798
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350739779
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350739530
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350739299
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350739216
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 22.38%
Veuillez trouver les photos des contaminants.

exemples de contaminants: papier: https://www.fotonower.com/view/22161107?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22161108?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22161110?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22161113?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22161115?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22161116?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161075_09-04-2025_12_27_09.pdf.

Lien vers velours :https://www.fotonower.com/velours/22161107,22161108,22161109,22161110,22161111,22161112,22161113,22161114,22161115,22161116,22161117?tags=papier,autre,mal_croppe,carton,environnement,flou,metal,background,pet_clair,pet_fonce,pehd.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 10:27:17 GMT Content-Length: 0 Connection: close X-Message-Id: 8obCgT0bSbSoX0awBgjCnA 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 [1350740015, 1350740010, 1350739831, 1350739798, 1350739779, 1350739530, 1350739299, 1350739216] 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, '2734297') ('3318', '22161075', '1350740015', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350740010', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739831', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739798', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739779', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739530', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739299', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739216', None, None, None, None, None, '2734297') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.01491689682006836 save_final save missing photos in datou_result : time spend for datou_step_exec : 7.595083236694336 time spend to save output : 0.0151519775390625 total time spend for step 9 : 7.610235214233398 step10:split_time_score Wed Apr 9 12:27:17 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'}] (('11', 8),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 09042025 22161075 Nombre de photos uploadées : 8 / 23040 (0%) 09042025 22161075 Nombre de photos taguées (types de déchets): 0 / 8 (0%) 09042025 22161075 Nombre de photos taguées (volume) : 0 / 8 (0%) elapsed_time : load_data_split_time_score 7.152557373046875e-07 elapsed_time : order_list_meta_photo_and_scores 6.198883056640625e-06 ???????? elapsed_time : fill_and_build_computed_from_old_data 0.00026869773864746094 elapsed_time : insert_dashboard_record_day_entry 0.02251458168029785 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161064 order by id desc limit 1 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 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161073 order by id desc limit 1 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 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'09042025': {'nb_upload': 8, '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 [1350740015, 1350740010, 1350739831, 1350739798, 1350739779, 1350739530, 1350739299, 1350739216] Looping around the photos to save general results len do output : 1 /22161075Didn'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, '2734297') ('3318', '22161075', '1350740015', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350740010', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739831', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739798', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739779', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739530', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739299', None, None, None, None, None, '2734297') ('3318', None, None, None, None, None, None, None, '2734297') ('3318', '22161075', '1350739216', None, None, None, None, None, '2734297') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.013408422470092773 save_final save missing photos in datou_result : time spend for datou_step_exec : 3.740563154220581 time spend to save output : 0.013666152954101562 total time spend for step 10 : 3.7542293071746826 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 8 set_done_treatment 205.48user 124.76system 6:54.98elapsed 79%CPU (0avgtext+0avgdata 5483320maxresident)k 744520inputs+184896outputs (8164major+16983975minor)pagefaults 0swaps