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 : 365925 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 : ['4072125'] with mtr_portfolio_ids : ['28685556'] and first list_photo_ids : [] new path : /proc/365925/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 35 ; length of list_pids : 35 ; length of list_args : 35 time to download the photos : 5.047463655471802 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Wed Nov 19 01:20:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 7216 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-11-19 01:20:37.101487: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-11-19 01:20:37.138548: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-11-19 01:20:37.141427: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f50e8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-11-19 01:20:37.141500: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-11-19 01:20:37.147160: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-11-19 01:20:37.398421: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x45ba8d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-11-19 01:20:37.398483: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-11-19 01:20:37.400622: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-11-19 01:20:37.401066: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-19 01:20:37.404478: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-19 01:20:37.424668: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-19 01:20:37.425174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-19 01:20:37.459060: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-19 01:20:37.463704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-19 01:20:37.523179: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-19 01:20:37.524928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-19 01:20:37.525437: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-19 01:20:37.526357: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-19 01:20:37.526378: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-19 01:20:37.526390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-19 01:20:37.528272: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6640 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-11-19 01:20:37.884642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-11-19 01:20:37.884762: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-19 01:20:37.884783: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-19 01:20:37.884802: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-19 01:20:37.884821: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-19 01:20:37.884839: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-19 01:20:37.884857: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-19 01:20:37.884876: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-19 01:20:37.886152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-19 01:20:37.887418: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-11-19 01:20:37.887451: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-19 01:20:37.887467: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-19 01:20:37.887481: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-19 01:20:37.887495: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-19 01:20:37.887509: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-19 01:20:37.887523: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-19 01:20:37.887536: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-19 01:20:37.888555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-19 01:20:37.888588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-19 01:20:37.888596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-19 01:20:37.888604: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-19 01:20:37.889631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6640 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-11-19 01:20:49.070086: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-19 01:20:49.324127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 35 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 30.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 36.00000 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: 1920.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 37.00000 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: 1920.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 37.00000 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: 1920.00000 nb d'objets trouves : 13 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 35.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 48.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 47.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 39.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 42.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 48.00000 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: 1920.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 46.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 37.00000 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: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 51.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 50.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 51.00000 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: 1920.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 38.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 35.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 47.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 22.00000 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: 1920.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 37.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 42.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 43.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 47.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 44.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 38.00000 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: 1920.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 40.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 37.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 40.00000 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: 1920.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 366480 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 734 tf kernel not reseted sub process len(results) : 35 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 35 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 : 6023 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 : 0.00020694732666015625 nb_pixel_total : 4641 time to create 1 rle with old method : 0.006414890289306641 length of segment : 89 time for calcul the mask position with numpy : 9.870529174804688e-05 nb_pixel_total : 2344 time to create 1 rle with old method : 0.0032563209533691406 length of segment : 59 time for calcul the mask position with numpy : 0.0002853870391845703 nb_pixel_total : 5073 time to create 1 rle with old method : 0.006890773773193359 length of segment : 100 time for calcul the mask position with numpy : 0.00016188621520996094 nb_pixel_total : 2565 time to create 1 rle with old method : 0.0035054683685302734 length of segment : 62 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 2045 time to create 1 rle with old method : 0.0028870105743408203 length of segment : 37 time for calcul the mask position with numpy : 0.0004286766052246094 nb_pixel_total : 24650 time to create 1 rle with old method : 0.03277158737182617 length of segment : 168 time for calcul the mask position with numpy : 0.00029158592224121094 nb_pixel_total : 4832 time to create 1 rle with old method : 0.006589651107788086 length of segment : 104 time for calcul the mask position with numpy : 0.0001010894775390625 nb_pixel_total : 2894 time to create 1 rle with old method : 0.003923654556274414 length of segment : 82 time for calcul the mask position with numpy : 0.0001728534698486328 nb_pixel_total : 5057 time to create 1 rle with old method : 0.0072095394134521484 length of segment : 93 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 1624 time to create 1 rle with old method : 0.0023632049560546875 length of segment : 44 time for calcul the mask position with numpy : 0.0001399517059326172 nb_pixel_total : 2325 time to create 1 rle with old method : 0.0033016204833984375 length of segment : 45 time for calcul the mask position with numpy : 0.0001690387725830078 nb_pixel_total : 5032 time to create 1 rle with old method : 0.007135152816772461 length of segment : 45 time for calcul the mask position with numpy : 0.0022192001342773438 nb_pixel_total : 93610 time to create 1 rle with old method : 0.12098193168640137 length of segment : 496 time for calcul the mask position with numpy : 0.00022673606872558594 nb_pixel_total : 5399 time to create 1 rle with old method : 0.007376909255981445 length of segment : 68 time for calcul the mask position with numpy : 0.0001857280731201172 nb_pixel_total : 3825 time to create 1 rle with old method : 0.005242824554443359 length of segment : 71 time for calcul the mask position with numpy : 0.00015592575073242188 nb_pixel_total : 2732 time to create 1 rle with old method : 0.003993034362792969 length of segment : 58 time for calcul the mask position with numpy : 0.00015234947204589844 nb_pixel_total : 2896 time to create 1 rle with old method : 0.0043833255767822266 length of segment : 49 time for calcul the mask position with numpy : 0.00025725364685058594 nb_pixel_total : 5598 time to create 1 rle with old method : 0.007869958877563477 length of segment : 105 time for calcul the mask position with numpy : 0.0002923011779785156 nb_pixel_total : 7292 time to create 1 rle with old method : 0.009904146194458008 length of segment : 100 time for calcul the mask position with numpy : 0.0002529621124267578 nb_pixel_total : 4595 time to create 1 rle with old method : 0.006151437759399414 length of segment : 111 time for calcul the mask position with numpy : 0.0002510547637939453 nb_pixel_total : 5947 time to create 1 rle with old method : 0.00800180435180664 length of segment : 129 time for calcul the mask position with numpy : 0.00013899803161621094 nb_pixel_total : 3538 time to create 1 rle with old method : 0.0048809051513671875 length of segment : 56 time for calcul the mask position with numpy : 0.0003178119659423828 nb_pixel_total : 5975 time to create 1 rle with old method : 0.007966756820678711 length of segment : 169 time for calcul the mask position with numpy : 0.0002474784851074219 nb_pixel_total : 13292 time to create 1 rle with old method : 0.0175015926361084 length of segment : 155 time for calcul the mask position with numpy : 0.0018529891967773438 nb_pixel_total : 111008 time to create 1 rle with old method : 0.14191842079162598 length of segment : 523 time for calcul the mask position with numpy : 0.00025153160095214844 nb_pixel_total : 6986 time to create 1 rle with old method : 0.009673118591308594 length of segment : 88 time for calcul the mask position with numpy : 0.0001614093780517578 nb_pixel_total : 2507 time to create 1 rle with old method : 0.003480672836303711 length of segment : 125 time for calcul the mask position with numpy : 0.0002155303955078125 nb_pixel_total : 10530 time to create 1 rle with old method : 0.014307737350463867 length of segment : 153 time for calcul the mask position with numpy : 0.0018849372863769531 nb_pixel_total : 94602 time to create 1 rle with old method : 0.12329721450805664 length of segment : 524 time for calcul the mask position with numpy : 0.00017714500427246094 nb_pixel_total : 4161 time to create 1 rle with old method : 0.0056171417236328125 length of segment : 110 time for calcul the mask position with numpy : 0.0002536773681640625 nb_pixel_total : 7945 time to create 1 rle with old method : 0.010956048965454102 length of segment : 154 time for calcul the mask position with numpy : 0.00022172927856445312 nb_pixel_total : 6211 time to create 1 rle with old method : 0.008107900619506836 length of segment : 229 time for calcul the mask position with numpy : 0.00015091896057128906 nb_pixel_total : 4818 time to create 1 rle with old method : 0.0068585872650146484 length of segment : 79 time for calcul the mask position with numpy : 0.0002231597900390625 nb_pixel_total : 8462 time to create 1 rle with old method : 0.01143646240234375 length of segment : 129 time for calcul the mask position with numpy : 0.00013828277587890625 nb_pixel_total : 3985 time to create 1 rle with old method : 0.005540132522583008 length of segment : 75 time for calcul the mask position with numpy : 0.0003025531768798828 nb_pixel_total : 7796 time to create 1 rle with old method : 0.010679960250854492 length of segment : 118 time for calcul the mask position with numpy : 0.0001380443572998047 nb_pixel_total : 3068 time to create 1 rle with old method : 0.004327297210693359 length of segment : 46 time for calcul the mask position with numpy : 0.00010943412780761719 nb_pixel_total : 4499 time to create 1 rle with old method : 0.006272554397583008 length of segment : 70 time for calcul the mask position with numpy : 0.0001068115234375 nb_pixel_total : 3871 time to create 1 rle with old method : 0.005417585372924805 length of segment : 59 time for calcul the mask position with numpy : 0.00027179718017578125 nb_pixel_total : 3884 time to create 1 rle with old method : 0.0053250789642333984 length of segment : 127 time for calcul the mask position with numpy : 0.0016415119171142578 nb_pixel_total : 98860 time to create 1 rle with old method : 0.14984464645385742 length of segment : 530 time for calcul the mask position with numpy : 0.00026226043701171875 nb_pixel_total : 4626 time to create 1 rle with old method : 0.006882905960083008 length of segment : 95 time for calcul the mask position with numpy : 0.0007531642913818359 nb_pixel_total : 20173 time to create 1 rle with old method : 0.036420583724975586 length of segment : 222 time for calcul the mask position with numpy : 0.0002503395080566406 nb_pixel_total : 5352 time to create 1 rle with old method : 0.007281780242919922 length of segment : 81 time for calcul the mask position with numpy : 0.00027060508728027344 nb_pixel_total : 5570 time to create 1 rle with old method : 0.007655620574951172 length of segment : 128 time for calcul the mask position with numpy : 0.00021076202392578125 nb_pixel_total : 3931 time to create 1 rle with old method : 0.005371570587158203 length of segment : 73 time for calcul the mask position with numpy : 0.0009009838104248047 nb_pixel_total : 31697 time to create 1 rle with old method : 0.04193115234375 length of segment : 311 time for calcul the mask position with numpy : 0.0005578994750976562 nb_pixel_total : 14512 time to create 1 rle with old method : 0.019672155380249023 length of segment : 165 time for calcul the mask position with numpy : 0.0021207332611083984 nb_pixel_total : 106726 time to create 1 rle with old method : 0.1374373435974121 length of segment : 521 time for calcul the mask position with numpy : 0.0007295608520507812 nb_pixel_total : 35239 time to create 1 rle with old method : 0.04712986946105957 length of segment : 179 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 2075 time to create 1 rle with old method : 0.002827882766723633 length of segment : 51 time for calcul the mask position with numpy : 0.0001659393310546875 nb_pixel_total : 6089 time to create 1 rle with old method : 0.008453369140625 length of segment : 111 time for calcul the mask position with numpy : 0.00011539459228515625 nb_pixel_total : 3049 time to create 1 rle with old method : 0.004975557327270508 length of segment : 45 time for calcul the mask position with numpy : 0.00021338462829589844 nb_pixel_total : 5246 time to create 1 rle with old method : 0.010068655014038086 length of segment : 104 time for calcul the mask position with numpy : 0.00018477439880371094 nb_pixel_total : 7026 time to create 1 rle with old method : 0.013060808181762695 length of segment : 125 time for calcul the mask position with numpy : 0.0002465248107910156 nb_pixel_total : 8762 time to create 1 rle with old method : 0.01632547378540039 length of segment : 86 time for calcul the mask position with numpy : 0.00013709068298339844 nb_pixel_total : 4759 time to create 1 rle with old method : 0.009051084518432617 length of segment : 82 time for calcul the mask position with numpy : 0.00019240379333496094 nb_pixel_total : 3697 time to create 1 rle with old method : 0.005151271820068359 length of segment : 69 time for calcul the mask position with numpy : 0.002269744873046875 nb_pixel_total : 105144 time to create 1 rle with old method : 0.13494873046875 length of segment : 529 time for calcul the mask position with numpy : 0.00035452842712402344 nb_pixel_total : 7793 time to create 1 rle with old method : 0.010536670684814453 length of segment : 173 time for calcul the mask position with numpy : 0.00025200843811035156 nb_pixel_total : 6318 time to create 1 rle with old method : 0.008542776107788086 length of segment : 88 time for calcul the mask position with numpy : 0.00024628639221191406 nb_pixel_total : 4627 time to create 1 rle with old method : 0.0062563419342041016 length of segment : 104 time for calcul the mask position with numpy : 0.000240325927734375 nb_pixel_total : 5954 time to create 1 rle with old method : 0.008053064346313477 length of segment : 80 time for calcul the mask position with numpy : 0.0005030632019042969 nb_pixel_total : 11257 time to create 1 rle with old method : 0.014912605285644531 length of segment : 217 time for calcul the mask position with numpy : 0.0003426074981689453 nb_pixel_total : 8019 time to create 1 rle with old method : 0.01079249382019043 length of segment : 137 time for calcul the mask position with numpy : 0.00011730194091796875 nb_pixel_total : 3890 time to create 1 rle with old method : 0.0053253173828125 length of segment : 89 time for calcul the mask position with numpy : 0.00035762786865234375 nb_pixel_total : 23984 time to create 1 rle with old method : 0.032950401306152344 length of segment : 176 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 1409 time to create 1 rle with old method : 0.0019774436950683594 length of segment : 70 time for calcul the mask position with numpy : 0.0001506805419921875 nb_pixel_total : 6325 time to create 1 rle with old method : 0.008585214614868164 length of segment : 170 time for calcul the mask position with numpy : 0.0016629695892333984 nb_pixel_total : 115397 time to create 1 rle with old method : 0.15172648429870605 length of segment : 534 time for calcul the mask position with numpy : 0.00018262863159179688 nb_pixel_total : 7527 time to create 1 rle with old method : 0.010083675384521484 length of segment : 120 time for calcul the mask position with numpy : 0.00017118453979492188 nb_pixel_total : 6629 time to create 1 rle with old method : 0.008983850479125977 length of segment : 181 time for calcul the mask position with numpy : 0.0017743110656738281 nb_pixel_total : 108258 time to create 1 rle with old method : 0.14465761184692383 length of segment : 523 time for calcul the mask position with numpy : 0.0002613067626953125 nb_pixel_total : 11078 time to create 1 rle with old method : 0.017329692840576172 length of segment : 138 time for calcul the mask position with numpy : 0.00015354156494140625 nb_pixel_total : 5714 time to create 1 rle with old method : 0.008153438568115234 length of segment : 94 time for calcul the mask position with numpy : 0.0016741752624511719 nb_pixel_total : 116053 time to create 1 rle with old method : 0.1533031463623047 length of segment : 536 time for calcul the mask position with numpy : 0.0002593994140625 nb_pixel_total : 2855 time to create 1 rle with old method : 0.004079103469848633 length of segment : 65 time for calcul the mask position with numpy : 0.0005657672882080078 nb_pixel_total : 6343 time to create 1 rle with old method : 0.009483814239501953 length of segment : 104 time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 6633 time to create 1 rle with old method : 0.011162519454956055 length of segment : 117 time for calcul the mask position with numpy : 0.00043082237243652344 nb_pixel_total : 5216 time to create 1 rle with old method : 0.006588459014892578 length of segment : 106 time for calcul the mask position with numpy : 0.0004968643188476562 nb_pixel_total : 10894 time to create 1 rle with old method : 0.013361930847167969 length of segment : 116 time for calcul the mask position with numpy : 0.0002803802490234375 nb_pixel_total : 3395 time to create 1 rle with old method : 0.0047855377197265625 length of segment : 87 time for calcul the mask position with numpy : 0.00025653839111328125 nb_pixel_total : 5696 time to create 1 rle with old method : 0.007967472076416016 length of segment : 75 time for calcul the mask position with numpy : 0.0003676414489746094 nb_pixel_total : 8242 time to create 1 rle with old method : 0.011028528213500977 length of segment : 115 time for calcul the mask position with numpy : 0.0008771419525146484 nb_pixel_total : 30643 time to create 1 rle with old method : 0.04109525680541992 length of segment : 174 time for calcul the mask position with numpy : 0.00035834312438964844 nb_pixel_total : 8313 time to create 1 rle with old method : 0.0111846923828125 length of segment : 104 time for calcul the mask position with numpy : 0.0001785755157470703 nb_pixel_total : 7354 time to create 1 rle with old method : 0.009989500045776367 length of segment : 102 time for calcul the mask position with numpy : 0.00029850006103515625 nb_pixel_total : 5889 time to create 1 rle with old method : 0.00806570053100586 length of segment : 112 time for calcul the mask position with numpy : 0.00014781951904296875 nb_pixel_total : 2602 time to create 1 rle with old method : 0.003615856170654297 length of segment : 50 time for calcul the mask position with numpy : 0.0001804828643798828 nb_pixel_total : 5381 time to create 1 rle with old method : 0.007433414459228516 length of segment : 75 time for calcul the mask position with numpy : 0.00027298927307128906 nb_pixel_total : 7419 time to create 1 rle with old method : 0.01020669937133789 length of segment : 95 time spent for convertir_results : 3.9742658138275146 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 91 chid ids of type : 3594 Number RLEs to save : 13538 save missing photos in datou_result : time spend for datou_step_exec : 44.424832582473755 time spend to save output : 0.888554573059082 total time spend for step 1 : 45.31338715553284 step2:crop_condition Wed Nov 19 01:21:18 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 : 35 ! batch 1 Loaded 91 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 ! map_result returned by crop_photo_return_map_crop : length : 58 About to insert : list_path_to_insert length 58 new photo from crops ! About to upload 58 photos upload in portfolio : 3736932 init cache_photo without model_param we have 58 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763511681_365925 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085776_c8d0b8ab13da7f4fc7c62ed7c52d1147_rle_crop_4038198783_0.png', 0, 105, 89, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085776_c8d0b8ab13da7f4fc7c62ed7c52d1147_rle_crop_4038198784_0.png', 0, 66, 59, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085277_4542b51045871966394fa6af6b66886f_rle_crop_4038198785_0.png', 0, 73, 100, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085277_4542b51045871966394fa6af6b66886f_rle_crop_4038198786_0.png', 0, 55, 61, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198787_0.png', 0, 63, 37, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198789_0.png', 0, 70, 104, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198792_0.png', 0, 50, 44, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085270_659240d6cd6ba4ba7ed946f15f1af8c9_rle_crop_4038198793_0.png', 0, 64, 45, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084955_deefde9f7d9a6e89d29b9820a9129e58_rle_crop_4038198796_0.png', 0, 105, 67, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084939_d0044eb36bfbf91216d1b0f1e53c2a45_rle_crop_4038198797_0.png', 0, 84, 70, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084917_5ce81debc948df4518a3017903e5cabd_rle_crop_4038198798_0.png', 0, 72, 58, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198799_0.png', 0, 69, 48, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198800_0.png', 0, 103, 104, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198802_0.png', 0, 74, 110, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198803_0.png', 0, 67, 129, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084910_8a7eab539f225850acf5ed9b573a368f_rle_crop_4038198804_0.png', 0, 80, 54, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084910_8a7eab539f225850acf5ed9b573a368f_rle_crop_4038198805_0.png', 0, 75, 168, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198809_0.png', 0, 50, 124, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198810_0.png', 0, 93, 148, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198812_0.png', 0, 69, 110, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198813_0.png', 0, 140, 152, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198814_0.png', 0, 44, 229, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198815_0.png', 0, 106, 74, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198816_0.png', 0, 99, 127, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198817_0.png', 0, 88, 75, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083745_03ec8670a614fccccca74b26be90e0a3_rle_crop_4038198820_0.png', 0, 107, 70, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083745_03ec8670a614fccccca74b26be90e0a3_rle_crop_4038198821_0.png', 0, 106, 56, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198824_0.png', 0, 89, 95, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198827_0.png', 0, 65, 120, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198828_0.png', 0, 71, 73, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198832_0.png', 0, 368, 144, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198833_0.png', 0, 59, 50, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198834_0.png', 0, 114, 112, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198835_0.png', 0, 85, 43, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198836_0.png', 0, 109, 101, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198837_0.png', 0, 89, 122, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198838_0.png', 0, 142, 84, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083477_b6fe11f685f40d92848a079bce81cce0_rle_crop_4038198840_0.png', 0, 82, 67, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083474_ca6789a7f667ed21801c3f51028e6b62_rle_crop_4038198842_0.png', 0, 78, 173, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083456_9cad6d3dea1416e8f69cdd8ad9b6b0fd_rle_crop_4038198843_0.png', 0, 120, 88, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083456_9cad6d3dea1416e8f69cdd8ad9b6b0fd_rle_crop_4038198844_0.png', 0, 57, 104, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083456_9cad6d3dea1416e8f69cdd8ad9b6b0fd_rle_crop_4038198845_0.png', 0, 102, 80, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083436_3691e1837407fcaaad22a1bdd1812585_rle_crop_4038198846_0.png', 0, 113, 200, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083433_4873cd265f69414c0a9b98f5da7df1c0_rle_crop_4038198848_0.png', 0, 75, 84, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083430_eb22b2a3b9f9c8fff756c607b9f9d7d0_rle_crop_4038198851_0.png', 0, 72, 161, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083427_bcfeb55e087a6b1fff385bb7d0f21e2a_rle_crop_4038198854_0.png', 0, 69, 147, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083427_bcfeb55e087a6b1fff385bb7d0f21e2a_rle_crop_4038198856_0.png', 0, 128, 134, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083383_69fbd6e7adbe4ea8508d0e1929734642_rle_crop_4038198860_0.png', 0, 132, 104, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083355_55df7c0b9d0b6a41c1b36d1cf864f97c_rle_crop_4038198861_0.png', 0, 78, 116, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083355_55df7c0b9d0b6a41c1b36d1cf864f97c_rle_crop_4038198862_0.png', 0, 86, 105, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083349_044ef3619deaa12087872dd285f259a3_rle_crop_4038198864_0.png', 0, 50, 79, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198866_0.png', 0, 143, 115, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198868_0.png', 0, 112, 104, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198869_0.png', 0, 90, 102, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63_rle_crop_4038198870_0.png', 0, 89, 112, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63_rle_crop_4038198871_0.png', 0, 56, 49, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63_rle_crop_4038198872_0.png', 0, 100, 75, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511692), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63_rle_crop_4038198873_0.png', 0, 148, 92, 0, 1763511692,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 58 photos in the portfolio 3736932 time of upload the photos Elapsed time : 13.442536115646362 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 ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763511696_365925 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511697), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198790_0.png', 0, 48, 82, 0, 1763511697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511697), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198791_0.png', 0, 89, 93, 0, 1763511697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511697), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198801_0.png', 0, 106, 98, 0, 1763511697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511697), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083676_8eae01e12edf6429d1a68d82bf1ba44d_rle_crop_4038198823_0.png', 0, 314, 527, 0, 1763511697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511697), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083430_eb22b2a3b9f9c8fff756c607b9f9d7d0_rle_crop_4038198853_0.png', 0, 106, 99, 0, 1763511697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511697), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083425_1fdc89ebef2fe4a809ab5c833219c462_rle_crop_4038198857_0.png', 0, 78, 94, 0, 1763511697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511697), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083425_1fdc89ebef2fe4a809ab5c833219c462_rle_crop_4038198859_0.png', 0, 67, 65, 0, 1763511697,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.913245677947998 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763511699_365925 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511699), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198826_0.png', 0, 81, 81, 0, 1763511699,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511699), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083355_55df7c0b9d0b6a41c1b36d1cf864f97c_rle_crop_4038198863_0.png', 0, 122, 114, 0, 1763511699,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.9504506587982178 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 ! map_result returned by crop_photo_return_map_crop : length : 22 About to insert : list_path_to_insert length 22 new photo from crops ! About to upload 22 photos upload in portfolio : 3736932 init cache_photo without model_param we have 22 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763511705_365925 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198788_0.png', 0, 184, 165, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085270_659240d6cd6ba4ba7ed946f15f1af8c9_rle_crop_4038198794_0.png', 0, 148, 45, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395085270_659240d6cd6ba4ba7ed946f15f1af8c9_rle_crop_4038198795_0.png', 0, 314, 494, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198806_0.png', 0, 111, 153, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198807_0.png', 0, 332, 520, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198808_0.png', 0, 193, 82, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084529_1d95ff0ffe5ac8882c538d253f40e41e_rle_crop_4038198811_0.png', 0, 343, 517, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395084295_4bcc09700007338f10580143c21b8762_rle_crop_4038198818_0.png', 0, 117, 117, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083881_0c22a38fd50dfaf9fb273de1bef3aaf9_rle_crop_4038198819_0.png', 0, 91, 46, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198825_0.png', 0, 210, 184, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198830_0.png', 0, 211, 133, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198831_0.png', 0, 337, 521, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083477_b6fe11f685f40d92848a079bce81cce0_rle_crop_4038198839_0.png', 0, 80, 82, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083477_b6fe11f685f40d92848a079bce81cce0_rle_crop_4038198841_0.png', 0, 327, 525, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083436_3691e1837407fcaaad22a1bdd1812585_rle_crop_4038198847_0.png', 0, 95, 137, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083433_4873cd265f69414c0a9b98f5da7df1c0_rle_crop_4038198849_0.png', 0, 175, 172, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083433_4873cd265f69414c0a9b98f5da7df1c0_rle_crop_4038198850_0.png', 0, 27, 69, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083430_eb22b2a3b9f9c8fff756c607b9f9d7d0_rle_crop_4038198852_0.png', 0, 351, 526, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083427_bcfeb55e087a6b1fff385bb7d0f21e2a_rle_crop_4038198855_0.png', 0, 342, 518, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083425_1fdc89ebef2fe4a809ab5c833219c462_rle_crop_4038198858_0.png', 0, 354, 525, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083349_044ef3619deaa12087872dd285f259a3_rle_crop_4038198865_0.png', 0, 103, 72, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511709), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198867_0.png', 0, 315, 170, 0, 1763511709,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 22 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.4298095703125 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763511711_365925 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511711), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083745_03ec8670a614fccccca74b26be90e0a3_rle_crop_4038198822_0.png', 0, 43, 127, 0, 1763511711,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1763511711), 0.0, 0.0, 14, '', 0, 0, '1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198829_0.png', 0, 160, 304, 0, 1763511711,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.7950229644775391 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 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles 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 [1395085776, 1395085278, 1395085277, 1395085274, 1395085270, 1395084955, 1395084939, 1395084917, 1395084913, 1395084910, 1395084905, 1395084529, 1395084497, 1395084295, 1395083881, 1395083810, 1395083745, 1395083676, 1395083617, 1395083554, 1395083486, 1395083477, 1395083474, 1395083456, 1395083455, 1395083436, 1395083433, 1395083430, 1395083427, 1395083425, 1395083383, 1395083355, 1395083349, 1395083337, 1395083304] Looping around the photos to save general results len do output : 91 /1395122124Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122125Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122126Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122131Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122134Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122135Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122139Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122141Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122143Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122147Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122152Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122153Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122154Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122157Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122159Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122160Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122161Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122163Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122164Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122165Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122166Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122167Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122168Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122169Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122170Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122173Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122174Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122175Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122177Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122178Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395122179Didn't retrieve data .Didn't retrieve 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output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085776', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085278', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085277', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085274', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085270', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084955', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084939', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084917', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084913', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084910', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084905', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084529', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084497', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084295', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083881', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083810', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083745', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083676', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083617', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083554', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083486', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083477', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083474', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083456', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083455', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083436', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083433', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083430', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083427', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083425', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083383', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083355', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083349', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083337', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083304', None, None, None, None, None, '4072125') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 308 time used for this insertion : 0.02837967872619629 save_final save missing photos in datou_result : time spend for datou_step_exec : 33.623910903930664 time spend to save output : 0.03202533721923828 total time spend for step 2 : 33.6559362411499 step3:rle_unique_nms_with_priority Wed Nov 19 01:21:52 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 91 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.3109562397003174 time for calcul the mask position with numpy : 0.32543134689331055 nb_pixel_total : 2066615 time to create 1 rle with new method : 0.34772443771362305 time for calcul the mask position with numpy : 0.0070378780364990234 nb_pixel_total : 2344 time to create 1 rle with old method : 0.003113985061645508 time for calcul the mask position with numpy : 0.0066378116607666016 nb_pixel_total : 4641 time to create 1 rle with old method : 0.006124973297119141 create new chi : 0.7081630229949951 time to delete rle : 0.0199129581451416 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1376 TO DO : save crop sub photo not yet done ! save time : 0.1327369213104248 No data in photo_id : 1395085278 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.0419621467590332 time for calcul the mask position with numpy : 0.0711827278137207 nb_pixel_total : 2065962 time to create 1 rle with new method : 0.08321118354797363 time for calcul the mask position with numpy : 0.006432771682739258 nb_pixel_total : 2565 time to create 1 rle with old method : 0.003384113311767578 time for calcul the mask position with numpy : 0.006041526794433594 nb_pixel_total : 5073 time to create 1 rle with old method : 0.006623983383178711 create new chi : 0.1874985694885254 time to delete rle : 0.00024175643920898438 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1404 TO DO : save crop sub photo not yet done ! save time : 0.13524174690246582 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 0.4429469108581543 time for calcul the mask position with numpy : 0.16479015350341797 nb_pixel_total : 2032498 time to create 1 rle with new method : 0.2769176959991455 time for calcul the mask position with numpy : 0.006642818450927734 nb_pixel_total : 1624 time to create 1 rle with old method : 0.0021576881408691406 time for calcul the mask position with numpy : 0.006659030914306641 nb_pixel_total : 5057 time to create 1 rle with old method : 0.006902933120727539 time for calcul the mask position with numpy : 0.0069484710693359375 nb_pixel_total : 2894 time to create 1 rle with old method : 0.0040285587310791016 time for calcul the mask position with numpy : 0.00658106803894043 nb_pixel_total : 4832 time to create 1 rle with old method : 0.006340980529785156 time for calcul the mask position with numpy : 0.006456613540649414 nb_pixel_total : 24650 time to create 1 rle with old method : 0.03264355659484863 time for calcul the mask position with numpy : 0.006194353103637695 nb_pixel_total : 2045 time to create 1 rle with old method : 0.0026803016662597656 create new chi : 0.546807050704956 time to delete rle : 0.0003712177276611328 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 2136 TO DO : save crop sub photo not yet done ! save time : 0.17934489250183105 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.05618643760681152 time for calcul the mask position with numpy : 0.03169870376586914 nb_pixel_total : 1972633 time to create 1 rle with new method : 0.09836888313293457 time for calcul the mask position with numpy : 0.008470535278320312 nb_pixel_total : 93610 time to create 1 rle with old method : 0.1366109848022461 time for calcul the mask position with numpy : 0.006548404693603516 nb_pixel_total : 5032 time to create 1 rle with old method : 0.006664752960205078 time for calcul the mask position with numpy : 0.0063898563385009766 nb_pixel_total : 2325 time to create 1 rle with old method : 0.0031380653381347656 create new chi : 0.29843854904174805 time to delete rle : 0.0004105567932128906 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2252 TO DO : save crop sub photo not yet done ! save time : 0.18693852424621582 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.04301881790161133 time for calcul the mask position with numpy : 0.022881031036376953 nb_pixel_total : 2068201 time to create 1 rle with new method : 0.10939598083496094 time for calcul the mask position with numpy : 0.006364107131958008 nb_pixel_total : 5399 time to create 1 rle with old method : 0.007084846496582031 create new chi : 0.15754413604736328 time to delete rle : 0.00021719932556152344 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1216 TO DO : save crop sub photo not yet done ! save time : 0.12384819984436035 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03457760810852051 time for calcul the mask position with numpy : 0.18365907669067383 nb_pixel_total : 2069775 time to create 1 rle with new method : 0.0995016098022461 time for calcul the mask position with numpy : 0.010470151901245117 nb_pixel_total : 3825 time to create 1 rle with old method : 0.00505828857421875 create new chi : 0.3104848861694336 time to delete rle : 0.000263214111328125 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1222 TO DO : save crop sub photo not yet done ! save time : 0.13221311569213867 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.08044886589050293 time for calcul the mask position with numpy : 0.17734718322753906 nb_pixel_total : 2070868 time to create 1 rle with new method : 0.0866854190826416 time for calcul the mask position with numpy : 0.00784921646118164 nb_pixel_total : 2732 time to create 1 rle with old method : 0.003738880157470703 create new chi : 0.2863457202911377 time to delete rle : 0.000217437744140625 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1196 TO DO : save crop sub photo not yet done ! save time : 0.11479401588439941 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.21986722946166992 time for calcul the mask position with numpy : 0.21806716918945312 nb_pixel_total : 2047272 time to create 1 rle with new method : 0.10560750961303711 time for calcul the mask position with numpy : 0.006581306457519531 nb_pixel_total : 5947 time to create 1 rle with old method : 0.008177757263183594 time for calcul the mask position with numpy : 0.0071904659271240234 nb_pixel_total : 4595 time to create 1 rle with old method : 0.006165266036987305 time for calcul the mask position with numpy : 0.007230281829833984 nb_pixel_total : 7292 time to create 1 rle with old method : 0.01067209243774414 time for calcul the mask position with numpy : 0.008118152618408203 nb_pixel_total : 5598 time to create 1 rle with old method : 0.00799107551574707 time for calcul the mask position with numpy : 0.00686335563659668 nb_pixel_total : 2896 time to create 1 rle with old method : 0.0038156509399414062 create new chi : 0.4067976474761963 time to delete rle : 0.00037980079650878906 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 2068 TO DO : save crop sub photo not yet done ! save time : 0.18274474143981934 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.045867919921875 time for calcul the mask position with numpy : 0.021758079528808594 nb_pixel_total : 2064087 time to create 1 rle with new method : 0.1871626377105713 time for calcul the mask position with numpy : 0.006743431091308594 nb_pixel_total : 5975 time to create 1 rle with old method : 0.008229255676269531 time for calcul the mask position with numpy : 0.006654262542724609 nb_pixel_total : 3538 time to create 1 rle with old method : 0.004698038101196289 create new chi : 0.23554086685180664 time to delete rle : 0.00026607513427734375 batch 1 Loaded 5 chid ids of type : 3594 +++Number RLEs to save : 1530 TO DO : save crop sub photo not yet done ! save time : 0.1453385353088379 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.1803908348083496 time for calcul the mask position with numpy : 0.07278013229370117 nb_pixel_total : 1929277 time to create 1 rle with new method : 0.23765182495117188 time for calcul the mask position with numpy : 0.006510734558105469 nb_pixel_total : 10530 time to create 1 rle with old method : 0.014059782028198242 time for calcul the mask position with numpy : 0.0076677799224853516 nb_pixel_total : 2507 time to create 1 rle with old method : 0.004119873046875 time for calcul the mask position with numpy : 0.006335258483886719 nb_pixel_total : 6986 time to create 1 rle with old method : 0.009656906127929688 time for calcul the mask position with numpy : 0.007333993911743164 nb_pixel_total : 111008 time to create 1 rle with old method : 0.1472921371459961 time for calcul the mask position with numpy : 0.007033348083496094 nb_pixel_total : 13292 time to create 1 rle with old method : 0.017791032791137695 create new chi : 0.5491056442260742 time to delete rle : 0.0005824565887451172 batch 1 Loaded 11 chid ids of type : 3594 +++++++++Number RLEs to save : 3168 TO DO : save crop sub photo not yet done ! save time : 0.22593331336975098 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.037480831146240234 time for calcul the mask position with numpy : 0.020194292068481445 nb_pixel_total : 1978998 time to create 1 rle with new method : 0.05550026893615723 time for calcul the mask position with numpy : 0.0071947574615478516 nb_pixel_total : 94602 time to create 1 rle with old method : 0.12541580200195312 create new chi : 0.20870113372802734 time to delete rle : 0.0003383159637451172 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 2128 TO DO : save crop sub photo not yet done ! save time : 0.1700122356414795 nb_obj : 6 nb_hashtags : 1 time to prepare the origin masks : 0.3713831901550293 time for calcul the mask position with numpy : 0.3405954837799072 nb_pixel_total : 2038018 time to create 1 rle with new method : 0.5611593723297119 time for calcul the mask position with numpy : 0.006597042083740234 nb_pixel_total : 3985 time to create 1 rle with old method : 0.005040168762207031 time for calcul the mask position with numpy : 0.007039546966552734 nb_pixel_total : 8462 time to create 1 rle with old method : 0.010941743850708008 time for calcul the mask position with numpy : 0.007070779800415039 nb_pixel_total : 4818 time to create 1 rle with old method : 0.00651240348815918 time for calcul the mask position with numpy : 0.007450103759765625 nb_pixel_total : 6211 time to create 1 rle with old method : 0.008173227310180664 time for calcul the mask position with numpy : 0.006970643997192383 nb_pixel_total : 7945 time to create 1 rle with old method : 0.011619806289672852 time for calcul the mask position with numpy : 0.007114410400390625 nb_pixel_total : 4161 time to create 1 rle with old method : 0.007618427276611328 create new chi : 1.0039279460906982 time to delete rle : 0.0006513595581054688 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 2632 TO DO : save crop sub photo not yet done ! save time : 0.20981121063232422 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03496837615966797 time for calcul the mask position with numpy : 0.020056486129760742 nb_pixel_total : 2065804 time to create 1 rle with new method : 0.11951351165771484 time for calcul the mask position with numpy : 0.0065042972564697266 nb_pixel_total : 7796 time to create 1 rle with old method : 0.010325431823730469 create new chi : 0.16654562950134277 time to delete rle : 0.0002593994140625 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1316 TO DO : save crop sub photo not yet done ! save time : 0.13940930366516113 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.0362703800201416 time for calcul the mask position with numpy : 0.22610092163085938 nb_pixel_total : 2070532 time to create 1 rle with new method : 0.14867758750915527 time for calcul the mask position with numpy : 0.0061206817626953125 nb_pixel_total : 3068 time to create 1 rle with old method : 0.004073619842529297 create new chi : 0.3952765464782715 time to delete rle : 0.0002288818359375 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1172 TO DO : save crop sub photo not yet done ! save time : 0.1214134693145752 No data in photo_id : 1395083810 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.25841498374938965 time for calcul the mask position with numpy : 0.27271008491516113 nb_pixel_total : 2061346 time to create 1 rle with new method : 0.22583842277526855 time for calcul the mask position with numpy : 0.010888099670410156 nb_pixel_total : 3884 time to create 1 rle with old method : 0.005227327346801758 time for calcul the mask position with numpy : 0.010605335235595703 nb_pixel_total : 3871 time to create 1 rle with old method : 0.005250692367553711 time for calcul the mask position with numpy : 0.011064291000366211 nb_pixel_total : 4499 time to create 1 rle with old method : 0.005975961685180664 create new chi : 0.559950590133667 time to delete rle : 0.00036144256591796875 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1592 TO DO : save crop sub photo not yet done ! save time : 0.1543281078338623 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.05408310890197754 time for calcul the mask position with numpy : 0.021898508071899414 nb_pixel_total : 1974740 time to create 1 rle with new method : 0.16375136375427246 time for calcul the mask position with numpy : 0.007622718811035156 nb_pixel_total : 98860 time to create 1 rle with old method : 0.14320802688598633 create new chi : 0.33692049980163574 time to delete rle : 0.0004904270172119141 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 2140 TO DO : save crop sub photo not yet done ! save time : 0.18517851829528809 nb_obj : 5 nb_hashtags : 3 time to prepare the origin masks : 0.22215557098388672 time for calcul the mask position with numpy : 0.08819127082824707 nb_pixel_total : 2033948 time to create 1 rle with new method : 0.21239137649536133 time for calcul the mask position with numpy : 0.006502866744995117 nb_pixel_total : 3931 time to create 1 rle with old method : 0.005218505859375 time for calcul the mask position with numpy : 0.006455659866333008 nb_pixel_total : 5570 time to create 1 rle with old method : 0.007322788238525391 time for calcul the mask position with numpy : 0.006666898727416992 nb_pixel_total : 5352 time to create 1 rle with old method : 0.007329463958740234 time for calcul the mask position with numpy : 0.007022380828857422 nb_pixel_total : 20173 time to create 1 rle with old method : 0.026980876922607422 time for calcul the mask position with numpy : 0.006895780563354492 nb_pixel_total : 4626 time to create 1 rle with old method : 0.006188869476318359 create new chi : 0.39751410484313965 time to delete rle : 0.00047898292541503906 batch 1 Loaded 11 chid ids of type : 3594 ++++++++Number RLEs to save : 2278 TO DO : save crop sub photo not yet done ! save time : 0.18713760375976562 nb_obj : 4 nb_hashtags : 3 time to prepare the origin masks : 0.06508708000183105 time for calcul the mask position with numpy : 0.022202253341674805 nb_pixel_total : 1885426 time to create 1 rle with new method : 0.0907907485961914 time for calcul the mask position with numpy : 0.006680488586425781 nb_pixel_total : 35239 time to create 1 rle with old method : 0.04598093032836914 time for calcul the mask position with numpy : 0.006819009780883789 nb_pixel_total : 106726 time to create 1 rle with old method : 0.14069819450378418 time for calcul the mask position with numpy : 0.006476402282714844 nb_pixel_total : 14512 time to create 1 rle with old method : 0.019056081771850586 time for calcul the mask position with numpy : 0.006687641143798828 nb_pixel_total : 31697 time to create 1 rle with old method : 0.04134058952331543 create new chi : 0.39395809173583984 time to delete rle : 0.000560760498046875 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 3432 TO DO : save crop sub photo not yet done ! save time : 0.24530434608459473 nb_obj : 6 nb_hashtags : 1 time to prepare the origin masks : 0.3562281131744385 time for calcul the mask position with numpy : 0.07469391822814941 nb_pixel_total : 2041353 time to create 1 rle with new method : 0.1529707908630371 time for calcul the mask position with numpy : 0.006972074508666992 nb_pixel_total : 8762 time to create 1 rle with old method : 0.012003898620605469 time for calcul the mask position with numpy : 0.0070612430572509766 nb_pixel_total : 7026 time to create 1 rle with old method : 0.011554479598999023 time for calcul the mask position with numpy : 0.0067903995513916016 nb_pixel_total : 5246 time to create 1 rle with old method : 0.008638620376586914 time for calcul the mask position with numpy : 0.0067653656005859375 nb_pixel_total : 3049 time to create 1 rle with old method : 0.0040132999420166016 time for calcul the mask position with numpy : 0.006403923034667969 nb_pixel_total : 6089 time to create 1 rle with old method : 0.008011341094970703 time for calcul the mask position with numpy : 0.006635427474975586 nb_pixel_total : 2075 time to create 1 rle with old method : 0.0027952194213867188 create new chi : 0.32897257804870605 time to delete rle : 0.0004601478576660156 batch 1 Loaded 13 chid ids of type : 3594 ++++++++Number RLEs to save : 2124 TO DO : save crop sub photo not yet done ! save time : 0.1797502040863037 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.05414319038391113 time for calcul the mask position with numpy : 0.019187211990356445 nb_pixel_total : 1960000 time to create 1 rle with new method : 0.0871741771697998 time for calcul the mask position with numpy : 0.007409095764160156 nb_pixel_total : 105144 time to create 1 rle with old method : 0.16355228424072266 time for calcul the mask position with numpy : 0.006544589996337891 nb_pixel_total : 3697 time to create 1 rle with old method : 0.00490117073059082 time for calcul the mask position with numpy : 0.006613731384277344 nb_pixel_total : 4759 time to create 1 rle with old method : 0.006349086761474609 create new chi : 0.311295747756958 time to delete rle : 0.0004410743713378906 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2440 TO DO : save crop sub photo not yet done ! save time : 0.1837139129638672 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03478717803955078 time for calcul the mask position with numpy : 0.02023768424987793 nb_pixel_total : 2065807 time to create 1 rle with new method : 0.058029890060424805 time for calcul the mask position with numpy : 0.007528066635131836 nb_pixel_total : 7793 time to create 1 rle with old method : 0.01047062873840332 create new chi : 0.09662699699401855 time to delete rle : 0.0004093647003173828 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1426 TO DO : save crop sub photo not yet done ! save time : 0.13605022430419922 nb_obj : 3 nb_hashtags : 1 time to prepare the origin masks : 0.26378393173217773 time for calcul the mask position with numpy : 0.06982874870300293 nb_pixel_total : 2056701 time to create 1 rle with new method : 0.08970952033996582 time for calcul the mask position with numpy : 0.006433010101318359 nb_pixel_total : 5954 time to create 1 rle with old method : 0.007857322692871094 time for calcul the mask position with numpy : 0.006529808044433594 nb_pixel_total : 4627 time to create 1 rle with old method : 0.006166934967041016 time for calcul the mask position with numpy : 0.0068056583404541016 nb_pixel_total : 6318 time to create 1 rle with old method : 0.008312225341796875 create new chi : 0.21194744110107422 time to delete rle : 0.00034880638122558594 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1624 TO DO : save crop sub photo not yet done ! save time : 0.14230942726135254 No data in photo_id : 1395083455 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.04228019714355469 time for calcul the mask position with numpy : 0.18311643600463867 nb_pixel_total : 2054324 time to create 1 rle with new method : 0.08446025848388672 time for calcul the mask position with numpy : 0.006364107131958008 nb_pixel_total : 8019 time to create 1 rle with old method : 0.010753154754638672 time for calcul the mask position with numpy : 0.0066051483154296875 nb_pixel_total : 11257 time to create 1 rle with old method : 0.015038728713989258 create new chi : 0.31647586822509766 time to delete rle : 0.00035953521728515625 batch 1 Loaded 5 chid ids of type : 3594 +++Number RLEs to save : 1788 TO DO : save crop sub photo not yet done ! save time : 0.14711403846740723 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.11763453483581543 time for calcul the mask position with numpy : 0.12063264846801758 nb_pixel_total : 2044317 time to create 1 rle with new method : 0.09025740623474121 time for calcul the mask position with numpy : 0.0063018798828125 nb_pixel_total : 1409 time to create 1 rle with old method : 0.0018732547760009766 time for calcul the mask position with numpy : 0.006232738494873047 nb_pixel_total : 23984 time to create 1 rle with old method : 0.03135561943054199 time for calcul the mask position with numpy : 0.006234407424926758 nb_pixel_total : 3890 time to create 1 rle with old method : 0.005165815353393555 create new chi : 0.2811594009399414 time to delete rle : 0.0003905296325683594 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1750 TO DO : save crop sub photo not yet done ! save time : 0.15203356742858887 nb_obj : 3 nb_hashtags : 3 time to prepare the origin masks : 0.21634864807128906 time for calcul the mask position with numpy : 0.22019267082214355 nb_pixel_total : 1944351 time to create 1 rle with new method : 0.16867756843566895 time for calcul the mask position with numpy : 0.006277561187744141 nb_pixel_total : 7527 time to create 1 rle with old method : 0.009956121444702148 time for calcul the mask position with numpy : 0.007760047912597656 nb_pixel_total : 115397 time to create 1 rle with old method : 0.15190863609313965 time for calcul the mask position with numpy : 0.0065000057220458984 nb_pixel_total : 6325 time to create 1 rle with old method : 0.008851766586303711 create new chi : 0.5906467437744141 time to delete rle : 0.0003170967102050781 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2728 TO DO : save crop sub photo not yet done ! save time : 0.20785093307495117 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.07007431983947754 time for calcul the mask position with numpy : 0.043875694274902344 nb_pixel_total : 1947635 time to create 1 rle with new method : 0.09309935569763184 time for calcul the mask position with numpy : 0.006411552429199219 nb_pixel_total : 11078 time to create 1 rle with old method : 0.014786958694458008 time for calcul the mask position with numpy : 0.008994817733764648 nb_pixel_total : 108258 time to create 1 rle with old method : 0.14356279373168945 time for calcul the mask position with numpy : 0.0064122676849365234 nb_pixel_total : 6629 time to create 1 rle with old method : 0.008998394012451172 create new chi : 0.33791637420654297 time to delete rle : 0.0003821849822998047 batch 1 Loaded 7 chid ids of type : 3594 ++++Number RLEs to save : 2764 TO DO : save crop sub photo not yet done ! save time : 0.22597527503967285 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.07604503631591797 time for calcul the mask position with numpy : 0.08320903778076172 nb_pixel_total : 1948978 time to create 1 rle with new method : 0.08627629280090332 time for calcul the mask position with numpy : 0.006862163543701172 nb_pixel_total : 2855 time to create 1 rle with old method : 0.003825664520263672 time for calcul the mask position with numpy : 0.0069849491119384766 nb_pixel_total : 116053 time to create 1 rle with old method : 0.15003275871276855 time for calcul the mask position with numpy : 0.0064239501953125 nb_pixel_total : 5714 time to create 1 rle with old method : 0.00762176513671875 create new chi : 0.36308813095092773 time to delete rle : 0.00046896934509277344 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2470 TO DO : save crop sub photo not yet done ! save time : 0.19161343574523926 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.0322110652923584 time for calcul the mask position with numpy : 0.01907658576965332 nb_pixel_total : 2067257 time to create 1 rle with new method : 0.10048675537109375 time for calcul the mask position with numpy : 0.00599217414855957 nb_pixel_total : 6343 time to create 1 rle with old method : 0.008216619491577148 create new chi : 0.14075493812561035 time to delete rle : 0.00022602081298828125 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1288 TO DO : save crop sub photo not yet done ! save time : 0.11467671394348145 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.20345163345336914 time for calcul the mask position with numpy : 0.09548139572143555 nb_pixel_total : 2050857 time to create 1 rle with new method : 0.1654670238494873 time for calcul the mask position with numpy : 0.0061397552490234375 nb_pixel_total : 10894 time to create 1 rle with old method : 0.013655424118041992 time for calcul the mask position with numpy : 0.006122589111328125 nb_pixel_total : 5216 time to create 1 rle with old method : 0.0066416263580322266 time for calcul the mask position with numpy : 0.006159305572509766 nb_pixel_total : 6633 time to create 1 rle with old method : 0.008594751358032227 create new chi : 0.3186023235321045 time to delete rle : 0.000324249267578125 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1758 TO DO : save crop sub photo not yet done ! save time : 0.14582300186157227 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.04090452194213867 time for calcul the mask position with numpy : 0.876157283782959 nb_pixel_total : 2064509 time to create 1 rle with new method : 0.08514094352722168 time for calcul the mask position with numpy : 0.006623744964599609 nb_pixel_total : 5696 time to create 1 rle with old method : 0.007903575897216797 time for calcul the mask position with numpy : 0.006443023681640625 nb_pixel_total : 3395 time to create 1 rle with old method : 0.004491329193115234 create new chi : 0.996467113494873 time to delete rle : 0.0003025531768798828 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1404 TO DO : save crop sub photo not yet done ! save time : 0.12352204322814941 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.5803306102752686 time for calcul the mask position with numpy : 0.28424644470214844 nb_pixel_total : 2019586 time to create 1 rle with new method : 0.08872652053833008 time for calcul the mask position with numpy : 0.006418704986572266 nb_pixel_total : 6816 time to create 1 rle with old method : 0.008912324905395508 time for calcul the mask position with numpy : 0.006233930587768555 nb_pixel_total : 8313 time to create 1 rle with old method : 0.01086735725402832 time for calcul the mask position with numpy : 0.00658869743347168 nb_pixel_total : 30643 time to create 1 rle with old method : 0.03982424736022949 time for calcul the mask position with numpy : 0.006374359130859375 nb_pixel_total : 8242 time to create 1 rle with old method : 0.010503053665161133 create new chi : 0.47983407974243164 time to delete rle : 0.0003788471221923828 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2038 TO DO : save crop sub photo not yet done ! save time : 0.18108010292053223 nb_obj : 4 nb_hashtags : 1 time to prepare the origin masks : 0.0669870376586914 time for calcul the mask position with numpy : 0.5075523853302002 nb_pixel_total : 2052309 time to create 1 rle with new method : 0.5258681774139404 time for calcul the mask position with numpy : 0.006195545196533203 nb_pixel_total : 7419 time to create 1 rle with old method : 0.009495258331298828 time for calcul the mask position with numpy : 0.006112575531005859 nb_pixel_total : 5381 time to create 1 rle with old method : 0.006884336471557617 time for calcul the mask position with numpy : 0.006266117095947266 nb_pixel_total : 2602 time to create 1 rle with old method : 0.0034363269805908203 time for calcul the mask position with numpy : 0.0062656402587890625 nb_pixel_total : 5889 time to create 1 rle with old method : 0.0076978206634521484 create new chi : 1.0940797328948975 time to delete rle : 0.00036644935607910156 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 1744 TO DO : save crop sub photo not yet done ! save time : 0.1564769744873047 map_output_result : {1395085776: (0.0, 'Should be the crop_list due to order', 0), 1395085278: (0.0, 'Should be the crop_list due to order', 0.0), 1395085277: (0.0, 'Should be the crop_list due to order', 0), 1395085274: (0.0, 'Should be the crop_list due to order', 0), 1395085270: (0.0, 'Should be the crop_list due to order', 0), 1395084955: (0.0, 'Should be the crop_list due to order', 0), 1395084939: (0.0, 'Should be the crop_list due to order', 0), 1395084917: (0.0, 'Should be the crop_list due to order', 0), 1395084913: (0.0, 'Should be the crop_list due to order', 0), 1395084910: (0.0, 'Should be the crop_list due to order', 0), 1395084905: (0.0, 'Should be the crop_list due to order', 0), 1395084529: (0.0, 'Should be the crop_list due to order', 0), 1395084497: (0.0, 'Should be the crop_list due to order', 0), 1395084295: (0.0, 'Should be the crop_list due to order', 0), 1395083881: (0.0, 'Should be the crop_list due to order', 0), 1395083810: (0.0, 'Should be the crop_list due to order', 0.0), 1395083745: (0.0, 'Should be the crop_list due to order', 0), 1395083676: (0.0, 'Should be the crop_list due to order', 0), 1395083617: (0.0, 'Should be the crop_list due to order', 0), 1395083554: (0.0, 'Should be the crop_list due to order', 0), 1395083486: (0.0, 'Should be the crop_list due to order', 0), 1395083477: (0.0, 'Should be the crop_list due to order', 0), 1395083474: (0.0, 'Should be the crop_list due to order', 0), 1395083456: (0.0, 'Should be the crop_list due to order', 0), 1395083455: (0.0, 'Should be the crop_list due to order', 0.0), 1395083436: (0.0, 'Should be the crop_list due to order', 0), 1395083433: (0.0, 'Should be the crop_list due to order', 0), 1395083430: (0.0, 'Should be the crop_list due to order', 0), 1395083427: (0.0, 'Should be the crop_list due to order', 0), 1395083425: (0.0, 'Should be the crop_list due to order', 0), 1395083383: (0.0, 'Should be the crop_list due to order', 0), 1395083355: (0.0, 'Should be the crop_list due to order', 0), 1395083349: (0.0, 'Should be the crop_list due to order', 0), 1395083337: (0.0, 'Should be the crop_list due to order', 0), 1395083304: (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 [1395085776, 1395085278, 1395085277, 1395085274, 1395085270, 1395084955, 1395084939, 1395084917, 1395084913, 1395084910, 1395084905, 1395084529, 1395084497, 1395084295, 1395083881, 1395083810, 1395083745, 1395083676, 1395083617, 1395083554, 1395083486, 1395083477, 1395083474, 1395083456, 1395083455, 1395083436, 1395083433, 1395083430, 1395083427, 1395083425, 1395083383, 1395083355, 1395083349, 1395083337, 1395083304] Looping around the photos to save general results len do output : 35 /1395085776.Didn't retrieve data . /1395085278.Didn't retrieve data . /1395085277.Didn't retrieve data . /1395085274.Didn't retrieve data . /1395085270.Didn't retrieve data . /1395084955.Didn't retrieve data . /1395084939.Didn't retrieve data . /1395084917.Didn't retrieve data . /1395084913.Didn't retrieve data . /1395084910.Didn't retrieve data . /1395084905.Didn't retrieve data . /1395084529.Didn't retrieve data . /1395084497.Didn't retrieve data . /1395084295.Didn't retrieve data . /1395083881.Didn't retrieve data . /1395083810.Didn't retrieve data . /1395083745.Didn't retrieve data . /1395083676.Didn't retrieve data . /1395083617.Didn't retrieve data . /1395083554.Didn't retrieve data . /1395083486.Didn't retrieve data . /1395083477.Didn't retrieve data . /1395083474.Didn't retrieve data . /1395083456.Didn't retrieve data . /1395083455.Didn't retrieve data . /1395083436.Didn't retrieve data . /1395083433.Didn't retrieve data . /1395083430.Didn't retrieve data . /1395083427.Didn't retrieve data . /1395083425.Didn't retrieve data . /1395083383.Didn't retrieve data . /1395083355.Didn't retrieve data . /1395083349.Didn't retrieve data . /1395083337.Didn't retrieve data . /1395083304.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, '4072125') ('3318', '28685556', '1395085776', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085278', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085277', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085274', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085270', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084955', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084939', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084917', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084913', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084910', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084905', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084529', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084497', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084295', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083881', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083810', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083745', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083676', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083617', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083554', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083486', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083477', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083474', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083456', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083455', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083436', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083433', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083430', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083427', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083425', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083383', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083355', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083349', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083337', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083304', None, None, None, None, None, '4072125') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 105 time used for this insertion : 0.018943309783935547 save_final save missing photos in datou_result : time spend for datou_step_exec : 23.911605834960938 time spend to save output : 0.02031707763671875 total time spend for step 3 : 23.931922912597656 step4:ventilate_hashtags_in_portfolio Wed Nov 19 01:22:16 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 28685556 get user id for portfolio 28685556 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`=28685556 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','carton','mal_croppe','background','papier','metal','pet_fonce','environnement','pehd','pet_clair','autre')) 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`=28685556 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','carton','mal_croppe','background','papier','metal','pet_fonce','environnement','pehd','pet_clair','autre')) 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`=28685556 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('flou','carton','mal_croppe','background','papier','metal','pet_fonce','environnement','pehd','pet_clair','autre')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/28686003,28686004,28686005,28686006,28686007,28686008,28686009,28686010,28686011,28686012,28686013?tags=flou,carton,mal_croppe,background,papier,metal,pet_fonce,environnement,pehd,pet_clair,autre Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1395085776, 1395085278, 1395085277, 1395085274, 1395085270, 1395084955, 1395084939, 1395084917, 1395084913, 1395084910, 1395084905, 1395084529, 1395084497, 1395084295, 1395083881, 1395083810, 1395083745, 1395083676, 1395083617, 1395083554, 1395083486, 1395083477, 1395083474, 1395083456, 1395083455, 1395083436, 1395083433, 1395083430, 1395083427, 1395083425, 1395083383, 1395083355, 1395083349, 1395083337, 1395083304] Looping around the photos to save general results len do output : 1 /28685556. 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, '4072125') ('3318', '28685556', '1395085776', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085278', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085277', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085274', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085270', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084955', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084939', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084917', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084913', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084910', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084905', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084529', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084497', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084295', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083881', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083810', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083745', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083676', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083617', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083554', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083486', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083477', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083474', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083456', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083455', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083436', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083433', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083430', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083427', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083425', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083383', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083355', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083349', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083337', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083304', None, None, None, None, None, '4072125') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.0216062068939209 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.7417006492614746 time spend to save output : 0.022023439407348633 total time spend for step 4 : 1.7637240886688232 step5:final Wed Nov 19 01:22:17 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 ! 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 : {1395085776: ('0.021235890652557324',), 1395085278: ('0.021235890652557324',), 1395085277: ('0.021235890652557324',), 1395085274: ('0.021235890652557324',), 1395085270: ('0.021235890652557324',), 1395084955: ('0.021235890652557324',), 1395084939: ('0.021235890652557324',), 1395084917: ('0.021235890652557324',), 1395084913: ('0.021235890652557324',), 1395084910: ('0.021235890652557324',), 1395084905: ('0.021235890652557324',), 1395084529: ('0.021235890652557324',), 1395084497: ('0.021235890652557324',), 1395084295: ('0.021235890652557324',), 1395083881: ('0.021235890652557324',), 1395083810: ('0.021235890652557324',), 1395083745: ('0.021235890652557324',), 1395083676: ('0.021235890652557324',), 1395083617: ('0.021235890652557324',), 1395083554: ('0.021235890652557324',), 1395083486: ('0.021235890652557324',), 1395083477: ('0.021235890652557324',), 1395083474: ('0.021235890652557324',), 1395083456: ('0.021235890652557324',), 1395083455: ('0.021235890652557324',), 1395083436: ('0.021235890652557324',), 1395083433: ('0.021235890652557324',), 1395083430: ('0.021235890652557324',), 1395083427: ('0.021235890652557324',), 1395083425: ('0.021235890652557324',), 1395083383: ('0.021235890652557324',), 1395083355: ('0.021235890652557324',), 1395083349: ('0.021235890652557324',), 1395083337: ('0.021235890652557324',), 1395083304: ('0.021235890652557324',)} new output for save of step final : {1395085776: ('0.021235890652557324',), 1395085278: ('0.021235890652557324',), 1395085277: ('0.021235890652557324',), 1395085274: ('0.021235890652557324',), 1395085270: ('0.021235890652557324',), 1395084955: ('0.021235890652557324',), 1395084939: ('0.021235890652557324',), 1395084917: ('0.021235890652557324',), 1395084913: ('0.021235890652557324',), 1395084910: ('0.021235890652557324',), 1395084905: ('0.021235890652557324',), 1395084529: ('0.021235890652557324',), 1395084497: ('0.021235890652557324',), 1395084295: ('0.021235890652557324',), 1395083881: ('0.021235890652557324',), 1395083810: ('0.021235890652557324',), 1395083745: ('0.021235890652557324',), 1395083676: ('0.021235890652557324',), 1395083617: ('0.021235890652557324',), 1395083554: ('0.021235890652557324',), 1395083486: ('0.021235890652557324',), 1395083477: ('0.021235890652557324',), 1395083474: ('0.021235890652557324',), 1395083456: ('0.021235890652557324',), 1395083455: ('0.021235890652557324',), 1395083436: ('0.021235890652557324',), 1395083433: ('0.021235890652557324',), 1395083430: ('0.021235890652557324',), 1395083427: ('0.021235890652557324',), 1395083425: ('0.021235890652557324',), 1395083383: ('0.021235890652557324',), 1395083355: ('0.021235890652557324',), 1395083349: ('0.021235890652557324',), 1395083337: ('0.021235890652557324',), 1395083304: ('0.021235890652557324',)} [1395085776, 1395085278, 1395085277, 1395085274, 1395085270, 1395084955, 1395084939, 1395084917, 1395084913, 1395084910, 1395084905, 1395084529, 1395084497, 1395084295, 1395083881, 1395083810, 1395083745, 1395083676, 1395083617, 1395083554, 1395083486, 1395083477, 1395083474, 1395083456, 1395083455, 1395083436, 1395083433, 1395083430, 1395083427, 1395083425, 1395083383, 1395083355, 1395083349, 1395083337, 1395083304] Looping around the photos to save general results len do output : 35 /1395085776.Didn't retrieve data . /1395085278.Didn't retrieve data . /1395085277.Didn't retrieve data . /1395085274.Didn't retrieve data . /1395085270.Didn't retrieve data . /1395084955.Didn't retrieve data . /1395084939.Didn't retrieve data . /1395084917.Didn't retrieve data . /1395084913.Didn't retrieve data . /1395084910.Didn't retrieve data . /1395084905.Didn't retrieve data . /1395084529.Didn't retrieve data . /1395084497.Didn't retrieve data . /1395084295.Didn't retrieve data . /1395083881.Didn't retrieve data . /1395083810.Didn't retrieve data . /1395083745.Didn't retrieve data . /1395083676.Didn't retrieve data . /1395083617.Didn't retrieve data . /1395083554.Didn't retrieve data . /1395083486.Didn't retrieve data . /1395083477.Didn't retrieve data . /1395083474.Didn't retrieve data . /1395083456.Didn't retrieve data . /1395083455.Didn't retrieve data . /1395083436.Didn't retrieve data . /1395083433.Didn't retrieve data . /1395083430.Didn't retrieve data . /1395083427.Didn't retrieve data . /1395083425.Didn't retrieve data . /1395083383.Didn't retrieve data . /1395083355.Didn't retrieve data . /1395083349.Didn't retrieve data . /1395083337.Didn't retrieve data . /1395083304.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, '4072125') ('3318', '28685556', '1395085776', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085278', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085277', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085274', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085270', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084955', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084939', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084917', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084913', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084910', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084905', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084529', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084497', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084295', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083881', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083810', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083745', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083676', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083617', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083554', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083486', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083477', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083474', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083456', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083455', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083436', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083433', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083430', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083427', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083425', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083383', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083355', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083349', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083337', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083304', None, None, None, None, None, '4072125') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 105 time used for this insertion : 0.018549680709838867 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.11974310874938965 time spend to save output : 0.019697189331054688 total time spend for step 5 : 0.13944029808044434 step6:blur_detection Wed Nov 19 01:22:17 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 ! 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/1763511628_365925_1395085776_c8d0b8ab13da7f4fc7c62ed7c52d1147.jpg resize: (1080, 1920) 1395085776 2.5183632989086995 treat image : temp/1763511628_365925_1395085278_a59a8aa8e94720c1b3baccd3e8392f2f.jpg resize: (1080, 1920) 1395085278 0.5426287806057146 treat image : temp/1763511628_365925_1395085277_4542b51045871966394fa6af6b66886f.jpg resize: (1080, 1920) 1395085277 -3.8530138977208037 treat image : temp/1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab.jpg resize: (1080, 1920) 1395085274 -0.13372500492072117 treat image : temp/1763511628_365925_1395085270_659240d6cd6ba4ba7ed946f15f1af8c9.jpg resize: (1080, 1920) 1395085270 0.7188211557125053 treat image : temp/1763511628_365925_1395084955_deefde9f7d9a6e89d29b9820a9129e58.jpg resize: (1080, 1920) 1395084955 1.7784568640572296 treat image : temp/1763511628_365925_1395084939_d0044eb36bfbf91216d1b0f1e53c2a45.jpg resize: (1080, 1920) 1395084939 0.05143277458693898 treat image : temp/1763511628_365925_1395084917_5ce81debc948df4518a3017903e5cabd.jpg resize: (1080, 1920) 1395084917 -4.2281773403441925 treat image : temp/1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504.jpg resize: (1080, 1920) 1395084913 -0.26764140227663924 treat image : temp/1763511628_365925_1395084910_8a7eab539f225850acf5ed9b573a368f.jpg resize: (1080, 1920) 1395084910 0.5252341125745491 treat image : temp/1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9.jpg resize: (1080, 1920) 1395084905 -3.0310736501342346 treat image : temp/1763511628_365925_1395084529_1d95ff0ffe5ac8882c538d253f40e41e.jpg resize: (1080, 1920) 1395084529 2.1660305213833517 treat image : temp/1763511628_365925_1395084497_34cb8794d32939f476811e1953183229.jpg resize: (1080, 1920) 1395084497 -3.938799744947716 treat image : temp/1763511628_365925_1395084295_4bcc09700007338f10580143c21b8762.jpg resize: (1080, 1920) 1395084295 -0.6098513063731087 treat image : temp/1763511628_365925_1395083881_0c22a38fd50dfaf9fb273de1bef3aaf9.jpg resize: (1080, 1920) 1395083881 5.156801840302457 treat image : temp/1763511628_365925_1395083810_4e741b4677d90f43ed0261c4f3d162a8.jpg resize: (1080, 1920) 1395083810 0.08598656233276217 treat image : temp/1763511628_365925_1395083745_03ec8670a614fccccca74b26be90e0a3.jpg resize: (1080, 1920) 1395083745 -3.4345272042075616 treat image : temp/1763511628_365925_1395083676_8eae01e12edf6429d1a68d82bf1ba44d.jpg resize: (1080, 1920) 1395083676 -0.28109493796973445 treat image : temp/1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468.jpg resize: (1080, 1920) 1395083617 -1.903961185865236 treat image : temp/1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5.jpg resize: (1080, 1920) 1395083554 -4.017942597857622 treat image : temp/1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b.jpg resize: (1080, 1920) 1395083486 0.4824289008789361 treat image : temp/1763511628_365925_1395083477_b6fe11f685f40d92848a079bce81cce0.jpg resize: (1080, 1920) 1395083477 0.2175079323414451 treat image : temp/1763511628_365925_1395083474_ca6789a7f667ed21801c3f51028e6b62.jpg resize: (1080, 1920) 1395083474 0.8854326480866106 treat image : temp/1763511628_365925_1395083456_9cad6d3dea1416e8f69cdd8ad9b6b0fd.jpg resize: (1080, 1920) 1395083456 0.11987867731731564 treat image : temp/1763511628_365925_1395083455_9066a60ea5e61483fe9d46a52d1fc473.jpg resize: (1080, 1920) 1395083455 2.1424096635516885 treat image : temp/1763511628_365925_1395083436_3691e1837407fcaaad22a1bdd1812585.jpg resize: (1080, 1920) 1395083436 -0.11716570045315836 treat image : temp/1763511628_365925_1395083433_4873cd265f69414c0a9b98f5da7df1c0.jpg resize: (1080, 1920) 1395083433 0.38631061488105767 treat image : temp/1763511628_365925_1395083430_eb22b2a3b9f9c8fff756c607b9f9d7d0.jpg resize: (1080, 1920) 1395083430 0.938667006723197 treat image : temp/1763511628_365925_1395083427_bcfeb55e087a6b1fff385bb7d0f21e2a.jpg resize: (1080, 1920) 1395083427 1.3422070953233238 treat image : temp/1763511628_365925_1395083425_1fdc89ebef2fe4a809ab5c833219c462.jpg resize: (1080, 1920) 1395083425 0.8809069431381841 treat image : temp/1763511628_365925_1395083383_69fbd6e7adbe4ea8508d0e1929734642.jpg resize: (1080, 1920) 1395083383 -0.014154291492839581 treat image : temp/1763511628_365925_1395083355_55df7c0b9d0b6a41c1b36d1cf864f97c.jpg resize: (1080, 1920) 1395083355 0.5813021953119787 treat image : temp/1763511628_365925_1395083349_044ef3619deaa12087872dd285f259a3.jpg resize: (1080, 1920) 1395083349 0.14845800719951685 treat image : temp/1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528.jpg resize: (1080, 1920) 1395083337 1.2882751986521725 treat image : temp/1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63.jpg resize: (1080, 1920) 1395083304 0.5260813506750699 treat image : temp/1763511628_365925_1395085776_c8d0b8ab13da7f4fc7c62ed7c52d1147_rle_crop_4038198783_0.png resize: (89, 105) 1395122124 -1.937859210279708 treat image : temp/1763511628_365925_1395085776_c8d0b8ab13da7f4fc7c62ed7c52d1147_rle_crop_4038198784_0.png resize: (59, 66) 1395122125 -2.5458763014496943 treat image : temp/1763511628_365925_1395085277_4542b51045871966394fa6af6b66886f_rle_crop_4038198785_0.png resize: (100, 73) 1395122126 -3.7591723921408904 treat image : temp/1763511628_365925_1395085277_4542b51045871966394fa6af6b66886f_rle_crop_4038198786_0.png resize: (61, 55) 1395122128 1.2889725041821676 treat image : temp/1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198787_0.png resize: (37, 63) 1395122130 20.0 treat image : temp/1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198789_0.png resize: (104, 70) 1395122131 -0.7276268732614343 treat image : temp/1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198792_0.png resize: (44, 50) 1395122132 1.6112799946084884 treat image : temp/1763511628_365925_1395085270_659240d6cd6ba4ba7ed946f15f1af8c9_rle_crop_4038198793_0.png resize: (45, 64) 1395122133 2.165794046524466 treat image : temp/1763511628_365925_1395084955_deefde9f7d9a6e89d29b9820a9129e58_rle_crop_4038198796_0.png resize: (67, 105) 1395122134 0.047087686015660685 treat image : temp/1763511628_365925_1395084939_d0044eb36bfbf91216d1b0f1e53c2a45_rle_crop_4038198797_0.png resize: (70, 84) 1395122135 -0.7637828896756892 treat image : temp/1763511628_365925_1395084917_5ce81debc948df4518a3017903e5cabd_rle_crop_4038198798_0.png resize: (58, 72) 1395122136 -1.7912298971361649 treat image : temp/1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198799_0.png resize: (48, 69) 1395122137 20.0 treat image : temp/1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198800_0.png resize: (104, 103) 1395122138 -1.442811435992747 treat image : temp/1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198802_0.png resize: (110, 74) 1395122139 -0.3402906496491893 treat image : temp/1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198803_0.png resize: (129, 67) 1395122140 -1.129613925552855 treat image : temp/1763511628_365925_1395084910_8a7eab539f225850acf5ed9b573a368f_rle_crop_4038198804_0.png resize: (54, 80) 1395122141 0.7422007648338645 treat image : temp/1763511628_365925_1395084910_8a7eab539f225850acf5ed9b573a368f_rle_crop_4038198805_0.png resize: (168, 75) 1395122142 -2.5687455165932764 treat image : temp/1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198809_0.png resize: (124, 50) 1395122143 -2.851240747479889 treat image : temp/1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198810_0.png resize: (148, 93) 1395122144 -0.6568734626893933 treat image : temp/1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198812_0.png resize: (110, 69) 1395122145 -2.0860200371674567 treat image : temp/1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198813_0.png resize: (152, 140) 1395122146 -2.279919449431316 treat image : temp/1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198814_0.png resize: (229, 44) 1395122147 -2.1696221627562196 treat image : temp/1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198815_0.png resize: (74, 106) 1395122148 -1.8101792123827456 treat image : temp/1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198816_0.png resize: (127, 99) 1395122149 -1.392055627718139 treat image : temp/1763511628_365925_1395084497_34cb8794d32939f476811e1953183229_rle_crop_4038198817_0.png resize: (75, 88) 1395122150 -2.2937560461343813 treat image : temp/1763511628_365925_1395083745_03ec8670a614fccccca74b26be90e0a3_rle_crop_4038198820_0.png resize: (70, 107) 1395122151 -0.9230067457384129 treat image : temp/1763511628_365925_1395083745_03ec8670a614fccccca74b26be90e0a3_rle_crop_4038198821_0.png resize: (56, 106) 1395122152 -3.190947261776484 treat image : temp/1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198824_0.png resize: (95, 89) 1395122153 -4.7305888486300995 treat image : temp/1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198827_0.png resize: (120, 65) 1395122154 -0.4488079128640493 treat image : temp/1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198828_0.png resize: (73, 71) 1395122155 1.905221104635591 treat image : temp/1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198832_0.png resize: (144, 368) 1395122156 -4.530623421605851 treat image : temp/1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198833_0.png resize: (50, 59) 1395122157 -0.5612129939315581 treat image : temp/1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198834_0.png resize: (112, 114) 1395122158 -1.1207815127943137 treat image : temp/1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198835_0.png resize: (43, 85) 1395122159 20.0 treat image : temp/1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198836_0.png resize: (101, 109) 1395122160 -1.8622762144980407 treat image : temp/1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198837_0.png resize: (122, 89) 1395122161 -1.8011527923447526 treat image : temp/1763511628_365925_1395083486_286fc5fd7a8c33fcf36b79e97133850b_rle_crop_4038198838_0.png resize: (84, 142) 1395122162 -0.5016052806819774 treat image : temp/1763511628_365925_1395083477_b6fe11f685f40d92848a079bce81cce0_rle_crop_4038198840_0.png resize: (67, 82) 1395122163 0.4807522735939639 treat image : temp/1763511628_365925_1395083474_ca6789a7f667ed21801c3f51028e6b62_rle_crop_4038198842_0.png resize: (173, 78) 1395122164 -0.7031726950159893 treat image : temp/1763511628_365925_1395083456_9cad6d3dea1416e8f69cdd8ad9b6b0fd_rle_crop_4038198843_0.png resize: (88, 120) 1395122165 -1.4921499289050162 treat image : temp/1763511628_365925_1395083456_9cad6d3dea1416e8f69cdd8ad9b6b0fd_rle_crop_4038198844_0.png resize: (104, 57) 1395122166 0.5199683768768157 treat image : temp/1763511628_365925_1395083456_9cad6d3dea1416e8f69cdd8ad9b6b0fd_rle_crop_4038198845_0.png resize: (80, 102) 1395122167 -0.11980167143110589 treat image : temp/1763511628_365925_1395083436_3691e1837407fcaaad22a1bdd1812585_rle_crop_4038198846_0.png resize: (200, 113) 1395122168 -1.4866288482924206 treat image : temp/1763511628_365925_1395083433_4873cd265f69414c0a9b98f5da7df1c0_rle_crop_4038198848_0.png resize: (84, 75) 1395122169 -1.4716272404758828 treat image : temp/1763511628_365925_1395083430_eb22b2a3b9f9c8fff756c607b9f9d7d0_rle_crop_4038198851_0.png resize: (161, 72) 1395122170 -1.183298927926759 treat image : temp/1763511628_365925_1395083427_bcfeb55e087a6b1fff385bb7d0f21e2a_rle_crop_4038198854_0.png resize: (147, 69) 1395122171 -1.447664867592403 treat image : temp/1763511628_365925_1395083427_bcfeb55e087a6b1fff385bb7d0f21e2a_rle_crop_4038198856_0.png resize: (134, 128) 1395122172 -1.089699785700569 treat image : temp/1763511628_365925_1395083383_69fbd6e7adbe4ea8508d0e1929734642_rle_crop_4038198860_0.png resize: (104, 132) 1395122173 -0.3921088717517016 treat image : temp/1763511628_365925_1395083355_55df7c0b9d0b6a41c1b36d1cf864f97c_rle_crop_4038198861_0.png resize: (116, 78) 1395122174 -1.1940603254460047 treat image : temp/1763511628_365925_1395083355_55df7c0b9d0b6a41c1b36d1cf864f97c_rle_crop_4038198862_0.png resize: (105, 86) 1395122175 -2.0398006158227706 treat image : temp/1763511628_365925_1395083349_044ef3619deaa12087872dd285f259a3_rle_crop_4038198864_0.png resize: (79, 50) 1395122176 -0.5152223397954948 treat image : temp/1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198866_0.png resize: (115, 143) 1395122177 0.2064304678176099 treat image : temp/1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198868_0.png resize: (104, 112) 1395122178 -1.0580179544884611 treat image : temp/1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198869_0.png resize: (102, 90) 1395122179 3.4426693083169604 treat image : temp/1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63_rle_crop_4038198870_0.png resize: (112, 89) 1395122180 -1.055625808658336 treat image : temp/1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63_rle_crop_4038198871_0.png resize: (49, 56) 1395122181 20.0 treat image : temp/1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63_rle_crop_4038198872_0.png resize: (75, 100) 1395122182 0.05720502635794963 treat image : temp/1763511628_365925_1395083304_b6f62a2f4de153eab6302abe2ffaca63_rle_crop_4038198873_0.png resize: (92, 148) 1395122183 -0.4861223750435771 treat image : temp/1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198790_0.png resize: (82, 48) 1395122188 -0.3370585939067769 treat image : temp/1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198791_0.png resize: (93, 89) 1395122189 -2.179175820976248 treat image : temp/1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504_rle_crop_4038198801_0.png resize: (98, 106) 1395122190 -1.2302883627161423 treat image : temp/1763511628_365925_1395083676_8eae01e12edf6429d1a68d82bf1ba44d_rle_crop_4038198823_0.png resize: (527, 314) 1395122191 0.1660692886066205 treat image : temp/1763511628_365925_1395083430_eb22b2a3b9f9c8fff756c607b9f9d7d0_rle_crop_4038198853_0.png resize: (99, 106) 1395122192 -1.943127471522414 treat image : temp/1763511628_365925_1395083425_1fdc89ebef2fe4a809ab5c833219c462_rle_crop_4038198857_0.png resize: (94, 78) 1395122193 -0.2561018602573284 treat image : temp/1763511628_365925_1395083425_1fdc89ebef2fe4a809ab5c833219c462_rle_crop_4038198859_0.png resize: (65, 67) 1395122194 -1.287043019485693 treat image : temp/1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198826_0.png resize: (81, 81) 1395122195 -1.6911593131931468 treat image : temp/1763511628_365925_1395083355_55df7c0b9d0b6a41c1b36d1cf864f97c_rle_crop_4038198863_0.png resize: (114, 122) 1395122196 -1.635287076121494 treat image : temp/1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab_rle_crop_4038198788_0.png resize: (165, 184) 1395122293 -0.5824732691749166 treat image : temp/1763511628_365925_1395085270_659240d6cd6ba4ba7ed946f15f1af8c9_rle_crop_4038198794_0.png resize: (45, 148) 1395122294 -2.595894916457652 treat image : temp/1763511628_365925_1395085270_659240d6cd6ba4ba7ed946f15f1af8c9_rle_crop_4038198795_0.png resize: (494, 314) 1395122295 -0.2747287213266688 treat image : temp/1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198806_0.png resize: (153, 111) 1395122297 -2.374370620305368 treat image : temp/1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198807_0.png resize: (520, 332) 1395122298 0.47969408081580717 treat image : temp/1763511628_365925_1395084905_d49a975a85fac53ecfaadfaeea3f1fb9_rle_crop_4038198808_0.png resize: (82, 193) 1395122299 -2.57562002650755 treat image : temp/1763511628_365925_1395084529_1d95ff0ffe5ac8882c538d253f40e41e_rle_crop_4038198811_0.png resize: (517, 343) 1395122301 -0.03251666682473713 treat image : temp/1763511628_365925_1395084295_4bcc09700007338f10580143c21b8762_rle_crop_4038198818_0.png resize: (117, 117) 1395122302 -1.1844612655589795 treat image : temp/1763511628_365925_1395083881_0c22a38fd50dfaf9fb273de1bef3aaf9_rle_crop_4038198819_0.png resize: (46, 91) 1395122303 0.3475955904900575 treat image : temp/1763511628_365925_1395083617_227fc66690fc642118d17926b5a6e468_rle_crop_4038198825_0.png resize: (184, 210) 1395122305 -2.859761770216153 treat image : temp/1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198830_0.png resize: (133, 211) 1395122306 -3.1108774040925016 treat image : temp/1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198831_0.png resize: (521, 337) 1395122307 0.32279698101780907 treat image : temp/1763511628_365925_1395083477_b6fe11f685f40d92848a079bce81cce0_rle_crop_4038198839_0.png resize: (82, 80) 1395122309 -0.9900263657950354 treat image : temp/1763511628_365925_1395083477_b6fe11f685f40d92848a079bce81cce0_rle_crop_4038198841_0.png resize: (525, 327) 1395122310 0.1983380031060079 treat image : temp/1763511628_365925_1395083436_3691e1837407fcaaad22a1bdd1812585_rle_crop_4038198847_0.png resize: (137, 95) 1395122311 -1.7915067282073718 treat image : temp/1763511628_365925_1395083433_4873cd265f69414c0a9b98f5da7df1c0_rle_crop_4038198849_0.png resize: (172, 175) 1395122313 -1.4957822118497972 treat image : temp/1763511628_365925_1395083433_4873cd265f69414c0a9b98f5da7df1c0_rle_crop_4038198850_0.png resize: (69, 27) 1395122314 -0.11450715947600867 treat image : temp/1763511628_365925_1395083430_eb22b2a3b9f9c8fff756c607b9f9d7d0_rle_crop_4038198852_0.png resize: (526, 351) 1395122315 0.2399783511650409 treat image : temp/1763511628_365925_1395083427_bcfeb55e087a6b1fff385bb7d0f21e2a_rle_crop_4038198855_0.png resize: (518, 342) 1395122317 0.26186205096317 treat image : temp/1763511628_365925_1395083425_1fdc89ebef2fe4a809ab5c833219c462_rle_crop_4038198858_0.png resize: (525, 354) 1395122318 0.2042906982647326 treat image : temp/1763511628_365925_1395083349_044ef3619deaa12087872dd285f259a3_rle_crop_4038198865_0.png resize: (72, 103) 1395122319 0.006087096938327416 treat image : temp/1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198867_0.png resize: (170, 315) 1395122321 -0.7061213963523993 treat image : temp/1763511628_365925_1395083745_03ec8670a614fccccca74b26be90e0a3_rle_crop_4038198822_0.png resize: (127, 43) 1395122335 -1.0643067983000924 treat image : temp/1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198829_0.png resize: (304, 160) 1395122336 -1.1023614583245414 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 : 126 time used for this insertion : 0.019177913665771484 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 126 time used for this insertion : 0.03470611572265625 save missing photos in datou_result : time spend for datou_step_exec : 30.541868209838867 time spend to save output : 0.05891776084899902 total time spend for step 6 : 30.600785970687866 step7:brightness Wed Nov 19 01:22:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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/1763511628_365925_1395085776_c8d0b8ab13da7f4fc7c62ed7c52d1147.jpg treat image : temp/1763511628_365925_1395085278_a59a8aa8e94720c1b3baccd3e8392f2f.jpg treat image : temp/1763511628_365925_1395085277_4542b51045871966394fa6af6b66886f.jpg treat image : temp/1763511628_365925_1395085274_516cd592ffad87b2b298fff4efebe2ab.jpg treat image : temp/1763511628_365925_1395085270_659240d6cd6ba4ba7ed946f15f1af8c9.jpg treat image : temp/1763511628_365925_1395084955_deefde9f7d9a6e89d29b9820a9129e58.jpg treat image : temp/1763511628_365925_1395084939_d0044eb36bfbf91216d1b0f1e53c2a45.jpg treat image : temp/1763511628_365925_1395084917_5ce81debc948df4518a3017903e5cabd.jpg treat image : temp/1763511628_365925_1395084913_9f250827dd86b0b5754ca5866efa0504.jpg treat image : temp/1763511628_365925_1395084910_8a7eab539f225850acf5ed9b573a368f.jpg treat image : 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temp/1763511628_365925_1395083337_a2e777f0dc2a89766d6f0cc2dd82a528_rle_crop_4038198867_0.png treat image : temp/1763511628_365925_1395083745_03ec8670a614fccccca74b26be90e0a3_rle_crop_4038198822_0.png treat image : temp/1763511628_365925_1395083554_db4129bd2b6f077891a4d485671675d5_rle_crop_4038198829_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 : 126 time used for this insertion : 0.01758885383605957 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 126 time used for this insertion : 0.033751726150512695 save missing photos in datou_result : time spend for datou_step_exec : 8.076608657836914 time spend to save output : 0.05656719207763672 total time spend for step 7 : 8.13317584991455 step8:velours_tree Wed Nov 19 01:22:56 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 : 0.13462591171264648 time spend to save output : 3.743171691894531e-05 total time spend for step 8 : 0.13466334342956543 step9:send_mail_cod Wed Nov 19 01:22:56 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_P28685556_19-11-2025_01_22_56.pdf 28686003 imagette286860031763511776 28686004 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 .imagette286860041763511776 28686005 imagette286860051763511777 28686006 imagette286860061763511777 28686007 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 .imagette286860071763511777 28686008 change filename to text .change filename to text .imagette286860081763511778 28686009 imagette286860091763511778 28686011 imagette286860111763511778 28686012 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 .imagette286860121763511778 28686013 change filename to text .change filename to text .imagette286860131763511780 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=28685556 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/28686003,28686004,28686005,28686006,28686007,28686008,28686009,28686010,28686011,28686012,28686013?tags=flou,carton,mal_croppe,background,papier,metal,pet_fonce,environnement,pehd,pet_clair,autre args[1395085776] : ((1395085776, 2.5183632989086995, 492688767), (1395085776, 0.5670313026823445, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395085278] : ((1395085278, 0.5426287806057146, 492688767), (1395085278, 0.7374636996765571, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395085277] : ((1395085277, -3.8530138977208037, 492609224), (1395085277, 0.5331280998728601, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395085274] : ((1395085274, -0.13372500492072117, 492688767), (1395085274, 0.4374208068881055, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395085270] : ((1395085270, 0.7188211557125053, 492688767), (1395085270, 0.4040250855078333, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084955] : ((1395084955, 1.7784568640572296, 492688767), (1395084955, 1.0661810649504961, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084939] : ((1395084939, 0.05143277458693898, 492688767), (1395084939, 0.7285672813622951, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084917] : ((1395084917, -4.2281773403441925, 492609224), (1395084917, 0.44655432735437917, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084913] : ((1395084913, -0.26764140227663924, 492688767), (1395084913, 0.4825259681257753, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084910] : ((1395084910, 0.5252341125745491, 492688767), (1395084910, 0.4755044633681914, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084905] : ((1395084905, -3.0310736501342346, 492609224), (1395084905, 0.7848629948834849, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084529] : ((1395084529, 2.1660305213833517, 492688767), (1395084529, 0.7087877195114615, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084497] : ((1395084497, -3.938799744947716, 492609224), (1395084497, 0.5167218159802006, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395084295] : ((1395084295, -0.6098513063731087, 492688767), (1395084295, 0.987524518144973, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083881] : ((1395083881, 5.156801840302457, 492688767), (1395083881, 0.6692475299165892, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083810] : ((1395083810, 0.08598656233276217, 492688767), (1395083810, 0.8314871234770869, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083745] : ((1395083745, -3.4345272042075616, 492609224), (1395083745, 0.5221411436564325, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083676] : ((1395083676, -0.28109493796973445, 492688767), (1395083676, 0.3843889716415202, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083617] : ((1395083617, -1.903961185865236, 492688767), (1395083617, 0.7014169514007951, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083554] : ((1395083554, -4.017942597857622, 492609224), (1395083554, 0.47088088603632294, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083486] : ((1395083486, 0.4824289008789361, 492688767), (1395083486, 0.3710086533467031, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083477] : ((1395083477, 0.2175079323414451, 492688767), (1395083477, 0.640762594315764, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083474] : ((1395083474, 0.8854326480866106, 492688767), (1395083474, 0.5024000685112621, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083456] : ((1395083456, 0.11987867731731564, 492688767), (1395083456, 0.6748121509653439, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083455] : ((1395083455, 2.1424096635516885, 492688767), (1395083455, 0.6214463379952434, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083436] : ((1395083436, -0.11716570045315836, 492688767), (1395083436, 0.9022840288981037, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083433] : ((1395083433, 0.38631061488105767, 492688767), (1395083433, 0.7371649144465879, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083430] : ((1395083430, 0.938667006723197, 492688767), (1395083430, 0.4039115186091266, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083427] : ((1395083427, 1.3422070953233238, 492688767), (1395083427, 0.40213140310756307, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083425] : ((1395083425, 0.8809069431381841, 492688767), (1395083425, 0.34650158253554053, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083383] : ((1395083383, -0.014154291492839581, 492688767), (1395083383, 0.7260700111265531, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083355] : ((1395083355, 0.5813021953119787, 492688767), (1395083355, 0.37389294734582823, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083349] : ((1395083349, 0.14845800719951685, 492688767), (1395083349, 0.7616887764096165, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083337] : ((1395083337, 1.2882751986521725, 492688767), (1395083337, 0.5706462741754529, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com args[1395083304] : ((1395083304, 0.5260813506750699, 492688767), (1395083304, 0.8910084390694266, 2107752395), '0.021235890652557324') We are sending mail with results at report@fotonower.com refus_total : 0.021235890652557324 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=28685556 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28685556_19-11-2025_01_22_56.pdf results_Auto_P28685556_19-11-2025_01_22_56.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28685556_19-11-2025_01_22_56.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','28685556','results_Auto_P28685556_19-11-2025_01_22_56.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28685556_19-11-2025_01_22_56.pdf','pdf','','0.38','0.021235890652557324') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/28685556

https://www.fotonower.com/image?json=false&list_photos_id=1395085776
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.5183632989086995)
https://www.fotonower.com/image?json=false&list_photos_id=1395085278
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395085277
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395085274
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395085270
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395084955
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.7784568640572296)
https://www.fotonower.com/image?json=false&list_photos_id=1395084939
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395084917
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395084913
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395084910
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395084905
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395084529
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.1660305213833517)
https://www.fotonower.com/image?json=false&list_photos_id=1395084497
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395084295
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083881
La photo est trop floue, merci de reprendre une photo.(avec le score = 5.156801840302457)
https://www.fotonower.com/image?json=false&list_photos_id=1395083810
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083745
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083676
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083617
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083554
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083486
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083477
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083474
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083456
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083455
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.1424096635516885)
https://www.fotonower.com/image?json=false&list_photos_id=1395083436
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083433
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083430
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083427
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.3422070953233238)
https://www.fotonower.com/image?json=false&list_photos_id=1395083425
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083383
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083355
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083349
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395083337
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.2882751986521725)
https://www.fotonower.com/image?json=false&list_photos_id=1395083304
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 2.12%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/28686004?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/28686007?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/28686008?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/28686012?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/28686013?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28685556_19-11-2025_01_22_56.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/28686003,28686004,28686005,28686006,28686007,28686008,28686009,28686010,28686011,28686012,28686013?tags=flou,carton,mal_croppe,background,papier,metal,pet_fonce,environnement,pehd,pet_clair,autre.


L'équipe Fotonower 202 b'' Date: Wed, 19 Nov 2025 00:23:03 GMT Content-Length: 0 Connection: close Server: nginx X-Message-Id: ESyGgpRtT1K3XO5FMN_QRQ 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 [1395085776, 1395085278, 1395085277, 1395085274, 1395085270, 1395084955, 1395084939, 1395084917, 1395084913, 1395084910, 1395084905, 1395084529, 1395084497, 1395084295, 1395083881, 1395083810, 1395083745, 1395083676, 1395083617, 1395083554, 1395083486, 1395083477, 1395083474, 1395083456, 1395083455, 1395083436, 1395083433, 1395083430, 1395083427, 1395083425, 1395083383, 1395083355, 1395083349, 1395083337, 1395083304] 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, '4072125') ('3318', '28685556', '1395085776', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085278', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085277', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085274', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085270', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084955', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084939', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084917', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084913', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084910', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084905', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084529', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084497', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084295', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083881', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083810', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083745', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083676', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083617', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083554', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083486', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083477', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083474', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083456', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083455', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083436', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083433', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083430', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083427', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083425', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083383', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083355', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083349', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083337', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083304', None, None, None, None, None, '4072125') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 35 time used for this insertion : 0.019965648651123047 save_final save missing photos in datou_result : time spend for datou_step_exec : 6.93187952041626 time spend to save output : 0.020414352416992188 total time spend for step 9 : 6.952293872833252 step10:split_time_score Wed Nov 19 01:23:03 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'}] (('19', 22), ('20', 13)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 18112025 28685556 Nombre de photos uploadées : 35 / 23040 (0%) 18112025 28685556 Nombre de photos taguées (types de déchets): 0 / 35 (0%) 18112025 28685556 Nombre de photos taguées (volume) : 0 / 35 (0%) elapsed_time : load_data_split_time_score 2.384185791015625e-06 elapsed_time : order_list_meta_photo_and_scores 5.4836273193359375e-06 ??????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0015652179718017578 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.20800256729125977 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.10302089006899057 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28650767_18-11-2025_09_31_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28650767 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`=28650767 AND mptpi.`type`=3594 To do Qualite : 0.19006823881172832 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28657578_18-11-2025_14_52_17.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28657578 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`=28657578 AND mptpi.`type`=3594 To do Qualite : 0.20229590647505133 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28657580_18-11-2025_13_33_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28657580 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`=28657580 AND mptpi.`type`=3594 To do Qualite : 0.07675022628798672 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28657587_18-11-2025_12_52_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28657587 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`=28657587 AND mptpi.`type`=3594 To do Qualite : 0.0032867270171957675 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28666729_18-11-2025_18_31_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28666729 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`=28666729 AND mptpi.`type`=3594 To do Qualite : 0.13427932098765435 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28666743_18-11-2025_17_11_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28666743 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`=28666743 AND mptpi.`type`=3594 To do Qualite : 0.09589124754489341 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28676890_18-11-2025_23_31_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28676890 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`=28676890 AND mptpi.`type`=3594 To do Qualite : 0.021235890652557324 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28685556_19-11-2025_01_22_56.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28685556 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`=28685556 AND mptpi.`type`=3594 To do Qualite : 0.14069093088624338 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28685558_19-11-2025_01_12_06.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28685558 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`=28685558 AND mptpi.`type`=3594 To do Qualite : 0.07281703317901234 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28677782_18-11-2025_21_42_02.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28677782 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`=28677782 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'18112025': {'nb_upload': 35, '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 [1395085776, 1395085278, 1395085277, 1395085274, 1395085270, 1395084955, 1395084939, 1395084917, 1395084913, 1395084910, 1395084905, 1395084529, 1395084497, 1395084295, 1395083881, 1395083810, 1395083745, 1395083676, 1395083617, 1395083554, 1395083486, 1395083477, 1395083474, 1395083456, 1395083455, 1395083436, 1395083433, 1395083430, 1395083427, 1395083425, 1395083383, 1395083355, 1395083349, 1395083337, 1395083304] Looping around the photos to save general results len do output : 1 /28685556Didn'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, '4072125') ('3318', '28685556', '1395085776', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085278', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085277', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085274', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395085270', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084955', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084939', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084917', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084913', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084910', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084905', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084529', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084497', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395084295', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083881', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083810', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083745', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083676', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083617', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083554', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083486', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083477', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083474', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083456', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083455', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083436', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083433', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083430', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083427', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083425', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083383', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083355', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083349', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083337', None, None, None, None, None, '4072125') ('3318', None, None, None, None, None, None, None, '4072125') ('3318', '28685556', '1395083304', None, None, None, None, None, '4072125') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.020279407501220703 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.9707062244415283 time spend to save output : 0.020540237426757812 total time spend for step 10 : 0.9912464618682861 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 35 set_done_treatment 83.32user 43.87system 2:39.19elapsed 79%CPU (0avgtext+0avgdata 3370940maxresident)k 1910408inputs+28096outputs (7549major+2887319minor)pagefaults 0swaps