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 : 3675541 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 : ['4102347'] with mtr_portfolio_ids : ['28828426'] and first list_photo_ids : [] new path : /proc/3675541/ 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 , WARNING: data may be incomplete, need to offset and complete ! BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 40 ; length of list_pids : 40 ; length of list_args : 40 time to download the photos : 5.8472089767456055 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 Mon Nov 24 14:00:36 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 : 10998 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-11-24 14:00:40.350876: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-11-24 14:00:40.386519: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-11-24 14:00:40.388807: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f8a2c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-11-24 14:00:40.388860: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-11-24 14:00:40.393352: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-11-24 14:00:40.766580: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3dcdff00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-11-24 14:00:40.766635: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-11-24 14:00:40.768651: 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 2025-11-24 14:00:40.770495: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-24 14:00:40.802224: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-24 14:00:40.821672: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-24 14:00:40.825923: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-24 14:00:40.858343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-24 14:00:40.862806: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-24 14:00:40.917820: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-24 14:00:40.919674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-24 14:00:40.920050: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-24 14:00:40.921673: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-24 14:00:40.921693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-24 14:00:40.921714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-24 14:00:40.923783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 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. 2025-11-24 14:00:41.380306: 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 2025-11-24 14:00:41.380387: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-24 14:00:41.380408: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-24 14:00:41.380426: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-24 14:00:41.380444: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-24 14:00:41.380462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-24 14:00:41.380479: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-24 14:00:41.380496: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-24 14:00:41.382102: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-24 14:00:41.383582: 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 2025-11-24 14:00:41.383618: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-24 14:00:41.383634: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-24 14:00:41.383648: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-24 14:00:41.383662: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-24 14:00:41.383675: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-24 14:00:41.383688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-24 14:00:41.383702: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-24 14:00:41.384991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-24 14:00:41.385022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-24 14:00:41.385030: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-24 14:00:41.385038: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-24 14:00:41.386383: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 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 : [] 2025-11-24 14:00:46.627175: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 51380224 exceeds 10% of free system memory. 2025-11-24 14:00:51.183294: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-24 14:00:51.591504: 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 : 40 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 23.00000 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 : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 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: 32.00000 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: 32.00000 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: 29.00000 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: 19.00000 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: 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: 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 : 7 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 : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 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: 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 : 4 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 : 6 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 : 4 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 : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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: 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 : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 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: 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: 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 : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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 : 1 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 : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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: 26.00000 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: 32.00000 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: 32.00000 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 : 3 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 : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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: 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 : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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 : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 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: 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 : 4 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: 34.00000 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: 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 : 4 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 : 4 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 : 7 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 : 6 Detection mask done ! Trying to reset tf kernel 3677303 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5706 tf kernel not reseted sub process len(results) : 40 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 40 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 : 10998 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'] DEBUG bbox = [309, 1077, 423, 1188] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0006835460662841797 nb_pixel_total : 7741 time to create 1 rle with old method : 0.014118432998657227 length of segment : 106 DEBUG bbox = [0, 0, 1032, 1203] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.01610708236694336 nb_pixel_total : 691731 time to create 1 rle with new method : 0.3081841468811035 length of segment : 1239 DEBUG bbox = [477, 1521, 1056, 1911] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00185394287109375 nb_pixel_total : 118301 time to create 1 rle with old method : 0.12959051132202148 length of segment : 554 DEBUG bbox = [3, 1323, 120, 1431] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.000152587890625 nb_pixel_total : 7241 time to create 1 rle with old method : 0.00833582878112793 length of segment : 112 DEBUG bbox = [0, 0, 1032, 1677] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0162661075592041 nb_pixel_total : 1481791 time to create 1 rle with new method : 0.0662074089050293 length of segment : 1285 DEBUG bbox = [90, 1398, 357, 1665] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0005068778991699219 nb_pixel_total : 30065 time to create 1 rle with old method : 0.032105445861816406 length of segment : 218 DEBUG bbox = [714, 1209, 822, 1320] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00013971328735351562 nb_pixel_total : 8772 time to create 1 rle with old method : 0.009692668914794922 length of segment : 100 DEBUG bbox = [477, 1593, 654, 1713] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00020122528076171875 nb_pixel_total : 12891 time to create 1 rle with old method : 0.014090776443481445 length of segment : 163 DEBUG bbox = [309, 1077, 429, 1185] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001366138458251953 nb_pixel_total : 8582 time to create 1 rle with old method : 0.009832620620727539 length of segment : 111 DEBUG bbox = [0, 1317, 186, 1431] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00018525123596191406 nb_pixel_total : 11929 time to create 1 rle with old method : 0.013327360153198242 length of segment : 185 DEBUG bbox = [3, 1674, 204, 1881] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00038814544677734375 nb_pixel_total : 30798 time to create 1 rle with old method : 0.034621477127075195 length of segment : 198 DEBUG bbox = [12, 1524, 156, 1650] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00019812583923339844 nb_pixel_total : 10109 time to create 1 rle with old method : 0.011670351028442383 length of segment : 143 DEBUG bbox = [900, 747, 1059, 897] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00021648406982421875 nb_pixel_total : 15393 time to create 1 rle with old method : 0.017528772354125977 length of segment : 158 DEBUG bbox = [921, 642, 1050, 714] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001277923583984375 nb_pixel_total : 7540 time to create 1 rle with old method : 0.0086517333984375 length of segment : 125 DEBUG bbox = [306, 1080, 423, 1173] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001201629638671875 nb_pixel_total : 7695 time to create 1 rle with old method : 0.008791923522949219 length of segment : 108 DEBUG bbox = [966, 906, 1080, 1047] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00014472007751464844 nb_pixel_total : 6975 time to create 1 rle with old method : 0.008121252059936523 length of segment : 111 DEBUG bbox = [0, 0, 975, 1719] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.5637497901916504 nb_pixel_total : 1489993 time to create 1 rle with new method : 0.11465644836425781 length of segment : 1200 DEBUG bbox = [942, 444, 1065, 579] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00035190582275390625 nb_pixel_total : 12658 time to create 1 rle with old method : 0.014190435409545898 length of segment : 120 DEBUG bbox = [639, 1284, 699, 1353] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 2825 time to create 1 rle with old method : 0.0033571720123291016 length of segment : 54 DEBUG bbox = [522, 1527, 1050, 1890] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0017218589782714844 nb_pixel_total : 101010 time to create 1 rle with old method : 0.11197161674499512 length of segment : 505 DEBUG bbox = [507, 1449, 585, 1560] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00020956993103027344 nb_pixel_total : 5776 time to create 1 rle with old method : 0.0070955753326416016 length of segment : 75 DEBUG bbox = [384, 1221, 480, 1314] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002589225769042969 nb_pixel_total : 4338 time to create 1 rle with old method : 0.005811452865600586 length of segment : 90 DEBUG bbox = [318, 1080, 426, 1179] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00021409988403320312 nb_pixel_total : 6973 time to create 1 rle with old method : 0.008054971694946289 length of segment : 104 DEBUG bbox = [0, 90, 1008, 1509] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.018009185791015625 nb_pixel_total : 1281010 time to create 1 rle with new method : 0.04869675636291504 length of segment : 1346 DEBUG bbox = [420, 957, 543, 1005] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002663135528564453 nb_pixel_total : 3356 time to create 1 rle with old method : 0.004004478454589844 length of segment : 118 DEBUG bbox = [201, 1167, 306, 1302] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003120899200439453 nb_pixel_total : 12285 time to create 1 rle with old method : 0.013900041580200195 length of segment : 104 DEBUG bbox = [495, 1500, 1080, 1902] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0023424625396728516 nb_pixel_total : 107624 time to create 1 rle with old method : 0.12106943130493164 length of segment : 541 DEBUG bbox = [0, 0, 978, 1518] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.04781341552734375 nb_pixel_total : 1274625 time to create 1 rle with new method : 0.31082606315612793 length of segment : 1332 DEBUG bbox = [732, 1263, 969, 1416] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0007028579711914062 nb_pixel_total : 17467 time to create 1 rle with old method : 0.019060850143432617 length of segment : 214 DEBUG bbox = [270, 1380, 360, 1587] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00032711029052734375 nb_pixel_total : 12752 time to create 1 rle with old method : 0.014560461044311523 length of segment : 87 DEBUG bbox = [153, 1638, 288, 1854] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00045990943908691406 nb_pixel_total : 20069 time to create 1 rle with old method : 0.021909475326538086 length of segment : 127 DEBUG bbox = [609, 873, 687, 951] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00019431114196777344 nb_pixel_total : 3797 time to create 1 rle with old method : 0.00420069694519043 length of segment : 69 DEBUG bbox = [489, 1467, 579, 1557] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00021147727966308594 nb_pixel_total : 4985 time to create 1 rle with old method : 0.00558161735534668 length of segment : 79 DEBUG bbox = [495, 1521, 1080, 1920] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.003431558609008789 nb_pixel_total : 126285 time to create 1 rle with old method : 0.13035297393798828 length of segment : 560 DEBUG bbox = [489, 1464, 582, 1560] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002574920654296875 nb_pixel_total : 5584 time to create 1 rle with old method : 0.006145477294921875 length of segment : 80 DEBUG bbox = [312, 1080, 420, 1179] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002913475036621094 nb_pixel_total : 7139 time to create 1 rle with old method : 0.007639169692993164 length of segment : 101 DEBUG bbox = [0, 0, 1032, 1716] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.07011294364929199 nb_pixel_total : 1378479 time to create 1 rle with new method : 0.27088069915771484 length of segment : 1562 DEBUG bbox = [792, 1215, 954, 1368] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00034928321838378906 nb_pixel_total : 11551 time to create 1 rle with old method : 0.013343572616577148 length of segment : 173 DEBUG bbox = [882, 801, 1011, 912] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00015616416931152344 nb_pixel_total : 6469 time to create 1 rle with old method : 0.0072863101959228516 length of segment : 108 DEBUG bbox = [498, 1518, 1080, 1887] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0016064643859863281 nb_pixel_total : 99654 time to create 1 rle with old method : 0.11658263206481934 length of segment : 544 DEBUG bbox = [81, 1308, 210, 1467] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00032019615173339844 nb_pixel_total : 12967 time to create 1 rle with old method : 0.014578104019165039 length of segment : 116 DEBUG bbox = [6, 1242, 75, 1317] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001552104949951172 nb_pixel_total : 3921 time to create 1 rle with old method : 0.0049784183502197266 length of segment : 59 DEBUG bbox = [489, 1455, 591, 1563] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0002257823944091797 nb_pixel_total : 6042 time to create 1 rle with old method : 0.007611989974975586 length of segment : 88 DEBUG bbox = [3, 1386, 60, 1500] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00015807151794433594 nb_pixel_total : 3862 time to create 1 rle with old method : 0.004601478576660156 length of segment : 50 DEBUG bbox = [303, 1083, 444, 1233] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003669261932373047 nb_pixel_total : 9172 time to create 1 rle with old method : 0.010609865188598633 length of segment : 121 DEBUG bbox = [300, 1089, 426, 1188] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00027489662170410156 nb_pixel_total : 8319 time to create 1 rle with old method : 0.009856224060058594 length of segment : 116 DEBUG bbox = [177, 1332, 432, 1512] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0006494522094726562 nb_pixel_total : 15291 time to create 1 rle with old method : 0.017795324325561523 length of segment : 208 DEBUG bbox = [300, 1077, 438, 1194] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003230571746826172 nb_pixel_total : 9075 time to create 1 rle with old method : 0.010513067245483398 length of segment : 116 DEBUG bbox = [486, 879, 534, 909] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00013828277587890625 nb_pixel_total : 904 time to create 1 rle with old method : 0.0011048316955566406 length of segment : 47 DEBUG bbox = [291, 1104, 432, 1197] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00028395652770996094 nb_pixel_total : 6984 time to create 1 rle with old method : 0.008124828338623047 length of segment : 119 DEBUG bbox = [3, 1227, 60, 1314] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00014400482177734375 nb_pixel_total : 4005 time to create 1 rle with old method : 0.0048274993896484375 length of segment : 52 DEBUG bbox = [990, 681, 1074, 762] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0001862049102783203 nb_pixel_total : 4300 time to create 1 rle with old method : 0.0050122737884521484 length of segment : 108 DEBUG bbox = [465, 1509, 1080, 1920] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0026378631591796875 nb_pixel_total : 132623 time to create 1 rle with old method : 0.1452324390411377 length of segment : 574 DEBUG bbox = [30, 6, 981, 1659] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.015514850616455078 nb_pixel_total : 1053325 time to create 1 rle with new method : 0.07236862182617188 length of segment : 1479 DEBUG bbox = [303, 1086, 429, 1191] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0003101825714111328 nb_pixel_total : 8623 time to create 1 rle with old method : 0.009769916534423828 length of segment : 118 DEBUG bbox = [318, 1059, 486, 1227] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0005424022674560547 nb_pixel_total : 16701 time to create 1 rle with old method : 0.027579307556152344 length of segment : 186 DEBUG bbox = [0, 1254, 69, 1329] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00019598007202148438 nb_pixel_total : 3108 time to create 1 rle with old method : 0.005223989486694336 length of segment : 66 DEBUG bbox = [21, 1401, 198, 1572] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0005781650543212891 nb_pixel_total : 22399 time to create 1 rle with old method : 0.025667905807495117 length of segment : 171 DEBUG bbox = [6, 1305, 192, 1407] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.0004279613494873047 nb_pixel_total : 12437 time to create 1 rle with old method : 0.013887405395507812 length of segment : 184 DEBUG bbox = [321, 1098, 426, 1182] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.00022292137145996094 nb_pixel_total : 5613 time to create 1 rle with old method : 0.006608724594116211 length of segment : 97 DEBUG bbox = [6, 504, 1047, 1773] DEBUG masks shape = (1080, 1920) time for calcul the mask position with numpy : 0.03686952590942383 nb_pixel_total : 977229 time to create 1 rle with new method : 0.04799485206604004 length of segment : 1512 time spent for convertir_results : 7.1786439418792725 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 61 chid ids of type : 3594 Number RLEs to save : 19796 save missing photos in datou_result : time spend for datou_step_exec : 47.95907759666443 time spend to save output : 1.7598259449005127 total time spend for step 1 : 49.71890354156494 step2:crop_condition Mon Nov 24 14:01:26 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 : 40 ! batch 1 Loaded 61 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 ! map_result returned by crop_photo_return_map_crop : length : 12 About to insert : list_path_to_insert length 12 new photo from crops ! About to upload 12 photos upload in portfolio : 3736932 init cache_photo without model_param we have 12 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763989290_3675541 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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872773_a006a3a7ba961a4011d86fd38b20a1bf_rle_crop_4043689412_0.png', 0, 93, 106, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872773_a006a3a7ba961a4011d86fd38b20a1bf_rle_crop_4043689413_0.png', 0, 1161, 1010, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872761_f2f1ffdff96383cf14b4d503f2aba28e_rle_crop_4043689418_0.png', 0, 101, 100, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689426_0.png', 0, 89, 107, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872712_064431273f0adaf893de8424dc15512e_rle_crop_4043689433_0.png', 0, 82, 90, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872706_e357cf5d1c229f74016ea4aee45d5f07_rle_crop_4043689436_0.png', 0, 47, 116, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872698_bdaad0d64375723456764ae88bab5f36_rle_crop_4043689444_0.png', 0, 88, 79, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689446_0.png', 0, 91, 80, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689447_0.png', 0, 87, 101, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872656_4b30e205695e3072c00b0e0394daf227_rle_crop_4043689458_0.png', 0, 174, 201, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872651_ee01ae3fdaa496bc53f9f24cea92c1db_rle_crop_4043689460_0.png', 0, 27, 47, 0, 1763989292,'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(1763989292), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872643_0519009d0ef50d5bfdb09a870b3c82dc_rle_crop_4043689463_0.png', 0, 75, 83, 0, 1763989292,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 12 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.682694673538208 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 14 About to insert : list_path_to_insert length 14 new photo from crops ! About to upload 14 photos upload in portfolio : 3736932 init cache_photo without model_param we have 14 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763989295_3675541 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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96_rle_crop_4043689414_0.png', 0, 365, 553, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872761_f2f1ffdff96383cf14b4d503f2aba28e_rle_crop_4043689419_0.png', 0, 102, 161, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872759_cfa5ebc9b5429363ef0dac2b3f718a3a_rle_crop_4043689420_0.png', 0, 99, 110, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872719_4235713827b087d22e7b3b4274299445_rle_crop_4043689432_0.png', 0, 98, 75, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872712_064431273f0adaf893de8424dc15512e_rle_crop_4043689434_0.png', 0, 84, 104, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689451_0.png', 0, 343, 536, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872672_17ab726beae4e0b3e0e4b44294a49040_rle_crop_4043689454_0.png', 0, 96, 84, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872671_08ea492908ea2ebb5503a08f664c2ef0_rle_crop_4043689456_0.png', 0, 119, 116, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872658_253dd165ecabc9cfe676e8c3b33bb67c_rle_crop_4043689457_0.png', 0, 94, 116, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872654_504a302108a3eed78026e85e4bcdfcb5_rle_crop_4043689459_0.png', 0, 102, 115, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872647_982295c309035a3f9362b8a6a91ab6e9_rle_crop_4043689461_0.png', 0, 79, 119, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872615_a0df0666ddd5ad42b60a40ec470b5688_rle_crop_4043689466_0.png', 0, 97, 118, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872606_e5d76289d06bf0f47ba71e7ac68e7c67_rle_crop_4043689467_0.png', 0, 165, 164, 0, 1763989297,'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(1763989297), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872599_ee62aaba977cdd4442f3736b49f8de7f_rle_crop_4043689471_0.png', 0, 80, 96, 0, 1763989297,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 14 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.093001127243042 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 begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 33 About to insert : list_path_to_insert length 33 new photo from crops ! About to upload 33 photos upload in portfolio : 3736932 init cache_photo without model_param we have 33 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1763989336_3675541 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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96_rle_crop_4043689415_0.png', 0, 97, 112, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96_rle_crop_4043689416_0.png', 0, 1637, 1004, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872761_f2f1ffdff96383cf14b4d503f2aba28e_rle_crop_4043689417_0.png', 0, 211, 217, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872759_cfa5ebc9b5429363ef0dac2b3f718a3a_rle_crop_4043689421_0.png', 0, 113, 176, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872756_55e36efe4a99865b550b4f85a9b63a0c_rle_crop_4043689422_0.png', 0, 205, 198, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872756_55e36efe4a99865b550b4f85a9b63a0c_rle_crop_4043689423_0.png', 0, 105, 143, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689424_0.png', 0, 145, 156, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689425_0.png', 0, 69, 125, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689427_0.png', 0, 120, 105, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689428_0.png', 0, 1688, 952, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689429_0.png', 0, 128, 119, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689431_0.png', 0, 352, 504, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872708_a46fe06d2dcff1181d4c9b28a5d01cfc_rle_crop_4043689435_0.png', 0, 1416, 977, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872704_2c8b6266ad7dc134d4b19e108829c6fe_rle_crop_4043689438_0.png', 0, 359, 535, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872704_2c8b6266ad7dc134d4b19e108829c6fe_rle_crop_4043689439_0.png', 0, 1485, 948, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7_rle_crop_4043689440_0.png', 0, 129, 214, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7_rle_crop_4043689441_0.png', 0, 202, 78, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7_rle_crop_4043689442_0.png', 0, 213, 121, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7_rle_crop_4043689443_0.png', 0, 65, 68, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872698_bdaad0d64375723456764ae88bab5f36_rle_crop_4043689445_0.png', 0, 383, 554, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689448_0.png', 0, 1672, 990, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689449_0.png', 0, 142, 149, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689450_0.png', 0, 85, 105, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872680_9e35c66ebf6e39096e3bb04029d94d0a_rle_crop_4043689452_0.png', 0, 152, 115, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872678_c1a09c5b7cdbe37581eaff51ac07160f_rle_crop_4043689453_0.png', 0, 75, 59, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872672_17ab726beae4e0b3e0e4b44294a49040_rle_crop_4043689455_0.png', 0, 100, 50, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872645_e3f87371b3b40ad00570860417e07cf5_rle_crop_4043689462_0.png', 0, 86, 51, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872641_bc4c247756caf514ff007c9c847f4acc_rle_crop_4043689464_0.png', 0, 382, 568, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872641_bc4c247756caf514ff007c9c847f4acc_rle_crop_4043689465_0.png', 0, 1630, 948, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872606_e5d76289d06bf0f47ba71e7ac68e7c67_rle_crop_4043689468_0.png', 0, 67, 65, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872605_7f80f108968b000b0ff33ce15c4c551a_rle_crop_4043689469_0.png', 0, 171, 170, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872599_ee62aaba977cdd4442f3736b49f8de7f_rle_crop_4043689470_0.png', 0, 97, 159, 0, 1763989342,'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(1763989342), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872597_f88a635f71a23986a06d351ef8ed4fa5_rle_crop_4043689472_0.png', 0, 1268, 984, 0, 1763989342,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 33 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.60375714302063 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/1763989345_3675541 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(1763989346), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689430_0.png', 0, 65, 54, 0, 1763989346,'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(1763989346), 0.0, 0.0, 14, '', 0, 0, '1763989230_3675541_1395872706_e357cf5d1c229f74016ea4aee45d5f07_rle_crop_4043689437_0.png', 0, 135, 104, 0, 1763989346,'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 : 1.3075249195098877 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1395872773, 1395872765, 1395872763, 1395872761, 1395872759, 1395872756, 1395872754, 1395872719, 1395872712, 1395872708, 1395872706, 1395872704, 1395872703, 1395872702, 1395872701, 1395872700, 1395872698, 1395872692, 1395872680, 1395872678, 1395872676, 1395872674, 1395872672, 1395872671, 1395872658, 1395872656, 1395872654, 1395872652, 1395872651, 1395872647, 1395872645, 1395872643, 1395872641, 1395872615, 1395872606, 1395872605, 1395872603, 1395872601, 1395872599, 1395872597] Looping around the photos to save general results len do output : 61 /1395900541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1395900884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872773', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872765', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872763', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872761', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872759', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872756', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872754', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872719', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872712', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872708', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872706', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872704', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872703', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872702', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872701', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872700', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872698', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872692', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872680', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872678', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872676', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872674', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872672', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872671', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872658', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872656', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872654', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872652', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872651', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872647', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872645', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872643', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872641', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872615', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872606', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872605', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872603', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872601', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872599', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872597', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 223 time used for this insertion : 0.02778005599975586 save_final save missing photos in datou_result : time spend for datou_step_exec : 60.54733419418335 time spend to save output : 0.03067493438720703 total time spend for step 2 : 60.57800912857056 step3:rle_unique_nms_with_priority Mon Nov 24 14:02:26 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 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 61 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.11725974082946777 time for calcul the mask position with numpy : 0.05749845504760742 nb_pixel_total : 1381869 time to create 1 rle with new method : 0.18247103691101074 time for calcul the mask position with numpy : 0.012779951095581055 nb_pixel_total : 683990 time to create 1 rle with new method : 0.10395002365112305 time for calcul the mask position with numpy : 0.006269931793212891 nb_pixel_total : 7741 time to create 1 rle with old method : 0.008741140365600586 create new chi : 0.38619208335876465 time to delete rle : 0.03204154968261719 batch 1 Loaded 5 chid ids of type : 3594 +++Number RLEs to save : 3770 TO DO : save crop sub photo not yet done ! save time : 0.304903507232666 No data in photo_id : 1395872765 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.20702648162841797 time for calcul the mask position with numpy : 0.010847806930541992 nb_pixel_total : 471881 time to create 1 rle with new method : 0.09794139862060547 time for calcul the mask position with numpy : 0.07559537887573242 nb_pixel_total : 1476177 time to create 1 rle with new method : 0.08966445922851562 time for calcul the mask position with numpy : 0.007823467254638672 nb_pixel_total : 7241 time to create 1 rle with old method : 0.01177668571472168 time for calcul the mask position with numpy : 0.009469032287597656 nb_pixel_total : 118301 time to create 1 rle with old method : 0.14988255500793457 create new chi : 0.47278594970703125 time to delete rle : 0.0009486675262451172 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 4981 TO DO : save crop sub photo not yet done ! save time : 0.36163949966430664 nb_obj : 3 nb_hashtags : 3 time to prepare the origin masks : 0.08574676513671875 time for calcul the mask position with numpy : 0.12501811981201172 nb_pixel_total : 2021872 time to create 1 rle with new method : 0.1104886531829834 time for calcul the mask position with numpy : 0.0062255859375 nb_pixel_total : 12891 time to create 1 rle with old method : 0.014183521270751953 time for calcul the mask position with numpy : 0.006306886672973633 nb_pixel_total : 8772 time to create 1 rle with old method : 0.009676694869995117 time for calcul the mask position with numpy : 0.006517171859741211 nb_pixel_total : 30065 time to create 1 rle with old method : 0.03324437141418457 create new chi : 0.32192373275756836 time to delete rle : 0.0003974437713623047 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2042 TO DO : save crop sub photo not yet done ! save time : 0.19111919403076172 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.039849042892456055 time for calcul the mask position with numpy : 0.0747840404510498 nb_pixel_total : 2053089 time to create 1 rle with new method : 0.15804433822631836 time for calcul the mask position with numpy : 0.006758928298950195 nb_pixel_total : 11929 time to create 1 rle with old method : 0.013355016708374023 time for calcul the mask position with numpy : 0.006920337677001953 nb_pixel_total : 8582 time to create 1 rle with old method : 0.009700298309326172 create new chi : 0.2810842990875244 time to delete rle : 0.0003082752227783203 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1672 TO DO : save crop sub photo not yet done ! save time : 0.16240572929382324 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.048671722412109375 time for calcul the mask position with numpy : 0.06195974349975586 nb_pixel_total : 2032693 time to create 1 rle with new method : 0.08810973167419434 time for calcul the mask position with numpy : 0.006111860275268555 nb_pixel_total : 10109 time to create 1 rle with old method : 0.011181831359863281 time for calcul the mask position with numpy : 0.006135225296020508 nb_pixel_total : 30798 time to create 1 rle with old method : 0.03374600410461426 create new chi : 0.21844744682312012 time to delete rle : 0.00034427642822265625 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1762 TO DO : save crop sub photo not yet done ! save time : 0.1620185375213623 nb_obj : 7 nb_hashtags : 2 time to prepare the origin masks : 0.47631359100341797 time for calcul the mask position with numpy : 0.009005546569824219 nb_pixel_total : 445097 time to create 1 rle with new method : 0.12242627143859863 time for calcul the mask position with numpy : 0.00647735595703125 nb_pixel_total : 101010 time to create 1 rle with old method : 0.11281085014343262 time for calcul the mask position with numpy : 0.006152629852294922 nb_pixel_total : 12658 time to create 1 rle with old method : 0.013683319091796875 time for calcul the mask position with numpy : 0.046723365783691406 nb_pixel_total : 1477232 time to create 1 rle with new method : 0.08625197410583496 time for calcul the mask position with numpy : 0.006433725357055664 nb_pixel_total : 6975 time to create 1 rle with old method : 0.008149385452270508 time for calcul the mask position with numpy : 0.00632929801940918 nb_pixel_total : 7695 time to create 1 rle with old method : 0.008564233779907227 time for calcul the mask position with numpy : 0.006451606750488281 nb_pixel_total : 7540 time to create 1 rle with old method : 0.008228063583374023 time for calcul the mask position with numpy : 0.00635528564453125 nb_pixel_total : 15393 time to create 1 rle with old method : 0.016677141189575195 create new chi : 0.4859621524810791 time to delete rle : 0.0008485317230224609 batch 1 Loaded 16 chid ids of type : 3594 +++++++++++Number RLEs to save : 5722 TO DO : save crop sub photo not yet done ! save time : 0.42656707763671875 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03824639320373535 time for calcul the mask position with numpy : 0.023982524871826172 nb_pixel_total : 2067824 time to create 1 rle with new method : 0.030109882354736328 time for calcul the mask position with numpy : 0.00635218620300293 nb_pixel_total : 5776 time to create 1 rle with old method : 0.00644373893737793 create new chi : 0.06713104248046875 time to delete rle : 0.00027298927307128906 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1230 TO DO : save crop sub photo not yet done ! save time : 0.14228129386901855 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.051660776138305664 time for calcul the mask position with numpy : 0.16351318359375 nb_pixel_total : 2062289 time to create 1 rle with new method : 0.08378195762634277 time for calcul the mask position with numpy : 0.006250858306884766 nb_pixel_total : 6973 time to create 1 rle with old method : 0.007651805877685547 time for calcul the mask position with numpy : 0.006315946578979492 nb_pixel_total : 4338 time to create 1 rle with old method : 0.004785776138305664 create new chi : 0.28448057174682617 time to delete rle : 0.00026869773864746094 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1468 TO DO : save crop sub photo not yet done ! save time : 0.14733648300170898 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03853464126586914 time for calcul the mask position with numpy : 0.012029886245727539 nb_pixel_total : 792590 time to create 1 rle with new method : 0.15229582786560059 time for calcul the mask position with numpy : 0.01639723777770996 nb_pixel_total : 1281010 time to create 1 rle with new method : 0.03316211700439453 create new chi : 0.2143857479095459 time to delete rle : 0.00042557716369628906 batch 1 Loaded 3 chid ids of type : 3594 +++Number RLEs to save : 3772 TO DO : save crop sub photo not yet done ! save time : 0.26100826263427734 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.03960299491882324 time for calcul the mask position with numpy : 0.019255638122558594 nb_pixel_total : 2057959 time to create 1 rle with new method : 0.09424614906311035 time for calcul the mask position with numpy : 0.006221294403076172 nb_pixel_total : 12285 time to create 1 rle with old method : 0.013369560241699219 time for calcul the mask position with numpy : 0.006196498870849609 nb_pixel_total : 3356 time to create 1 rle with old method : 0.003725290298461914 create new chi : 0.15282964706420898 time to delete rle : 0.0005576610565185547 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1524 TO DO : save crop sub photo not yet done ! save time : 0.1576094627380371 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.07065367698669434 time for calcul the mask position with numpy : 0.013468027114868164 nb_pixel_total : 691351 time to create 1 rle with new method : 0.07888174057006836 time for calcul the mask position with numpy : 0.014638662338256836 nb_pixel_total : 1274625 time to create 1 rle with new method : 0.13080906867980957 time for calcul the mask position with numpy : 0.006647348403930664 nb_pixel_total : 107624 time to create 1 rle with old method : 0.11790227890014648 create new chi : 0.37589049339294434 time to delete rle : 0.0006787776947021484 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 4826 TO DO : save crop sub photo not yet done ! save time : 0.3334536552429199 nb_obj : 4 nb_hashtags : 1 time to prepare the origin masks : 0.10908317565917969 time for calcul the mask position with numpy : 0.4083995819091797 nb_pixel_total : 2019515 time to create 1 rle with new method : 0.09137845039367676 time for calcul the mask position with numpy : 0.005892753601074219 nb_pixel_total : 3797 time to create 1 rle with old method : 0.004122734069824219 time for calcul the mask position with numpy : 0.005966663360595703 nb_pixel_total : 20069 time to create 1 rle with old method : 0.021570205688476562 time for calcul the mask position with numpy : 0.0060977935791015625 nb_pixel_total : 12752 time to create 1 rle with old method : 0.013842105865478516 time for calcul the mask position with numpy : 0.006381034851074219 nb_pixel_total : 17467 time to create 1 rle with old method : 0.018428802490234375 create new chi : 0.5914096832275391 time to delete rle : 0.00035834312438964844 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2074 TO DO : save crop sub photo not yet done ! save time : 0.20213675498962402 No data in photo_id : 1395872702 No data in photo_id : 1395872701 No data in photo_id : 1395872700 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.04286336898803711 time for calcul the mask position with numpy : 0.06075334548950195 nb_pixel_total : 1942330 time to create 1 rle with new method : 0.08835148811340332 time for calcul the mask position with numpy : 0.0068988800048828125 nb_pixel_total : 126285 time to create 1 rle with old method : 0.13909339904785156 time for calcul the mask position with numpy : 0.006143808364868164 nb_pixel_total : 4985 time to create 1 rle with old method : 0.005594015121459961 create new chi : 0.3147590160369873 time to delete rle : 0.00039267539978027344 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2358 TO DO : save crop sub photo not yet done ! save time : 0.2024984359741211 nb_obj : 5 nb_hashtags : 3 time to prepare the origin masks : 0.1596207618713379 time for calcul the mask position with numpy : 0.010560035705566406 nb_pixel_total : 588998 time to create 1 rle with new method : 0.09362363815307617 time for calcul the mask position with numpy : 0.0065462589263916016 nb_pixel_total : 99654 time to create 1 rle with old method : 0.10756683349609375 time for calcul the mask position with numpy : 0.006360292434692383 nb_pixel_total : 6469 time to create 1 rle with old method : 0.006815671920776367 time for calcul the mask position with numpy : 0.04261183738708496 nb_pixel_total : 1365756 time to create 1 rle with new method : 0.07283306121826172 time for calcul the mask position with numpy : 0.0062830448150634766 nb_pixel_total : 7139 time to create 1 rle with old method : 0.007901906967163086 time for calcul the mask position with numpy : 0.005836009979248047 nb_pixel_total : 5584 time to create 1 rle with old method : 0.006015777587890625 create new chi : 0.38642406463623047 time to delete rle : 0.0009369850158691406 batch 1 Loaded 12 chid ids of type : 3594 +++++++++Number RLEs to save : 5870 TO DO : save crop sub photo not yet done ! save time : 0.4238865375518799 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03398919105529785 time for calcul the mask position with numpy : 0.0207366943359375 nb_pixel_total : 2060633 time to create 1 rle with new method : 0.027644872665405273 time for calcul the mask position with numpy : 0.005918741226196289 nb_pixel_total : 12967 time to create 1 rle with old method : 0.013612747192382812 create new chi : 0.06814265251159668 time to delete rle : 0.0002353191375732422 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1312 TO DO : save crop sub photo not yet done ! save time : 0.12526297569274902 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03429007530212402 time for calcul the mask position with numpy : 0.019664525985717773 nb_pixel_total : 2069679 time to create 1 rle with new method : 0.029326677322387695 time for calcul the mask position with numpy : 0.0060083866119384766 nb_pixel_total : 3921 time to create 1 rle with old method : 0.004244089126586914 create new chi : 0.05957484245300293 time to delete rle : 0.00022029876708984375 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1198 TO DO : save crop sub photo not yet done ! save time : 0.13537192344665527 No data in photo_id : 1395872676 No data in photo_id : 1395872674 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.07698917388916016 time for calcul the mask position with numpy : 0.08619379997253418 nb_pixel_total : 2063696 time to create 1 rle with new method : 0.09714150428771973 time for calcul the mask position with numpy : 0.006394386291503906 nb_pixel_total : 3862 time to create 1 rle with old method : 0.004252433776855469 time for calcul the mask position with numpy : 0.006674051284790039 nb_pixel_total : 6042 time to create 1 rle with old method : 0.00648188591003418 create new chi : 0.21732568740844727 time to delete rle : 0.00026416778564453125 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1356 TO DO : save crop sub photo not yet done ! save time : 0.12788915634155273 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03381228446960449 time for calcul the mask position with numpy : 0.06903433799743652 nb_pixel_total : 2064428 time to create 1 rle with new method : 0.09030437469482422 time for calcul the mask position with numpy : 0.006000518798828125 nb_pixel_total : 9172 time to create 1 rle with old method : 0.010120868682861328 create new chi : 0.17874479293823242 time to delete rle : 0.00026488304138183594 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1322 TO DO : save crop sub photo not yet done ! save time : 0.1413114070892334 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03337240219116211 time for calcul the mask position with numpy : 0.01976323127746582 nb_pixel_total : 2065281 time to create 1 rle with new method : 0.06945586204528809 time for calcul the mask position with numpy : 0.006003379821777344 nb_pixel_total : 8319 time to create 1 rle with old method : 0.00892949104309082 create new chi : 0.11219477653503418 time to delete rle : 0.0002415180206298828 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1312 TO DO : save crop sub photo not yet done ! save time : 0.13442087173461914 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.032797813415527344 time for calcul the mask position with numpy : 0.09085392951965332 nb_pixel_total : 2058309 time to create 1 rle with new method : 0.10674118995666504 time for calcul the mask position with numpy : 0.00790095329284668 nb_pixel_total : 15291 time to create 1 rle with old method : 0.01765608787536621 create new chi : 0.2339179515838623 time to delete rle : 0.00028204917907714844 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1496 TO DO : save crop sub photo not yet done ! save time : 0.14589262008666992 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.0334169864654541 time for calcul the mask position with numpy : 0.0695333480834961 nb_pixel_total : 2064525 time to create 1 rle with new method : 0.14237689971923828 time for calcul the mask position with numpy : 0.006382465362548828 nb_pixel_total : 9075 time to create 1 rle with old method : 0.009948015213012695 create new chi : 0.23628973960876465 time to delete rle : 0.0002627372741699219 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1312 TO DO : save crop sub photo not yet done ! save time : 0.1268329620361328 No data in photo_id : 1395872652 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03601884841918945 time for calcul the mask position with numpy : 0.06699728965759277 nb_pixel_total : 2072696 time to create 1 rle with new method : 0.09707522392272949 time for calcul the mask position with numpy : 0.00632929801940918 nb_pixel_total : 904 time to create 1 rle with old method : 0.0009553432464599609 create new chi : 0.17455744743347168 time to delete rle : 0.00023055076599121094 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1174 TO DO : save crop sub photo not yet done ! save time : 0.13022065162658691 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.032468318939208984 time for calcul the mask position with numpy : 0.019788742065429688 nb_pixel_total : 2066616 time to create 1 rle with new method : 0.06904983520507812 time for calcul the mask position with numpy : 0.006247758865356445 nb_pixel_total : 6984 time to create 1 rle with old method : 0.0076904296875 create new chi : 0.11105942726135254 time to delete rle : 0.0002384185791015625 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1318 TO DO : save crop sub photo not yet done ! save time : 0.13843107223510742 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03319954872131348 time for calcul the mask position with numpy : 0.09428906440734863 nb_pixel_total : 2069595 time to create 1 rle with new method : 0.14640545845031738 time for calcul the mask position with numpy : 0.006178617477416992 nb_pixel_total : 4005 time to create 1 rle with old method : 0.004275321960449219 create new chi : 0.2591664791107178 time to delete rle : 0.0002224445343017578 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1184 TO DO : save crop sub photo not yet done ! save time : 0.12171554565429688 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03247642517089844 time for calcul the mask position with numpy : 0.06250333786010742 nb_pixel_total : 2069300 time to create 1 rle with new method : 0.12811732292175293 time for calcul the mask position with numpy : 0.005929231643676758 nb_pixel_total : 4300 time to create 1 rle with old method : 0.004656076431274414 create new chi : 0.21056246757507324 time to delete rle : 0.0002732276916503906 batch 1 Loaded 3 chid ids of type : 3594 +++Number RLEs to save : 1296 TO DO : save crop sub photo not yet done ! save time : 0.14090943336486816 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.04837155342102051 time for calcul the mask position with numpy : 0.012484550476074219 nb_pixel_total : 887652 time to create 1 rle with new method : 0.08823585510253906 time for calcul the mask position with numpy : 0.060993194580078125 nb_pixel_total : 1053325 time to create 1 rle with new method : 0.0954902172088623 time for calcul the mask position with numpy : 0.007215738296508789 nb_pixel_total : 132623 time to create 1 rle with old method : 0.14276623725891113 create new chi : 0.42129969596862793 time to delete rle : 0.0009114742279052734 batch 1 Loaded 5 chid ids of type : 3594 +++Number RLEs to save : 5186 TO DO : save crop sub photo not yet done ! save time : 0.38422417640686035 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.034050941467285156 time for calcul the mask position with numpy : 0.09621238708496094 nb_pixel_total : 2064977 time to create 1 rle with new method : 0.21813273429870605 time for calcul the mask position with numpy : 0.006184816360473633 nb_pixel_total : 8623 time to create 1 rle with old method : 0.009525537490844727 create new chi : 0.3384208679199219 time to delete rle : 0.0002627372741699219 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 : 0.1379075050354004 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.06263470649719238 time for calcul the mask position with numpy : 0.06661415100097656 nb_pixel_total : 2053791 time to create 1 rle with new method : 0.17496919631958008 time for calcul the mask position with numpy : 0.006006717681884766 nb_pixel_total : 3108 time to create 1 rle with old method : 0.0033876895904541016 time for calcul the mask position with numpy : 0.0059757232666015625 nb_pixel_total : 16701 time to create 1 rle with old method : 0.018236160278320312 create new chi : 0.2849557399749756 time to delete rle : 0.0002694129943847656 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1584 TO DO : save crop sub photo not yet done ! save time : 0.1521158218383789 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.035273075103759766 time for calcul the mask position with numpy : 0.04399442672729492 nb_pixel_total : 2051201 time to create 1 rle with new method : 0.10733222961425781 time for calcul the mask position with numpy : 0.006033182144165039 nb_pixel_total : 22399 time to create 1 rle with old method : 0.024343252182006836 create new chi : 0.19003558158874512 time to delete rle : 0.0002455711364746094 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1422 TO DO : save crop sub photo not yet done ! save time : 0.14927935600280762 No data in photo_id : 1395872603 No data in photo_id : 1395872601 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.0627739429473877 time for calcul the mask position with numpy : 0.09857702255249023 nb_pixel_total : 2055550 time to create 1 rle with new method : 0.17509675025939941 time for calcul the mask position with numpy : 0.006060361862182617 nb_pixel_total : 5613 time to create 1 rle with old method : 0.005957365036010742 time for calcul the mask position with numpy : 0.0060122013092041016 nb_pixel_total : 12437 time to create 1 rle with old method : 0.01378321647644043 create new chi : 0.31520771980285645 time to delete rle : 0.0003025531768798828 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1642 TO DO : save crop sub photo not yet done ! save time : 0.15500187873840332 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03732013702392578 time for calcul the mask position with numpy : 0.01415872573852539 nb_pixel_total : 1096371 time to create 1 rle with new method : 0.11163806915283203 time for calcul the mask position with numpy : 0.013614416122436523 nb_pixel_total : 977229 time to create 1 rle with new method : 0.11752486228942871 create new chi : 0.26418352127075195 time to delete rle : 0.00037407875061035156 batch 1 Loaded 3 chid ids of type : 3594 ++Number RLEs to save : 4104 TO DO : save crop sub photo not yet done ! save time : 0.31969547271728516 map_output_result : {1395872773: (0.0, 'Should be the crop_list due to order', 0), 1395872765: (0.0, 'Should be the crop_list due to order', 0.0), 1395872763: (0.0, 'Should be the crop_list due to order', 0), 1395872761: (0.0, 'Should be the crop_list due to order', 0), 1395872759: (0.0, 'Should be the crop_list due to order', 0), 1395872756: (0.0, 'Should be the crop_list due to order', 0), 1395872754: (0.0, 'Should be the crop_list due to order', 0), 1395872719: (0.0, 'Should be the crop_list due to order', 0), 1395872712: (0.0, 'Should be the crop_list due to order', 0), 1395872708: (0.0, 'Should be the crop_list due to order', 0), 1395872706: (0.0, 'Should be the crop_list due to order', 0), 1395872704: (0.0, 'Should be the crop_list due to order', 0), 1395872703: (0.0, 'Should be the crop_list due to order', 0), 1395872702: (0.0, 'Should be the crop_list due to order', 0.0), 1395872701: (0.0, 'Should be the crop_list due to order', 0.0), 1395872700: (0.0, 'Should be the crop_list due to order', 0.0), 1395872698: (0.0, 'Should be the crop_list due to order', 0), 1395872692: (0.0, 'Should be the crop_list due to order', 0), 1395872680: (0.0, 'Should be the crop_list due to order', 0), 1395872678: (0.0, 'Should be the crop_list due to order', 0), 1395872676: (0.0, 'Should be the crop_list due to order', 0.0), 1395872674: (0.0, 'Should be the crop_list due to order', 0.0), 1395872672: (0.0, 'Should be the crop_list due to order', 0), 1395872671: (0.0, 'Should be the crop_list due to order', 0), 1395872658: (0.0, 'Should be the crop_list due to order', 0), 1395872656: (0.0, 'Should be the crop_list due to order', 0), 1395872654: (0.0, 'Should be the crop_list due to order', 0), 1395872652: (0.0, 'Should be the crop_list due to order', 0.0), 1395872651: (0.0, 'Should be the crop_list due to order', 0), 1395872647: (0.0, 'Should be the crop_list due to order', 0), 1395872645: (0.0, 'Should be the crop_list due to order', 0), 1395872643: (0.0, 'Should be the crop_list due to order', 0), 1395872641: (0.0, 'Should be the crop_list due to order', 0), 1395872615: (0.0, 'Should be the crop_list due to order', 0), 1395872606: (0.0, 'Should be the crop_list due to order', 0), 1395872605: (0.0, 'Should be the crop_list due to order', 0), 1395872603: (0.0, 'Should be the crop_list due to order', 0.0), 1395872601: (0.0, 'Should be the crop_list due to order', 0.0), 1395872599: (0.0, 'Should be the crop_list due to order', 0), 1395872597: (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 [1395872773, 1395872765, 1395872763, 1395872761, 1395872759, 1395872756, 1395872754, 1395872719, 1395872712, 1395872708, 1395872706, 1395872704, 1395872703, 1395872702, 1395872701, 1395872700, 1395872698, 1395872692, 1395872680, 1395872678, 1395872676, 1395872674, 1395872672, 1395872671, 1395872658, 1395872656, 1395872654, 1395872652, 1395872651, 1395872647, 1395872645, 1395872643, 1395872641, 1395872615, 1395872606, 1395872605, 1395872603, 1395872601, 1395872599, 1395872597] Looping around the photos to save general results len do output : 40 /1395872773.Didn't retrieve data . /1395872765.Didn't retrieve data . /1395872763.Didn't retrieve data . /1395872761.Didn't retrieve data . /1395872759.Didn't retrieve data . /1395872756.Didn't retrieve data . /1395872754.Didn't retrieve data . /1395872719.Didn't retrieve data . /1395872712.Didn't retrieve data . /1395872708.Didn't retrieve data . /1395872706.Didn't retrieve data . /1395872704.Didn't retrieve data . /1395872703.Didn't retrieve data . /1395872702.Didn't retrieve data . /1395872701.Didn't retrieve data . /1395872700.Didn't retrieve data . /1395872698.Didn't retrieve data . /1395872692.Didn't retrieve data . /1395872680.Didn't retrieve data . /1395872678.Didn't retrieve data . /1395872676.Didn't retrieve data . /1395872674.Didn't retrieve data . /1395872672.Didn't retrieve data . /1395872671.Didn't retrieve data . /1395872658.Didn't retrieve data . /1395872656.Didn't retrieve data . /1395872654.Didn't retrieve data . /1395872652.Didn't retrieve data . /1395872651.Didn't retrieve data . /1395872647.Didn't retrieve data . /1395872645.Didn't retrieve data . /1395872643.Didn't retrieve data . /1395872641.Didn't retrieve data . /1395872615.Didn't retrieve data . /1395872606.Didn't retrieve data . /1395872605.Didn't retrieve data . /1395872603.Didn't retrieve data . /1395872601.Didn't retrieve data . /1395872599.Didn't retrieve data . /1395872597.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, '4102347') ('3318', '28828426', '1395872773', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872765', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872763', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872761', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872759', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872756', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872754', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872719', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872712', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872708', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872706', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872704', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872703', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872702', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872701', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872700', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872698', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872692', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872680', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872678', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872676', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872674', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872672', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872671', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872658', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872656', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872654', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872652', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872651', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872647', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872645', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872643', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872641', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872615', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872606', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872605', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872603', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872601', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872599', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872597', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 120 time used for this insertion : 0.022211551666259766 save_final save missing photos in datou_result : time spend for datou_step_exec : 17.590755224227905 time spend to save output : 0.023193359375 total time spend for step 3 : 17.613948583602905 step4:ventilate_hashtags_in_portfolio Mon Nov 24 14:02:44 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 : 28828426 get user id for portfolio 28828426 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`=28828426 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','mal_croppe','carton','environnement','flou','pet_clair','pet_fonce','background','papier','autre','pehd')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=28828426 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','mal_croppe','carton','environnement','flou','pet_clair','pet_fonce','background','papier','autre','pehd')) AND mptpi.`min_score`=0.5 To do 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`=28828426 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('metal','mal_croppe','carton','environnement','flou','pet_clair','pet_fonce','background','papier','autre','pehd')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/28831321,28831322,28831323,28831324,28831325,28831326,28831327,28831328,28831329,28831330,28831331?tags=metal,mal_croppe,carton,environnement,flou,pet_clair,pet_fonce,background,papier,autre,pehd Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1395872773, 1395872765, 1395872763, 1395872761, 1395872759, 1395872756, 1395872754, 1395872719, 1395872712, 1395872708, 1395872706, 1395872704, 1395872703, 1395872702, 1395872701, 1395872700, 1395872698, 1395872692, 1395872680, 1395872678, 1395872676, 1395872674, 1395872672, 1395872671, 1395872658, 1395872656, 1395872654, 1395872652, 1395872651, 1395872647, 1395872645, 1395872643, 1395872641, 1395872615, 1395872606, 1395872605, 1395872603, 1395872601, 1395872599, 1395872597] Looping around the photos to save general results len do output : 1 /28828426. 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, '4102347') ('3318', '28828426', '1395872773', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872765', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872763', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872761', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872759', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872756', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872754', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872719', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872712', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872708', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872706', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872704', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872703', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872702', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872701', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872700', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872698', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872692', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872680', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872678', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872676', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872674', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872672', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872671', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872658', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872656', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872654', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872652', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872651', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872647', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872645', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872643', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872641', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872615', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872606', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872605', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872603', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872601', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872599', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872597', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 41 time used for this insertion : 0.023257970809936523 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.7864313125610352 time spend to save output : 0.02370762825012207 total time spend for step 4 : 0.8101389408111572 step5:final Mon Nov 24 14:02:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : {1395872773: ('0.1292190272955247',), 1395872765: ('0.1292190272955247',), 1395872763: ('0.1292190272955247',), 1395872761: ('0.1292190272955247',), 1395872759: ('0.1292190272955247',), 1395872756: ('0.1292190272955247',), 1395872754: ('0.1292190272955247',), 1395872719: ('0.1292190272955247',), 1395872712: ('0.1292190272955247',), 1395872708: ('0.1292190272955247',), 1395872706: ('0.1292190272955247',), 1395872704: ('0.1292190272955247',), 1395872703: ('0.1292190272955247',), 1395872702: ('0.1292190272955247',), 1395872701: ('0.1292190272955247',), 1395872700: ('0.1292190272955247',), 1395872698: ('0.1292190272955247',), 1395872692: ('0.1292190272955247',), 1395872680: ('0.1292190272955247',), 1395872678: ('0.1292190272955247',), 1395872676: ('0.1292190272955247',), 1395872674: ('0.1292190272955247',), 1395872672: ('0.1292190272955247',), 1395872671: ('0.1292190272955247',), 1395872658: ('0.1292190272955247',), 1395872656: ('0.1292190272955247',), 1395872654: ('0.1292190272955247',), 1395872652: ('0.1292190272955247',), 1395872651: ('0.1292190272955247',), 1395872647: ('0.1292190272955247',), 1395872645: ('0.1292190272955247',), 1395872643: ('0.1292190272955247',), 1395872641: ('0.1292190272955247',), 1395872615: ('0.1292190272955247',), 1395872606: ('0.1292190272955247',), 1395872605: ('0.1292190272955247',), 1395872603: ('0.1292190272955247',), 1395872601: ('0.1292190272955247',), 1395872599: ('0.1292190272955247',), 1395872597: ('0.1292190272955247',)} new output for save of step final : {1395872773: ('0.1292190272955247',), 1395872765: ('0.1292190272955247',), 1395872763: ('0.1292190272955247',), 1395872761: ('0.1292190272955247',), 1395872759: ('0.1292190272955247',), 1395872756: ('0.1292190272955247',), 1395872754: ('0.1292190272955247',), 1395872719: ('0.1292190272955247',), 1395872712: ('0.1292190272955247',), 1395872708: ('0.1292190272955247',), 1395872706: ('0.1292190272955247',), 1395872704: ('0.1292190272955247',), 1395872703: ('0.1292190272955247',), 1395872702: ('0.1292190272955247',), 1395872701: ('0.1292190272955247',), 1395872700: ('0.1292190272955247',), 1395872698: ('0.1292190272955247',), 1395872692: ('0.1292190272955247',), 1395872680: ('0.1292190272955247',), 1395872678: ('0.1292190272955247',), 1395872676: ('0.1292190272955247',), 1395872674: ('0.1292190272955247',), 1395872672: ('0.1292190272955247',), 1395872671: ('0.1292190272955247',), 1395872658: ('0.1292190272955247',), 1395872656: ('0.1292190272955247',), 1395872654: ('0.1292190272955247',), 1395872652: ('0.1292190272955247',), 1395872651: ('0.1292190272955247',), 1395872647: ('0.1292190272955247',), 1395872645: ('0.1292190272955247',), 1395872643: ('0.1292190272955247',), 1395872641: ('0.1292190272955247',), 1395872615: ('0.1292190272955247',), 1395872606: ('0.1292190272955247',), 1395872605: ('0.1292190272955247',), 1395872603: ('0.1292190272955247',), 1395872601: ('0.1292190272955247',), 1395872599: ('0.1292190272955247',), 1395872597: ('0.1292190272955247',)} [1395872773, 1395872765, 1395872763, 1395872761, 1395872759, 1395872756, 1395872754, 1395872719, 1395872712, 1395872708, 1395872706, 1395872704, 1395872703, 1395872702, 1395872701, 1395872700, 1395872698, 1395872692, 1395872680, 1395872678, 1395872676, 1395872674, 1395872672, 1395872671, 1395872658, 1395872656, 1395872654, 1395872652, 1395872651, 1395872647, 1395872645, 1395872643, 1395872641, 1395872615, 1395872606, 1395872605, 1395872603, 1395872601, 1395872599, 1395872597] Looping around the photos to save general results len do output : 40 /1395872773.Didn't retrieve data . /1395872765.Didn't retrieve data . /1395872763.Didn't retrieve data . /1395872761.Didn't retrieve data . /1395872759.Didn't retrieve data . /1395872756.Didn't retrieve data . /1395872754.Didn't retrieve data . /1395872719.Didn't retrieve data . /1395872712.Didn't retrieve data . /1395872708.Didn't retrieve data . /1395872706.Didn't retrieve data . /1395872704.Didn't retrieve data . /1395872703.Didn't retrieve data . /1395872702.Didn't retrieve data . /1395872701.Didn't retrieve data . /1395872700.Didn't retrieve data . /1395872698.Didn't retrieve data . /1395872692.Didn't retrieve data . /1395872680.Didn't retrieve data . /1395872678.Didn't retrieve data . /1395872676.Didn't retrieve data . /1395872674.Didn't retrieve data . /1395872672.Didn't retrieve data . /1395872671.Didn't retrieve data . /1395872658.Didn't retrieve data . /1395872656.Didn't retrieve data . /1395872654.Didn't retrieve data . /1395872652.Didn't retrieve data . /1395872651.Didn't retrieve data . /1395872647.Didn't retrieve data . /1395872645.Didn't retrieve data . /1395872643.Didn't retrieve data . /1395872641.Didn't retrieve data . /1395872615.Didn't retrieve data . /1395872606.Didn't retrieve data . /1395872605.Didn't retrieve data . /1395872603.Didn't retrieve data . /1395872601.Didn't retrieve data . /1395872599.Didn't retrieve data . /1395872597.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, '4102347') ('3318', '28828426', '1395872773', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872765', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872763', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872761', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872759', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872756', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872754', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872719', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872712', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872708', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872706', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872704', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872703', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872702', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872701', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872700', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872698', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872692', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872680', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872678', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872676', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872674', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872672', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872671', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872658', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872656', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872654', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872652', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872651', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872647', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872645', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872643', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872641', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872615', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872606', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872605', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872603', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872601', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872599', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872597', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 120 time used for this insertion : 0.021309614181518555 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.13667631149291992 time spend to save output : 0.022897005081176758 total time spend for step 5 : 0.15957331657409668 step6:blur_detection Mon Nov 24 14:02:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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/1763989230_3675541_1395872773_a006a3a7ba961a4011d86fd38b20a1bf.jpg resize: (1080, 1920) 1395872773 2.0213828753451737 treat image : temp/1763989230_3675541_1395872765_613cb364c5fc6c33e4880c66fb146d05.jpg resize: (1080, 1920) 1395872765 -0.4369098369906549 treat image : temp/1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96.jpg resize: (1080, 1920) 1395872763 -2.623305283712547 treat image : temp/1763989230_3675541_1395872761_f2f1ffdff96383cf14b4d503f2aba28e.jpg resize: (1080, 1920) 1395872761 -1.0215057942597645 treat image : temp/1763989230_3675541_1395872759_cfa5ebc9b5429363ef0dac2b3f718a3a.jpg resize: (1080, 1920) 1395872759 -2.179658474343405 treat image : temp/1763989230_3675541_1395872756_55e36efe4a99865b550b4f85a9b63a0c.jpg resize: (1080, 1920) 1395872756 -2.4880582481798403 treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8.jpg resize: (1080, 1920) 1395872754 -0.38130856151495246 treat image : temp/1763989230_3675541_1395872719_4235713827b087d22e7b3b4274299445.jpg resize: (1080, 1920) 1395872719 -2.333144460407948 treat image : temp/1763989230_3675541_1395872712_064431273f0adaf893de8424dc15512e.jpg resize: (1080, 1920) 1395872712 -2.4762115462843566 treat image : temp/1763989230_3675541_1395872708_a46fe06d2dcff1181d4c9b28a5d01cfc.jpg resize: (1080, 1920) 1395872708 -0.08737807058836629 treat image : temp/1763989230_3675541_1395872706_e357cf5d1c229f74016ea4aee45d5f07.jpg resize: (1080, 1920) 1395872706 -2.321265697539924 treat image : temp/1763989230_3675541_1395872704_2c8b6266ad7dc134d4b19e108829c6fe.jpg resize: (1080, 1920) 1395872704 -1.780055487562566 treat image : temp/1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7.jpg resize: (1080, 1920) 1395872703 -2.1429458917577793 treat image : temp/1763989230_3675541_1395872702_fadafcca5f945b2e0b1f76b37e2d8184.jpg resize: (1080, 1920) 1395872702 -1.9159142764806207 treat image : temp/1763989230_3675541_1395872701_7e074b567ef0528a3e1deedb1d7d8c0b.jpg resize: (1080, 1920) 1395872701 -1.8396874277209219 treat image : temp/1763989230_3675541_1395872700_dbb3531446b7c6a7f3931cbc92e319d0.jpg resize: (1080, 1920) 1395872700 -2.1875803062246186 treat image : temp/1763989230_3675541_1395872698_bdaad0d64375723456764ae88bab5f36.jpg resize: (1080, 1920) 1395872698 -3.975794042178204 treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152.jpg resize: (1080, 1920) 1395872692 -0.19564209512037425 treat image : temp/1763989230_3675541_1395872680_9e35c66ebf6e39096e3bb04029d94d0a.jpg resize: (1080, 1920) 1395872680 -2.082728588709003 treat image : temp/1763989230_3675541_1395872678_c1a09c5b7cdbe37581eaff51ac07160f.jpg resize: (1080, 1920) 1395872678 -1.8343792794927924 treat image : temp/1763989230_3675541_1395872676_275ea2710a8385461f1707c348cbf11e.jpg resize: (1080, 1920) 1395872676 -2.0826933327529367 treat image : temp/1763989230_3675541_1395872674_b7999abbab32ffc6f5c56fc31a74dced.jpg resize: (1080, 1920) 1395872674 -1.6298896097689133 treat image : temp/1763989230_3675541_1395872672_17ab726beae4e0b3e0e4b44294a49040.jpg resize: (1080, 1920) 1395872672 -1.9524093835111636 treat image : temp/1763989230_3675541_1395872671_08ea492908ea2ebb5503a08f664c2ef0.jpg resize: (1080, 1920) 1395872671 -2.5810718662619214 treat image : temp/1763989230_3675541_1395872658_253dd165ecabc9cfe676e8c3b33bb67c.jpg resize: (1080, 1920) 1395872658 -2.1155846512729126 treat image : temp/1763989230_3675541_1395872656_4b30e205695e3072c00b0e0394daf227.jpg resize: (1080, 1920) 1395872656 -1.8770906571515593 treat image : temp/1763989230_3675541_1395872654_504a302108a3eed78026e85e4bcdfcb5.jpg resize: (1080, 1920) 1395872654 -1.5673853399856008 treat image : temp/1763989230_3675541_1395872652_e7dc871d15c09e6115011efe08224497.jpg resize: (1080, 1920) 1395872652 -1.4391992607854862 treat image : temp/1763989230_3675541_1395872651_ee01ae3fdaa496bc53f9f24cea92c1db.jpg resize: (1080, 1920) 1395872651 -1.754505917080677 treat image : temp/1763989230_3675541_1395872647_982295c309035a3f9362b8a6a91ab6e9.jpg resize: (1080, 1920) 1395872647 -2.4983340652397046 treat image : temp/1763989230_3675541_1395872645_e3f87371b3b40ad00570860417e07cf5.jpg resize: (1080, 1920) 1395872645 -2.045650001043954 treat image : temp/1763989230_3675541_1395872643_0519009d0ef50d5bfdb09a870b3c82dc.jpg resize: (1080, 1920) 1395872643 -2.025112072120659 treat image : temp/1763989230_3675541_1395872641_bc4c247756caf514ff007c9c847f4acc.jpg resize: (1080, 1920) 1395872641 -3.174254321526739 treat image : temp/1763989230_3675541_1395872615_a0df0666ddd5ad42b60a40ec470b5688.jpg resize: (1080, 1920) 1395872615 -2.6207827072617915 treat image : temp/1763989230_3675541_1395872606_e5d76289d06bf0f47ba71e7ac68e7c67.jpg resize: (1080, 1920) 1395872606 -1.3409573476868577 treat image : temp/1763989230_3675541_1395872605_7f80f108968b000b0ff33ce15c4c551a.jpg resize: (1080, 1920) 1395872605 -2.179571468779025 treat image : temp/1763989230_3675541_1395872603_9be1a57c559540c4f5ba91c2d82313eb.jpg resize: (1080, 1920) 1395872603 -2.0924293934074596 treat image : temp/1763989230_3675541_1395872601_141f1952001dd33b35dc00c0b19a4609.jpg resize: (1080, 1920) 1395872601 -1.7443939841840785 treat image : temp/1763989230_3675541_1395872599_ee62aaba977cdd4442f3736b49f8de7f.jpg resize: (1080, 1920) 1395872599 -1.6265371150011998 treat image : temp/1763989230_3675541_1395872597_f88a635f71a23986a06d351ef8ed4fa5.jpg resize: (1080, 1920) 1395872597 -2.609357972690891 treat image : temp/1763989230_3675541_1395872773_a006a3a7ba961a4011d86fd38b20a1bf_rle_crop_4043689412_0.png resize: (106, 93) 1395900541 0.3237560926634217 treat image : temp/1763989230_3675541_1395872773_a006a3a7ba961a4011d86fd38b20a1bf_rle_crop_4043689413_0.png resize: (1010, 1161) 1395900542 -0.03303394611718794 treat image : temp/1763989230_3675541_1395872761_f2f1ffdff96383cf14b4d503f2aba28e_rle_crop_4043689418_0.png resize: (100, 101) 1395900544 4.0597367289041095 treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689426_0.png resize: (107, 89) 1395900545 0.10622840297024808 treat image : temp/1763989230_3675541_1395872712_064431273f0adaf893de8424dc15512e_rle_crop_4043689433_0.png resize: (90, 82) 1395900546 -1.6510897417711223 treat image : temp/1763989230_3675541_1395872706_e357cf5d1c229f74016ea4aee45d5f07_rle_crop_4043689436_0.png resize: (116, 47) 1395900547 -1.2860880365623752 treat image : temp/1763989230_3675541_1395872698_bdaad0d64375723456764ae88bab5f36_rle_crop_4043689444_0.png resize: (79, 88) 1395900548 -0.8772876536723633 treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689446_0.png resize: (80, 91) 1395900549 0.29789391862681824 treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689447_0.png resize: (101, 87) 1395900550 0.8814621562737756 treat image : temp/1763989230_3675541_1395872656_4b30e205695e3072c00b0e0394daf227_rle_crop_4043689458_0.png resize: (201, 174) 1395900551 -1.423807078149998 treat image : temp/1763989230_3675541_1395872651_ee01ae3fdaa496bc53f9f24cea92c1db_rle_crop_4043689460_0.png resize: (47, 27) 1395900552 0.9110294306930049 treat image : temp/1763989230_3675541_1395872643_0519009d0ef50d5bfdb09a870b3c82dc_rle_crop_4043689463_0.png resize: (83, 75) 1395900553 -3.065278633980418 treat image : temp/1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96_rle_crop_4043689414_0.png resize: (553, 365) 1395900569 0.5657744059992186 treat image : 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into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 101 time used for this insertion : 0.017482757568359375 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 101 time used for this insertion : 0.031931400299072266 save missing photos in datou_result : time spend for datou_step_exec : 34.44580006599426 time spend to save output : 0.055805206298828125 total time spend for step 6 : 34.50160527229309 step7:brightness Mon Nov 24 14:03:19 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1763989230_3675541_1395872773_a006a3a7ba961a4011d86fd38b20a1bf.jpg treat image : temp/1763989230_3675541_1395872765_613cb364c5fc6c33e4880c66fb146d05.jpg treat image : temp/1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96.jpg treat image : temp/1763989230_3675541_1395872761_f2f1ffdff96383cf14b4d503f2aba28e.jpg treat image : temp/1763989230_3675541_1395872759_cfa5ebc9b5429363ef0dac2b3f718a3a.jpg treat image : temp/1763989230_3675541_1395872756_55e36efe4a99865b550b4f85a9b63a0c.jpg treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8.jpg treat image : temp/1763989230_3675541_1395872719_4235713827b087d22e7b3b4274299445.jpg treat image : temp/1763989230_3675541_1395872712_064431273f0adaf893de8424dc15512e.jpg treat image : temp/1763989230_3675541_1395872708_a46fe06d2dcff1181d4c9b28a5d01cfc.jpg treat image : 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treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689426_0.png treat image : temp/1763989230_3675541_1395872712_064431273f0adaf893de8424dc15512e_rle_crop_4043689433_0.png treat image : temp/1763989230_3675541_1395872706_e357cf5d1c229f74016ea4aee45d5f07_rle_crop_4043689436_0.png treat image : temp/1763989230_3675541_1395872698_bdaad0d64375723456764ae88bab5f36_rle_crop_4043689444_0.png treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689446_0.png treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689447_0.png treat image : temp/1763989230_3675541_1395872656_4b30e205695e3072c00b0e0394daf227_rle_crop_4043689458_0.png treat image : temp/1763989230_3675541_1395872651_ee01ae3fdaa496bc53f9f24cea92c1db_rle_crop_4043689460_0.png treat image : temp/1763989230_3675541_1395872643_0519009d0ef50d5bfdb09a870b3c82dc_rle_crop_4043689463_0.png treat image : temp/1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96_rle_crop_4043689414_0.png treat image : temp/1763989230_3675541_1395872761_f2f1ffdff96383cf14b4d503f2aba28e_rle_crop_4043689419_0.png treat image : temp/1763989230_3675541_1395872759_cfa5ebc9b5429363ef0dac2b3f718a3a_rle_crop_4043689420_0.png treat image : temp/1763989230_3675541_1395872719_4235713827b087d22e7b3b4274299445_rle_crop_4043689432_0.png treat image : temp/1763989230_3675541_1395872712_064431273f0adaf893de8424dc15512e_rle_crop_4043689434_0.png treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689451_0.png treat image : temp/1763989230_3675541_1395872672_17ab726beae4e0b3e0e4b44294a49040_rle_crop_4043689454_0.png treat image : temp/1763989230_3675541_1395872671_08ea492908ea2ebb5503a08f664c2ef0_rle_crop_4043689456_0.png treat image : temp/1763989230_3675541_1395872658_253dd165ecabc9cfe676e8c3b33bb67c_rle_crop_4043689457_0.png treat image : temp/1763989230_3675541_1395872654_504a302108a3eed78026e85e4bcdfcb5_rle_crop_4043689459_0.png treat image : temp/1763989230_3675541_1395872647_982295c309035a3f9362b8a6a91ab6e9_rle_crop_4043689461_0.png treat image : temp/1763989230_3675541_1395872615_a0df0666ddd5ad42b60a40ec470b5688_rle_crop_4043689466_0.png treat image : temp/1763989230_3675541_1395872606_e5d76289d06bf0f47ba71e7ac68e7c67_rle_crop_4043689467_0.png treat image : temp/1763989230_3675541_1395872599_ee62aaba977cdd4442f3736b49f8de7f_rle_crop_4043689471_0.png treat image : temp/1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96_rle_crop_4043689415_0.png treat image : temp/1763989230_3675541_1395872763_76e7622cebfefc86714885a8e2cc5b96_rle_crop_4043689416_0.png treat image : temp/1763989230_3675541_1395872761_f2f1ffdff96383cf14b4d503f2aba28e_rle_crop_4043689417_0.png treat image : temp/1763989230_3675541_1395872759_cfa5ebc9b5429363ef0dac2b3f718a3a_rle_crop_4043689421_0.png treat image : temp/1763989230_3675541_1395872756_55e36efe4a99865b550b4f85a9b63a0c_rle_crop_4043689422_0.png treat image : temp/1763989230_3675541_1395872756_55e36efe4a99865b550b4f85a9b63a0c_rle_crop_4043689423_0.png treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689424_0.png treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689425_0.png treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689427_0.png treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689428_0.png treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689429_0.png treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689431_0.png treat image : temp/1763989230_3675541_1395872708_a46fe06d2dcff1181d4c9b28a5d01cfc_rle_crop_4043689435_0.png treat image : temp/1763989230_3675541_1395872704_2c8b6266ad7dc134d4b19e108829c6fe_rle_crop_4043689438_0.png treat image : temp/1763989230_3675541_1395872704_2c8b6266ad7dc134d4b19e108829c6fe_rle_crop_4043689439_0.png treat image : temp/1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7_rle_crop_4043689440_0.png treat image : temp/1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7_rle_crop_4043689441_0.png treat image : temp/1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7_rle_crop_4043689442_0.png treat image : temp/1763989230_3675541_1395872703_bbaba1add00c0e1ce91f433df7a2dcf7_rle_crop_4043689443_0.png treat image : temp/1763989230_3675541_1395872698_bdaad0d64375723456764ae88bab5f36_rle_crop_4043689445_0.png treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689448_0.png treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689449_0.png treat image : temp/1763989230_3675541_1395872692_a535371be0f0927069413882faaa6152_rle_crop_4043689450_0.png treat image : temp/1763989230_3675541_1395872680_9e35c66ebf6e39096e3bb04029d94d0a_rle_crop_4043689452_0.png treat image : temp/1763989230_3675541_1395872678_c1a09c5b7cdbe37581eaff51ac07160f_rle_crop_4043689453_0.png treat image : temp/1763989230_3675541_1395872672_17ab726beae4e0b3e0e4b44294a49040_rle_crop_4043689455_0.png treat image : temp/1763989230_3675541_1395872645_e3f87371b3b40ad00570860417e07cf5_rle_crop_4043689462_0.png treat image : temp/1763989230_3675541_1395872641_bc4c247756caf514ff007c9c847f4acc_rle_crop_4043689464_0.png treat image : temp/1763989230_3675541_1395872641_bc4c247756caf514ff007c9c847f4acc_rle_crop_4043689465_0.png treat image : temp/1763989230_3675541_1395872606_e5d76289d06bf0f47ba71e7ac68e7c67_rle_crop_4043689468_0.png treat image : temp/1763989230_3675541_1395872605_7f80f108968b000b0ff33ce15c4c551a_rle_crop_4043689469_0.png treat image : temp/1763989230_3675541_1395872599_ee62aaba977cdd4442f3736b49f8de7f_rle_crop_4043689470_0.png treat image : temp/1763989230_3675541_1395872597_f88a635f71a23986a06d351ef8ed4fa5_rle_crop_4043689472_0.png treat image : temp/1763989230_3675541_1395872754_7da56ae8a2b419249f7fe3a3428bf6c8_rle_crop_4043689430_0.png treat image : temp/1763989230_3675541_1395872706_e357cf5d1c229f74016ea4aee45d5f07_rle_crop_4043689437_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 : 101 time used for this insertion : 0.029978036880493164 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 101 time used for this insertion : 0.030536890029907227 save missing photos in datou_result : time spend for datou_step_exec : 10.288125276565552 time spend to save output : 0.06686758995056152 total time spend for step 7 : 10.354992866516113 step8:velours_tree Mon Nov 24 14:03:30 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 : 0.1791398525238037 time spend to save output : 4.482269287109375e-05 total time spend for step 8 : 0.1791846752166748 step9:send_mail_cod Mon Nov 24 14:03:30 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_P28828426_24-11-2025_14_03_30.pdf 28831321 imagette288313211763989410 28831322 imagette288313221763989410 28831323 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 .imagette288313231763989410 28831325 imagette288313251763989411 28831326 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 .imagette288313261763989411 28831327 imagette288313271763989413 28831328 imagette288313281763989413 28831329 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 .imagette288313291763989413 28831330 change filename to text .change filename to text .imagette288313301763989414 28831331 imagette288313311763989414 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=28828426 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/28831321,28831322,28831323,28831324,28831325,28831326,28831327,28831328,28831329,28831330,28831331?tags=metal,mal_croppe,carton,environnement,flou,pet_clair,pet_fonce,background,papier,autre,pehd args[1395872773] : ((1395872773, 2.0213828753451737, 492688767), (1395872773, 0.3263493134922331, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872765] : ((1395872765, -0.4369098369906549, 492688767), (1395872765, 0.6653615789590934, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872763] : ((1395872763, -2.623305283712547, 492609224), (1395872763, 0.5340451728708412, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872761] : ((1395872761, -1.0215057942597645, 492688767), (1395872761, 0.708879348698139, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872759] : ((1395872759, -2.179658474343405, 492609224), (1395872759, 0.6198369647422538, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872756] : ((1395872756, -2.4880582481798403, 492609224), (1395872756, 0.45290858199044814, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872754] : ((1395872754, -0.38130856151495246, 492688767), (1395872754, 0.30529964547133465, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872719] : ((1395872719, -2.333144460407948, 492609224), (1395872719, 0.5142357973393156, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872712] : ((1395872712, -2.4762115462843566, 492609224), (1395872712, 0.6208506202543895, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872708] : ((1395872708, -0.08737807058836629, 492688767), (1395872708, 0.5636373449605571, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872706] : ((1395872706, -2.321265697539924, 492609224), (1395872706, 0.49585467954668566, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872704] : ((1395872704, -1.780055487562566, 492688767), (1395872704, 0.5429944971768125, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872703] : ((1395872703, -2.1429458917577793, 492609224), (1395872703, 0.5065205475951792, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872702] : ((1395872702, -1.9159142764806207, 492688767), (1395872702, 0.865238344320414, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872701] : ((1395872701, -1.8396874277209219, 492688767), (1395872701, 0.7509145118455972, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872700] : ((1395872700, -2.1875803062246186, 492609224), (1395872700, 0.5778129593140195, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872698] : ((1395872698, -3.975794042178204, 492609224), (1395872698, 0.4677035783300158, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872692] : ((1395872692, -0.19564209512037425, 492688767), (1395872692, 0.28336393136463073, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872680] : ((1395872680, -2.082728588709003, 492609224), (1395872680, 0.745140932260487, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872678] : ((1395872678, -1.8343792794927924, 492688767), (1395872678, 0.9016049435860358, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872676] : ((1395872676, -2.0826933327529367, 492609224), (1395872676, 0.4942485237105908, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872674] : ((1395872674, -1.6298896097689133, 492688767), (1395872674, 0.6806469958489151, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872672] : ((1395872672, -1.9524093835111636, 492688767), (1395872672, 0.7491853848451454, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872671] : ((1395872671, -2.5810718662619214, 492609224), (1395872671, 0.4390301833425513, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872658] : ((1395872658, -2.1155846512729126, 492609224), (1395872658, 0.6764042346943181, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872656] : ((1395872656, -1.8770906571515593, 492688767), (1395872656, 0.5754371419258484, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872654] : ((1395872654, -1.5673853399856008, 492688767), (1395872654, 0.3077220585928082, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872652] : ((1395872652, -1.4391992607854862, 492688767), (1395872652, 0.39460410668471724, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872651] : ((1395872651, -1.754505917080677, 492688767), (1395872651, 0.6333192178400592, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872647] : ((1395872647, -2.4983340652397046, 492609224), (1395872647, 0.6296632424393442, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872645] : ((1395872645, -2.045650001043954, 492609224), (1395872645, 0.6996536073291061, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872643] : ((1395872643, -2.025112072120659, 492609224), (1395872643, 0.7370292901043343, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872641] : ((1395872641, -3.174254321526739, 492609224), (1395872641, 0.5199231013579444, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872615] : ((1395872615, -2.6207827072617915, 492609224), (1395872615, 0.7739631824700421, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872606] : ((1395872606, -1.3409573476868577, 492688767), (1395872606, 0.7906085561160775, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872605] : ((1395872605, -2.179571468779025, 492609224), (1395872605, 0.5344030118024926, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872603] : ((1395872603, -2.0924293934074596, 492609224), (1395872603, 0.45309139134764176, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872601] : ((1395872601, -1.7443939841840785, 492688767), (1395872601, 0.5042481481862099, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872599] : ((1395872599, -1.6265371150011998, 492688767), (1395872599, 0.5835851477354116, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com args[1395872597] : ((1395872597, -2.609357972690891, 492609224), (1395872597, 0.524159410348056, 2107752395), '0.1292190272955247') We are sending mail with results at report@fotonower.com refus_total : 0.1292190272955247 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=28828426 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_P28828426_24-11-2025_14_03_30.pdf results_Auto_P28828426_24-11-2025_14_03_30.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828426_24-11-2025_14_03_30.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','28828426','results_Auto_P28828426_24-11-2025_14_03_30.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828426_24-11-2025_14_03_30.pdf','pdf','','0.89','0.1292190272955247') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/28828426

https://www.fotonower.com/image?json=false&list_photos_id=1395872773
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.0213828753451737)
https://www.fotonower.com/image?json=false&list_photos_id=1395872765
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872763
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872761
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872759
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872756
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872754
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872719
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872712
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872708
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872706
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872704
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872703
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872702
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872701
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872700
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872698
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872692
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872680
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872678
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872676
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872674
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872672
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872671
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872658
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872656
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872654
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872652
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872651
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872647
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872645
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872643
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872641
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872615
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872606
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872605
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872603
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872601
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872599
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1395872597
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 12.92%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/28831323?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/28831326?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/28831329?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/28831330?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828426_24-11-2025_14_03_30.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/28831321,28831322,28831323,28831324,28831325,28831326,28831327,28831328,28831329,28831330,28831331?tags=metal,mal_croppe,carton,environnement,flou,pet_clair,pet_fonce,background,papier,autre,pehd.


L'équipe Fotonower 202 b'' Date: Mon, 24 Nov 2025 13:03:37 GMT Content-Length: 0 Connection: close Server: nginx X-Message-Id: 02sbc61sR5yVA8clAZap4w 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 [1395872773, 1395872765, 1395872763, 1395872761, 1395872759, 1395872756, 1395872754, 1395872719, 1395872712, 1395872708, 1395872706, 1395872704, 1395872703, 1395872702, 1395872701, 1395872700, 1395872698, 1395872692, 1395872680, 1395872678, 1395872676, 1395872674, 1395872672, 1395872671, 1395872658, 1395872656, 1395872654, 1395872652, 1395872651, 1395872647, 1395872645, 1395872643, 1395872641, 1395872615, 1395872606, 1395872605, 1395872603, 1395872601, 1395872599, 1395872597] 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, '4102347') ('3318', '28828426', '1395872773', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872765', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872763', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872761', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872759', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872756', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872754', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872719', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872712', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872708', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872706', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872704', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872703', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872702', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872701', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872700', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872698', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872692', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872680', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872678', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872676', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872674', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872672', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872671', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872658', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872656', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872654', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872652', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872651', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872647', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872645', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872643', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872641', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872615', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872606', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872605', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872603', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872601', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872599', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872597', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 40 time used for this insertion : 0.02040553092956543 save_final save missing photos in datou_result : time spend for datou_step_exec : 7.715049505233765 time spend to save output : 0.020889997482299805 total time spend for step 9 : 7.7359395027160645 step10:split_time_score Mon Nov 24 14:03:38 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'}] (('09', 87),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 24112025 28828426 Nombre de photos uploadées : 87 / 23040 (0%) 24112025 28828426 Nombre de photos taguées (types de déchets): 0 / 87 (0%) 24112025 28828426 Nombre de photos taguées (volume) : 0 / 87 (0%) elapsed_time : load_data_split_time_score 2.384185791015625e-06 elapsed_time : order_list_meta_photo_and_scores 6.67572021484375e-06 ??????????????????????????????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.519890308380127 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.2363295555114746 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 : 0.1292190272955247 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828426_24-11-2025_14_03_30.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828426 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`=28828426 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828428 order by id desc limit 1 Qualite : 0.07008904145622898 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828431_24-11-2025_13_52_11.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828431 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`=28828431 AND mptpi.`type`=3594 To do Qualite : 0.25237177426268853 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828433_24-11-2025_12_21_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828433 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`=28828433 AND mptpi.`type`=3594 To do Qualite : 0.021958550347222217 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828435_24-11-2025_12_41_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828435 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`=28828435 AND mptpi.`type`=3594 To do Qualite : 0.05147855581275721 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28828444_24-11-2025_12_11_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28828444 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`=28828444 AND mptpi.`type`=3594 To do Qualite : 0.030636240206552704 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28829905_24-11-2025_13_41_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28829905 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`=28829905 AND mptpi.`type`=3594 To do Qualite : 0.03575169994212963 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28829907_24-11-2025_13_23_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28829907 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`=28829907 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'24112025': {'nb_upload': 87, '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 [1395872773, 1395872765, 1395872763, 1395872761, 1395872759, 1395872756, 1395872754, 1395872719, 1395872712, 1395872708, 1395872706, 1395872704, 1395872703, 1395872702, 1395872701, 1395872700, 1395872698, 1395872692, 1395872680, 1395872678, 1395872676, 1395872674, 1395872672, 1395872671, 1395872658, 1395872656, 1395872654, 1395872652, 1395872651, 1395872647, 1395872645, 1395872643, 1395872641, 1395872615, 1395872606, 1395872605, 1395872603, 1395872601, 1395872599, 1395872597] Looping around the photos to save general results len do output : 1 /28828426Didn'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, '4102347') ('3318', '28828426', '1395872773', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872765', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872763', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872761', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872759', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872756', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872754', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872719', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872712', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872708', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872706', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872704', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872703', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872702', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872701', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872700', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872698', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872692', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872680', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872678', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872676', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872674', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872672', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872671', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872658', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872656', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872654', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872652', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872651', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872647', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872645', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872643', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872641', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872615', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872606', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872605', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872603', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872601', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872599', None, None, None, None, None, '4102347') ('3318', None, None, None, None, None, None, None, '4102347') ('3318', '28828426', '1395872597', None, None, None, None, None, '4102347') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 41 time used for this insertion : 0.022743940353393555 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.582771062850952 time spend to save output : 0.02319955825805664 total time spend for step 10 : 2.605970621109009 caffe_path_current : About to save ! 2 After save, about to update current ! update_current_state 124.45user 41.68system 3:13.85elapsed 85%CPU (0avgtext+0avgdata 2792260maxresident)k 2639568inputs+82880outputs (15694major+2948819minor)pagefaults 0swaps