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 : 3252176 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 : ['4327901'] with mtr_portfolio_ids : ['30072108'] and first list_photo_ids : [] new path : /proc/3252176/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 15 ; length of list_pids : 15 ; length of list_args : 15 time to download the photos : 2.5403501987457275 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 Fri Jan 9 21:10:31 2026 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 : 5537 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-01-09 21:10:34.408985: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-09 21:10:34.419969: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-09 21:10:34.421388: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f6fb4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-09 21:10:34.421427: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-09 21:10:34.423611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-09 21:10:34.628969: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3815cc00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-09 21:10:34.629014: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-09 21:10:34.630067: 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 2026-01-09 21:10:34.630452: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-09 21:10:34.633312: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-09 21:10:34.635844: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-09 21:10:34.636319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-09 21:10:34.639272: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-09 21:10:34.640183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-09 21:10:34.643969: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-09 21:10:34.645002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-09 21:10:34.645058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-09 21:10:34.645625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-09 21:10:34.645638: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-09 21:10:34.645647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-09 21:10:34.646693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5062 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. 2026-01-09 21:10:34.896433: 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 2026-01-09 21:10:34.896534: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-09 21:10:34.896566: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-09 21:10:34.896595: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-09 21:10:34.896622: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-09 21:10:34.896648: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-09 21:10:34.896675: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-09 21:10:34.896702: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-09 21:10:34.898340: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-09 21:10:34.899377: 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 2026-01-09 21:10:34.899405: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-09 21:10:34.899420: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-09 21:10:34.899434: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-09 21:10:34.899447: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-09 21:10:34.899461: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-09 21:10:34.899475: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-09 21:10:34.899488: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-09 21:10:34.900379: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-09 21:10:34.900405: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-09 21:10:34.900414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-09 21:10:34.900421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-09 21:10:34.901428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5062 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 : [] 2026-01-09 21:10:46.955302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-09 21:10:47.137582: 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 : 15 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 13 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 13 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 12 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 14 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 27 NEW PHOTO Processing 1 images image shape: (2160, 3840, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3840.00000 nb d'objets trouves : 17 Detection mask done ! Trying to reset tf kernel 3252702 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 248 tf kernel not reseted sub process len(results) : 15 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 15 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 : 5537 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 = [174, 642, 474, 834] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008955001831054688 nb_pixel_total : 30630 time to create 1 rle with old method : 0.03359103202819824 length of segment : 273 DEBUG bbox = [1500, 960, 1800, 1284] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006988048553466797 nb_pixel_total : 28248 time to create 1 rle with old method : 0.030947208404541016 length of segment : 369 DEBUG bbox = [1980, 1386, 2148, 1596] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003445148468017578 nb_pixel_total : 25075 time to create 1 rle with old method : 0.027022600173950195 length of segment : 166 DEBUG bbox = [12, 132, 570, 306] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008139610290527344 nb_pixel_total : 43364 time to create 1 rle with old method : 0.046956777572631836 length of segment : 533 DEBUG bbox = [0, 318, 792, 1332] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.018465042114257812 nb_pixel_total : 375838 time to create 1 rle with new method : 0.04275345802307129 length of segment : 756 DEBUG bbox = [24, 2268, 336, 2394] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005049705505371094 nb_pixel_total : 19007 time to create 1 rle with old method : 0.020778417587280273 length of segment : 310 DEBUG bbox = [24, 120, 522, 522] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015065670013427734 nb_pixel_total : 85524 time to create 1 rle with old method : 0.09371399879455566 length of segment : 486 DEBUG bbox = [1896, 1788, 2040, 1980] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0002663135528564453 nb_pixel_total : 11594 time to create 1 rle with old method : 0.013247251510620117 length of segment : 162 DEBUG bbox = [192, 2784, 660, 3030] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009164810180664062 nb_pixel_total : 36925 time to create 1 rle with old method : 0.040912628173828125 length of segment : 453 DEBUG bbox = [1686, 1614, 2004, 1980] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008885860443115234 nb_pixel_total : 49421 time to create 1 rle with old method : 0.053794145584106445 length of segment : 469 DEBUG bbox = [1272, 714, 1752, 1362] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002724885940551758 nb_pixel_total : 199574 time to create 1 rle with new method : 0.010040521621704102 length of segment : 552 DEBUG bbox = [1212, 1770, 1542, 1956] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006122589111328125 nb_pixel_total : 28535 time to create 1 rle with old method : 0.03087019920349121 length of segment : 327 DEBUG bbox = [732, 276, 906, 396] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00029015541076660156 nb_pixel_total : 9304 time to create 1 rle with old method : 0.010683536529541016 length of segment : 148 DEBUG bbox = [336, 3192, 672, 3390] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006444454193115234 nb_pixel_total : 28879 time to create 1 rle with old method : 0.03189706802368164 length of segment : 340 DEBUG bbox = [240, 3324, 498, 3396] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00021600723266601562 nb_pixel_total : 11989 time to create 1 rle with old method : 0.013356208801269531 length of segment : 256 DEBUG bbox = [996, 1362, 1410, 1788] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0016214847564697266 nb_pixel_total : 71794 time to create 1 rle with old method : 0.07856059074401855 length of segment : 375 DEBUG bbox = [390, 90, 594, 264] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004019737243652344 nb_pixel_total : 24883 time to create 1 rle with old method : 0.02788853645324707 length of segment : 190 DEBUG bbox = [354, 84, 594, 252] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00043082237243652344 nb_pixel_total : 28020 time to create 1 rle with old method : 0.0310361385345459 length of segment : 231 DEBUG bbox = [1638, 546, 2082, 1272] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0035033226013183594 nb_pixel_total : 163503 time to create 1 rle with new method : 0.01448678970336914 length of segment : 420 DEBUG bbox = [1788, 1008, 2088, 1368] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009372234344482422 nb_pixel_total : 58512 time to create 1 rle with old method : 0.06615042686462402 length of segment : 309 DEBUG bbox = [138, 2106, 522, 2442] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009241104125976562 nb_pixel_total : 43115 time to create 1 rle with old method : 0.048038482666015625 length of segment : 360 DEBUG bbox = [2004, 966, 2148, 1176] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003190040588378906 nb_pixel_total : 19780 time to create 1 rle with old method : 0.021804332733154297 length of segment : 141 DEBUG bbox = [1056, 336, 1638, 546] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00121307373046875 nb_pixel_total : 91236 time to create 1 rle with old method : 0.09827446937561035 length of segment : 553 DEBUG bbox = [366, 1206, 564, 1380] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003407001495361328 nb_pixel_total : 16090 time to create 1 rle with old method : 0.017783164978027344 length of segment : 183 DEBUG bbox = [96, 2796, 624, 3180] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0017039775848388672 nb_pixel_total : 111995 time to create 1 rle with old method : 0.12625813484191895 length of segment : 475 DEBUG bbox = [786, 510, 1272, 942] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001695394515991211 nb_pixel_total : 125903 time to create 1 rle with old method : 0.1373581886291504 length of segment : 458 DEBUG bbox = [1308, 1332, 1470, 1506] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0002942085266113281 nb_pixel_total : 19319 time to create 1 rle with old method : 0.021084070205688477 length of segment : 153 DEBUG bbox = [456, 246, 870, 426] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006158351898193359 nb_pixel_total : 38281 time to create 1 rle with old method : 0.04105544090270996 length of segment : 367 DEBUG bbox = [1050, 660, 1608, 1374] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.004208087921142578 nb_pixel_total : 244918 time to create 1 rle with new method : 0.011033296585083008 length of segment : 523 DEBUG bbox = [156, 1626, 366, 1830] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00040459632873535156 nb_pixel_total : 22724 time to create 1 rle with old method : 0.02625274658203125 length of segment : 198 DEBUG bbox = [372, 2892, 510, 3060] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003402233123779297 nb_pixel_total : 13340 time to create 1 rle with old method : 0.02220439910888672 length of segment : 117 DEBUG bbox = [1524, 2808, 1968, 3084] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0012962818145751953 nb_pixel_total : 65998 time to create 1 rle with old method : 0.08895611763000488 length of segment : 415 DEBUG bbox = [336, 1392, 648, 1524] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00036144256591796875 nb_pixel_total : 15011 time to create 1 rle with old method : 0.017348289489746094 length of segment : 320 DEBUG bbox = [1350, 1848, 1764, 2388] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00189971923828125 nb_pixel_total : 142104 time to create 1 rle with old method : 0.15882253646850586 length of segment : 457 DEBUG bbox = [522, 648, 702, 768] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000217437744140625 nb_pixel_total : 11903 time to create 1 rle with old method : 0.013520240783691406 length of segment : 147 DEBUG bbox = [366, 246, 834, 378] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005624294281005859 nb_pixel_total : 41223 time to create 1 rle with old method : 0.04662799835205078 length of segment : 410 DEBUG bbox = [1194, 2814, 1710, 3456] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002228975296020508 nb_pixel_total : 156054 time to create 1 rle with new method : 0.009070634841918945 length of segment : 434 DEBUG bbox = [2034, 2736, 2130, 2856] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00013184547424316406 nb_pixel_total : 6772 time to create 1 rle with old method : 0.0077474117279052734 length of segment : 92 DEBUG bbox = [1254, 1410, 1440, 1578] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0002560615539550781 nb_pixel_total : 17066 time to create 1 rle with old method : 0.022342920303344727 length of segment : 171 DEBUG bbox = [1032, 1716, 1326, 2160] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015692710876464844 nb_pixel_total : 79605 time to create 1 rle with old method : 0.08758139610290527 length of segment : 261 DEBUG bbox = [426, 2220, 708, 2412] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008115768432617188 nb_pixel_total : 25894 time to create 1 rle with old method : 0.0285489559173584 length of segment : 272 DEBUG bbox = [78, 1122, 354, 1494] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0012803077697753906 nb_pixel_total : 60976 time to create 1 rle with old method : 0.06849956512451172 length of segment : 249 DEBUG bbox = [654, 1326, 858, 1620] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008432865142822266 nb_pixel_total : 31473 time to create 1 rle with old method : 0.03513956069946289 length of segment : 202 DEBUG bbox = [270, 2316, 726, 2682] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0020322799682617188 nb_pixel_total : 100969 time to create 1 rle with old method : 0.11391448974609375 length of segment : 429 DEBUG bbox = [1146, 2160, 2118, 2970] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.31651949882507324 nb_pixel_total : 506861 time to create 1 rle with new method : 0.03513669967651367 length of segment : 1071 DEBUG bbox = [558, 2508, 948, 2910] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002008199691772461 nb_pixel_total : 77189 time to create 1 rle with old method : 0.08624553680419922 length of segment : 381 DEBUG bbox = [954, 402, 1254, 1200] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0025184154510498047 nb_pixel_total : 122290 time to create 1 rle with old method : 0.14023470878601074 length of segment : 243 DEBUG bbox = [168, 2496, 558, 2820] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002095460891723633 nb_pixel_total : 75202 time to create 1 rle with old method : 0.10705137252807617 length of segment : 478 DEBUG bbox = [12, 1686, 456, 2430] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0037131309509277344 nb_pixel_total : 234496 time to create 1 rle with new method : 0.007938623428344727 length of segment : 433 DEBUG bbox = [162, 2340, 486, 2592] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005586147308349609 nb_pixel_total : 26865 time to create 1 rle with old method : 0.030031919479370117 length of segment : 238 DEBUG bbox = [558, 1434, 756, 1836] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0011010169982910156 nb_pixel_total : 59684 time to create 1 rle with old method : 0.06610560417175293 length of segment : 192 DEBUG bbox = [1284, 1122, 1458, 1350] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005710124969482422 nb_pixel_total : 19142 time to create 1 rle with old method : 0.022307157516479492 length of segment : 133 DEBUG bbox = [1074, 1776, 1302, 1980] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007236003875732422 nb_pixel_total : 15676 time to create 1 rle with old method : 0.018213272094726562 length of segment : 254 DEBUG bbox = [288, 1494, 654, 1734] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0013706684112548828 nb_pixel_total : 59717 time to create 1 rle with old method : 0.06666159629821777 length of segment : 348 DEBUG bbox = [1710, 1668, 2016, 1884] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00104522705078125 nb_pixel_total : 41485 time to create 1 rle with old method : 0.04707813262939453 length of segment : 290 DEBUG bbox = [990, 1122, 1248, 1356] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0009744167327880859 nb_pixel_total : 33707 time to create 1 rle with old method : 0.038301706314086914 length of segment : 249 DEBUG bbox = [1074, 1344, 1344, 1584] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007379055023193359 nb_pixel_total : 41931 time to create 1 rle with old method : 0.04640793800354004 length of segment : 258 DEBUG bbox = [108, 408, 678, 942] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0031900405883789062 nb_pixel_total : 175956 time to create 1 rle with new method : 0.004983186721801758 length of segment : 536 DEBUG bbox = [990, 2412, 1224, 2676] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008080005645751953 nb_pixel_total : 34559 time to create 1 rle with old method : 0.03958582878112793 length of segment : 246 DEBUG bbox = [426, 948, 684, 1188] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006289482116699219 nb_pixel_total : 36355 time to create 1 rle with old method : 0.040293216705322266 length of segment : 243 DEBUG bbox = [0, 2856, 204, 3270] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0010251998901367188 nb_pixel_total : 35758 time to create 1 rle with old method : 0.04123735427856445 length of segment : 203 DEBUG bbox = [1686, 168, 1884, 498] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007917881011962891 nb_pixel_total : 28872 time to create 1 rle with old method : 0.034679412841796875 length of segment : 163 DEBUG bbox = [1332, 1734, 1578, 1950] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008976459503173828 nb_pixel_total : 31118 time to create 1 rle with old method : 0.035338640213012695 length of segment : 202 DEBUG bbox = [1704, 1746, 1908, 1944] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007004737854003906 nb_pixel_total : 22427 time to create 1 rle with old method : 0.026195764541625977 length of segment : 198 DEBUG bbox = [1566, 1830, 1686, 1986] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003905296325683594 nb_pixel_total : 10285 time to create 1 rle with old method : 0.014854669570922852 length of segment : 102 DEBUG bbox = [1152, 1080, 1356, 1314] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0008528232574462891 nb_pixel_total : 35050 time to create 1 rle with old method : 0.04035472869873047 length of segment : 190 DEBUG bbox = [1110, 1284, 1380, 1446] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0006573200225830078 nb_pixel_total : 36182 time to create 1 rle with old method : 0.04190993309020996 length of segment : 256 DEBUG bbox = [708, 1200, 1146, 1620] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0025177001953125 nb_pixel_total : 105526 time to create 1 rle with old method : 0.11896514892578125 length of segment : 379 DEBUG bbox = [1794, 1530, 1920, 1710] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00033926963806152344 nb_pixel_total : 11687 time to create 1 rle with old method : 0.013606071472167969 length of segment : 110 DEBUG bbox = [390, 2316, 714, 2664] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0016338825225830078 nb_pixel_total : 76026 time to create 1 rle with old method : 0.0869913101196289 length of segment : 509 DEBUG bbox = [330, 1272, 714, 1476] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0013298988342285156 nb_pixel_total : 42560 time to create 1 rle with old method : 0.0477290153503418 length of segment : 375 DEBUG bbox = [360, 2274, 696, 2436] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005936622619628906 nb_pixel_total : 35958 time to create 1 rle with old method : 0.04002213478088379 length of segment : 308 DEBUG bbox = [954, 1854, 1062, 1974] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003147125244140625 nb_pixel_total : 8993 time to create 1 rle with old method : 0.010042190551757812 length of segment : 97 DEBUG bbox = [972, 1350, 1422, 2040] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0036296844482421875 nb_pixel_total : 134277 time to create 1 rle with old method : 0.1486985683441162 length of segment : 388 DEBUG bbox = [1968, 1896, 2154, 2070] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005447864532470703 nb_pixel_total : 25487 time to create 1 rle with old method : 0.027901411056518555 length of segment : 173 DEBUG bbox = [786, 1620, 1056, 1788] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005815029144287109 nb_pixel_total : 20750 time to create 1 rle with old method : 0.02265191078186035 length of segment : 256 DEBUG bbox = [1380, 558, 1662, 930] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0012679100036621094 nb_pixel_total : 50786 time to create 1 rle with old method : 0.07939743995666504 length of segment : 241 DEBUG bbox = [414, 198, 558, 336] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0003733634948730469 nb_pixel_total : 11637 time to create 1 rle with old method : 0.012845754623413086 length of segment : 121 DEBUG bbox = [1872, 1512, 2142, 1908] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.001767873764038086 nb_pixel_total : 58117 time to create 1 rle with old method : 0.06525373458862305 length of segment : 272 DEBUG bbox = [882, 570, 1506, 1296] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00606226921081543 nb_pixel_total : 171315 time to create 1 rle with new method : 0.012909650802612305 length of segment : 759 DEBUG bbox = [138, 1170, 324, 1368] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0007543563842773438 nb_pixel_total : 20382 time to create 1 rle with old method : 0.023164749145507812 length of segment : 164 DEBUG bbox = [1770, 612, 2160, 1884] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.005695343017578125 nb_pixel_total : 178696 time to create 1 rle with new method : 0.015146732330322266 length of segment : 602 DEBUG bbox = [66, 1908, 444, 2106] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0015382766723632812 nb_pixel_total : 48103 time to create 1 rle with old method : 0.053379058837890625 length of segment : 308 DEBUG bbox = [1134, 1410, 1302, 1578] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.000545501708984375 nb_pixel_total : 13015 time to create 1 rle with old method : 0.014843940734863281 length of segment : 122 DEBUG bbox = [1266, 1200, 1440, 1416] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0005099773406982422 nb_pixel_total : 25610 time to create 1 rle with old method : 0.029049158096313477 length of segment : 151 DEBUG bbox = [1812, 594, 1932, 744] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.00022983551025390625 nb_pixel_total : 11418 time to create 1 rle with old method : 0.013307571411132812 length of segment : 115 DEBUG bbox = [1386, 1386, 1776, 1860] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.002649068832397461 nb_pixel_total : 116348 time to create 1 rle with old method : 0.12902545928955078 length of segment : 354 DEBUG bbox = [396, 2358, 846, 2778] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0026407241821289062 nb_pixel_total : 113242 time to create 1 rle with old method : 0.1238868236541748 length of segment : 406 DEBUG bbox = [1026, 2676, 1680, 3036] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.003854513168334961 nb_pixel_total : 196270 time to create 1 rle with new method : 0.006615877151489258 length of segment : 633 DEBUG bbox = [2016, 1968, 2160, 2160] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0004858970642089844 nb_pixel_total : 15604 time to create 1 rle with old method : 0.0181577205657959 length of segment : 133 DEBUG bbox = [1716, 2688, 2082, 3018] DEBUG masks shape = (2160, 3840) time for calcul the mask position with numpy : 0.0016162395477294922 nb_pixel_total : 40354 time to create 1 rle with old method : 0.045508623123168945 length of segment : 467 time spent for convertir_results : 8.802354097366333 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 91 chid ids of type : 3594 Number RLEs to save : 28862 save missing photos in datou_result : time spend for datou_step_exec : 75.83499073982239 time spend to save output : 1.7893564701080322 total time spend for step 1 : 77.62434720993042 step2:crop_condition Fri Jan 9 21:11:49 2026 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 : 15 ! batch 1 Loaded 91 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 35 About to insert : list_path_to_insert length 35 new photo from crops ! About to upload 35 photos upload in portfolio : 3736932 init cache_photo without model_param we have 35 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1767989518_3252176 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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311_rle_crop_4095539979_0.png', 0, 156, 273, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311_rle_crop_4095539980_0.png', 0, 309, 267, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311_rle_crop_4095539982_0.png', 0, 158, 518, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539986_0.png', 0, 178, 127, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539987_0.png', 0, 198, 443, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539988_0.png', 0, 350, 301, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539990_0.png', 0, 177, 327, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539992_0.png', 0, 188, 328, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539993_0.png', 0, 71, 254, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539994_0.png', 0, 392, 372, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895_rle_crop_4095540002_0.png', 0, 154, 183, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0_rle_crop_4095540004_0.png', 0, 428, 431, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0_rle_crop_4095540005_0.png', 0, 172, 149, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0_rle_crop_4095540006_0.png', 0, 174, 367, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540011_0.png', 0, 112, 237, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5_rle_crop_4095540016_0.png', 0, 103, 92, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5_rle_crop_4095540017_0.png', 0, 153, 171, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193566_2d0aa04c7a4e06d910f2416fdf51bb64_rle_crop_4095540021_0.png', 0, 262, 202, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540026_0.png', 0, 279, 355, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540027_0.png', 0, 694, 432, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540028_0.png', 0, 227, 254, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540030_0.png', 0, 211, 132, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193560_1a8f036e21f6a1c19bfc693116024321_rle_crop_4095540033_0.png', 0, 194, 288, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193559_168c2820cd32b1e6d0087da0ca14e4ed_rle_crop_4095540037_0.png', 0, 260, 229, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193559_168c2820cd32b1e6d0087da0ca14e4ed_rle_crop_4095540038_0.png', 0, 228, 238, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540042_0.png', 0, 173, 198, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540043_0.png', 0, 137, 99, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540047_0.png', 0, 175, 104, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb_rle_crop_4095540053_0.png', 0, 160, 173, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb_rle_crop_4095540054_0.png', 0, 145, 255, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540059_0.png', 0, 191, 158, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540062_0.png', 0, 156, 119, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540064_0.png', 0, 135, 115, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540066_0.png', 0, 419, 404, 0, 1767989524,'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(1767989524), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540068_0.png', 0, 166, 132, 0, 1767989524,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 35 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.562105655670166 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 ! 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/1767989531_3252176 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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539984_0.png', 0, 109, 307, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539989_0.png', 0, 626, 474, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539991_0.png', 0, 93, 148, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540008_0.png', 0, 172, 192, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540009_0.png', 0, 161, 116, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540013_0.png', 0, 110, 147, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5_rle_crop_4095540018_0.png', 0, 394, 258, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5_rle_crop_4095540019_0.png', 0, 177, 272, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540024_0.png', 0, 360, 350, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193560_1a8f036e21f6a1c19bfc693116024321_rle_crop_4095540031_0.png', 0, 154, 198, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193560_1a8f036e21f6a1c19bfc693116024321_rle_crop_4095540034_0.png', 0, 224, 249, 0, 1767989533,'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(1767989533), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb_rle_crop_4095540052_0.png', 0, 637, 387, 0, 1767989533,'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 : 3.0158851146698 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1767989536_3252176 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(1767989537), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539995_0.png', 0, 166, 190, 0, 1767989537,'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(1767989537), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193560_1a8f036e21f6a1c19bfc693116024321_rle_crop_4095540035_0.png', 0, 217, 246, 0, 1767989537,'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(1767989537), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb_rle_crop_4095540056_0.png', 0, 119, 121, 0, 1767989537,'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(1767989537), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540061_0.png', 0, 182, 306, 0, 1767989537,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.3219201564788818 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 34 About to insert : list_path_to_insert length 34 new photo from crops ! About to upload 34 photos upload in portfolio : 3736932 init cache_photo without model_param we have 34 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1767989554_3252176 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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311_rle_crop_4095539981_0.png', 0, 181, 165, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539983_0.png', 0, 914, 709, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539985_0.png', 0, 342, 458, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539996_0.png', 0, 163, 224, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539997_0.png', 0, 676, 341, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895_rle_crop_4095539998_0.png', 0, 342, 283, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895_rle_crop_4095539999_0.png', 0, 324, 327, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895_rle_crop_4095540000_0.png', 0, 185, 141, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895_rle_crop_4095540001_0.png', 0, 205, 551, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895_rle_crop_4095540003_0.png', 0, 349, 464, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0_rle_crop_4095540007_0.png', 0, 672, 523, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540010_0.png', 0, 235, 406, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540012_0.png', 0, 436, 408, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540014_0.png', 0, 113, 410, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5_rle_crop_4095540015_0.png', 0, 507, 434, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5_rle_crop_4095540020_0.png', 0, 333, 244, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193566_2d0aa04c7a4e06d910f2416fdf51bb64_rle_crop_4095540022_0.png', 0, 336, 428, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193566_2d0aa04c7a4e06d910f2416fdf51bb64_rle_crop_4095540023_0.png', 0, 735, 876, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540025_0.png', 0, 738, 242, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193560_1a8f036e21f6a1c19bfc693116024321_rle_crop_4095540032_0.png', 0, 228, 348, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193559_168c2820cd32b1e6d0087da0ca14e4ed_rle_crop_4095540036_0.png', 0, 468, 521, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193559_168c2820cd32b1e6d0087da0ca14e4ed_rle_crop_4095540039_0.png', 0, 350, 165, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193559_168c2820cd32b1e6d0087da0ca14e4ed_rle_crop_4095540040_0.png', 0, 272, 162, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540046_0.png', 0, 412, 378, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540048_0.png', 0, 344, 285, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540049_0.png', 0, 202, 374, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540050_0.png', 0, 161, 308, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb_rle_crop_4095540055_0.png', 0, 327, 241, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540057_0.png', 0, 371, 260, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540058_0.png', 0, 613, 518, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540060_0.png', 0, 1064, 333, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540065_0.png', 0, 441, 354, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540067_0.png', 0, 354, 616, 0, 1767989561,'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(1767989561), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540069_0.png', 0, 300, 313, 0, 1767989561,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 34 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.420685768127441 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1767989565_3252176 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(1767989566), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540041_0.png', 0, 195, 202, 0, 1767989566,'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(1767989566), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540044_0.png', 0, 227, 190, 0, 1767989566,'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(1767989566), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540045_0.png', 0, 161, 254, 0, 1767989566,'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(1767989566), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540051_0.png', 0, 107, 97, 0, 1767989566,'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(1767989566), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540063_0.png', 0, 214, 150, 0, 1767989566,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.3587291240692139 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1767989568_3252176 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(1767989568), 0.0, 0.0, 14, '', 0, 0, '1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540029_0.png', 0, 392, 191, 0, 1767989568,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6517660617828369 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles 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 [1403193575, 1403193573, 1403193571, 1403193570, 1403193569, 1403193568, 1403193567, 1403193566, 1403193562, 1403193560, 1403193559, 1403193558, 1403193556, 1403193555, 1403193472] Looping around the photos to save general results len do output : 91 /1403196291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196305Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196307Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196309Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196311Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196314Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196315Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196318Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196320Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196321Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196323Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196324Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196325Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196326Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196327Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196328Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196329Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196330Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196331Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196332Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196334Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196341Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196485Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1403196528Didn'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, '4327901') ('3318', '30072108', '1403193575', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193573', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193571', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193570', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193569', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193568', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193567', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193566', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193562', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193560', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193559', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193558', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193556', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193555', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193472', None, None, None, None, None, '4327901') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 288 time used for this insertion : 0.07454395294189453 save_final save missing photos in datou_result : time spend for datou_step_exec : 59.57305383682251 time spend to save output : 0.07765817642211914 total time spend for step 2 : 59.65071201324463 step3:rle_unique_nms_with_priority Fri Jan 9 21:12:48 2026 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 91 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 2.643749713897705 time for calcul the mask position with numpy : 0.927722692489624 nb_pixel_total : 8167083 time to create 1 rle with new method : 0.899014949798584 time for calcul the mask position with numpy : 0.023983001708984375 nb_pixel_total : 43364 time to create 1 rle with old method : 0.049282073974609375 time for calcul the mask position with numpy : 0.02243661880493164 nb_pixel_total : 25075 time to create 1 rle with old method : 0.027275562286376953 time for calcul the mask position with numpy : 0.02244281768798828 nb_pixel_total : 28248 time to create 1 rle with old method : 0.030338048934936523 time for calcul the mask position with numpy : 0.029955148696899414 nb_pixel_total : 30630 time to create 1 rle with old method : 0.032631874084472656 create new chi : 2.0998332500457764 time to delete rle : 0.0214688777923584 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 4842 TO DO : save crop sub photo not yet done ! save time : 0.34441113471984863 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 3.9754209518432617 time for calcul the mask position with numpy : 0.58833909034729 nb_pixel_total : 7722418 time to create 1 rle with new method : 0.8787839412689209 time for calcul the mask position with numpy : 0.024206161499023438 nb_pixel_total : 43094 time to create 1 rle with old method : 0.047470808029174805 time for calcul the mask position with numpy : 0.02419567108154297 nb_pixel_total : 36925 time to create 1 rle with old method : 0.04050254821777344 time for calcul the mask position with numpy : 0.023032665252685547 nb_pixel_total : 11594 time to create 1 rle with old method : 0.012447357177734375 time for calcul the mask position with numpy : 0.022713422775268555 nb_pixel_total : 85524 time to create 1 rle with old method : 0.09051942825317383 time for calcul the mask position with numpy : 0.022366762161254883 nb_pixel_total : 19007 time to create 1 rle with old method : 0.020336627960205078 time for calcul the mask position with numpy : 0.03368520736694336 nb_pixel_total : 375838 time to create 1 rle with new method : 1.0051028728485107 create new chi : 2.8956539630889893 time to delete rle : 0.0009329319000244141 batch 1 Loaded 13 chid ids of type : 3594 ++++++++Number RLEs to save : 7214 TO DO : save crop sub photo not yet done ! save time : 0.47560882568359375 nb_obj : 9 nb_hashtags : 4 time to prepare the origin masks : 5.3843605518341064 time for calcul the mask position with numpy : 0.7642941474914551 nb_pixel_total : 7752237 time to create 1 rle with new method : 0.8707675933837891 time for calcul the mask position with numpy : 0.022892236709594727 nb_pixel_total : 163503 time to create 1 rle with new method : 0.7056448459625244 time for calcul the mask position with numpy : 0.023567676544189453 nb_pixel_total : 3703 time to create 1 rle with old method : 0.004315376281738281 time for calcul the mask position with numpy : 0.022634506225585938 nb_pixel_total : 24883 time to create 1 rle with old method : 0.026933908462524414 time for calcul the mask position with numpy : 0.02263355255126953 nb_pixel_total : 71794 time to create 1 rle with old method : 0.07636356353759766 time for calcul the mask position with numpy : 0.02314591407775879 nb_pixel_total : 11988 time to create 1 rle with old method : 0.01292872428894043 time for calcul the mask position with numpy : 0.02377486228942871 nb_pixel_total : 28879 time to create 1 rle with old method : 0.030710458755493164 time for calcul the mask position with numpy : 0.02259659767150879 nb_pixel_total : 9304 time to create 1 rle with old method : 0.009956836700439453 time for calcul the mask position with numpy : 0.022815227508544922 nb_pixel_total : 28535 time to create 1 rle with old method : 0.031123638153076172 time for calcul the mask position with numpy : 0.024741411209106445 nb_pixel_total : 199574 time to create 1 rle with new method : 0.8121602535247803 create new chi : 3.6374900341033936 time to delete rle : 0.0012624263763427734 batch 1 Loaded 19 chid ids of type : 3594 ++++++++++++Number RLEs to save : 7615 TO DO : save crop sub photo not yet done ! save time : 0.5106072425842285 nb_obj : 6 nb_hashtags : 2 time to prepare the origin masks : 3.3279666900634766 time for calcul the mask position with numpy : 0.6040832996368408 nb_pixel_total : 7953672 time to create 1 rle with new method : 0.8651373386383057 time for calcul the mask position with numpy : 0.023679733276367188 nb_pixel_total : 111995 time to create 1 rle with old method : 0.1191873550415039 time for calcul the mask position with numpy : 0.022577524185180664 nb_pixel_total : 16090 time to create 1 rle with old method : 0.01736927032470703 time for calcul the mask position with numpy : 0.023145437240600586 nb_pixel_total : 91236 time to create 1 rle with old method : 0.0984487533569336 time for calcul the mask position with numpy : 0.022957563400268555 nb_pixel_total : 19780 time to create 1 rle with old method : 0.02166748046875 time for calcul the mask position with numpy : 0.02752399444580078 nb_pixel_total : 43115 time to create 1 rle with old method : 0.047762155532836914 time for calcul the mask position with numpy : 0.03715705871582031 nb_pixel_total : 58512 time to create 1 rle with old method : 0.06318402290344238 create new chi : 2.0328524112701416 time to delete rle : 0.0008339881896972656 batch 1 Loaded 13 chid ids of type : 3594 +++++++Number RLEs to save : 6202 TO DO : save crop sub photo not yet done ! save time : 0.437727689743042 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 2.5632779598236084 time for calcul the mask position with numpy : 0.9991624355316162 nb_pixel_total : 7865979 time to create 1 rle with new method : 0.7706177234649658 time for calcul the mask position with numpy : 0.024193525314331055 nb_pixel_total : 244918 time to create 1 rle with new method : 1.0197420120239258 time for calcul the mask position with numpy : 0.038993120193481445 nb_pixel_total : 38281 time to create 1 rle with old method : 0.04465913772583008 time for calcul the mask position with numpy : 0.03713512420654297 nb_pixel_total : 19319 time to create 1 rle with old method : 0.022061586380004883 time for calcul the mask position with numpy : 0.03491663932800293 nb_pixel_total : 125903 time to create 1 rle with old method : 0.1413745880126953 create new chi : 3.196011543273926 time to delete rle : 0.0006954669952392578 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 5162 TO DO : save crop sub photo not yet done ! save time : 0.37535715103149414 nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 5.229079961776733 time for calcul the mask position with numpy : 1.1312661170959473 nb_pixel_total : 7982097 time to create 1 rle with new method : 0.9320907592773438 time for calcul the mask position with numpy : 0.03630709648132324 nb_pixel_total : 41223 time to create 1 rle with old method : 0.04477190971374512 time for calcul the mask position with numpy : 0.02239060401916504 nb_pixel_total : 11903 time to create 1 rle with old method : 0.013091325759887695 time for calcul the mask position with numpy : 0.02481389045715332 nb_pixel_total : 142104 time to create 1 rle with old method : 0.15621447563171387 time for calcul the mask position with numpy : 0.02438974380493164 nb_pixel_total : 15011 time to create 1 rle with old method : 0.01694488525390625 time for calcul the mask position with numpy : 0.024991273880004883 nb_pixel_total : 65998 time to create 1 rle with old method : 0.07410502433776855 time for calcul the mask position with numpy : 0.02475118637084961 nb_pixel_total : 13340 time to create 1 rle with old method : 0.014721393585205078 time for calcul the mask position with numpy : 0.023456335067749023 nb_pixel_total : 22724 time to create 1 rle with old method : 0.02524590492248535 create new chi : 2.6283626556396484 time to delete rle : 0.0009312629699707031 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 6288 TO DO : save crop sub photo not yet done ! save time : 0.43094682693481445 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 3.852668285369873 time for calcul the mask position with numpy : 0.8210129737854004 nb_pixel_total : 7948033 time to create 1 rle with new method : 0.9713146686553955 time for calcul the mask position with numpy : 0.023592233657836914 nb_pixel_total : 60976 time to create 1 rle with old method : 0.06636643409729004 time for calcul the mask position with numpy : 0.024510860443115234 nb_pixel_total : 25894 time to create 1 rle with old method : 0.03690934181213379 time for calcul the mask position with numpy : 0.028994321823120117 nb_pixel_total : 79605 time to create 1 rle with old method : 0.09763789176940918 time for calcul the mask position with numpy : 0.023055315017700195 nb_pixel_total : 17066 time to create 1 rle with old method : 0.018622159957885742 time for calcul the mask position with numpy : 0.022956132888793945 nb_pixel_total : 6772 time to create 1 rle with old method : 0.00732731819152832 time for calcul the mask position with numpy : 0.024442195892333984 nb_pixel_total : 156054 time to create 1 rle with new method : 0.8875405788421631 create new chi : 3.1173999309539795 time to delete rle : 0.0007283687591552734 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 5118 TO DO : save crop sub photo not yet done ! save time : 0.377962589263916 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 1.92301344871521 time for calcul the mask position with numpy : 1.0223121643066406 nb_pixel_total : 7655097 time to create 1 rle with new method : 1.1149721145629883 time for calcul the mask position with numpy : 0.027579545974731445 nb_pixel_total : 506861 time to create 1 rle with new method : 0.8739492893218994 time for calcul the mask position with numpy : 0.024689435958862305 nb_pixel_total : 100969 time to create 1 rle with old method : 0.11108851432800293 time for calcul the mask position with numpy : 0.0241549015045166 nb_pixel_total : 31473 time to create 1 rle with old method : 0.03492379188537598 create new chi : 3.300766944885254 time to delete rle : 0.0009479522705078125 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 5564 TO DO : save crop sub photo not yet done ! save time : 0.412930965423584 nb_obj : 7 nb_hashtags : 4 time to prepare the origin masks : 5.090221405029297 time for calcul the mask position with numpy : 0.8676800727844238 nb_pixel_total : 7679532 time to create 1 rle with new method : 1.006606101989746 time for calcul the mask position with numpy : 0.02258014678955078 nb_pixel_total : 19142 time to create 1 rle with old method : 0.021028518676757812 time for calcul the mask position with numpy : 0.023468017578125 nb_pixel_total : 59684 time to create 1 rle with old method : 0.06402873992919922 time for calcul the mask position with numpy : 0.022241592407226562 nb_pixel_total : 26865 time to create 1 rle with old method : 0.028548717498779297 time for calcul the mask position with numpy : 0.02541208267211914 nb_pixel_total : 234496 time to create 1 rle with new method : 0.9611475467681885 time for calcul the mask position with numpy : 0.02396869659423828 nb_pixel_total : 75202 time to create 1 rle with old method : 0.07884693145751953 time for calcul the mask position with numpy : 0.023277997970581055 nb_pixel_total : 122290 time to create 1 rle with old method : 0.1288762092590332 time for calcul the mask position with numpy : 0.02303171157836914 nb_pixel_total : 77189 time to create 1 rle with old method : 0.08275938034057617 create new chi : 3.462898015975952 time to delete rle : 0.0008885860443115234 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 6356 TO DO : save crop sub photo not yet done ! save time : 0.45851778984069824 nb_obj : 5 nb_hashtags : 4 time to prepare the origin masks : 3.118847131729126 time for calcul the mask position with numpy : 0.7495720386505127 nb_pixel_total : 8101884 time to create 1 rle with new method : 1.1583137512207031 time for calcul the mask position with numpy : 0.02298283576965332 nb_pixel_total : 41931 time to create 1 rle with old method : 0.04500389099121094 time for calcul the mask position with numpy : 0.022701740264892578 nb_pixel_total : 33707 time to create 1 rle with old method : 0.036458492279052734 time for calcul the mask position with numpy : 0.02263164520263672 nb_pixel_total : 41485 time to create 1 rle with old method : 0.044554948806762695 time for calcul the mask position with numpy : 0.024095535278320312 nb_pixel_total : 59717 time to create 1 rle with old method : 0.06621670722961426 time for calcul the mask position with numpy : 0.023614168167114258 nb_pixel_total : 15676 time to create 1 rle with old method : 0.016916513442993164 create new chi : 2.2733981609344482 time to delete rle : 0.0006754398345947266 batch 1 Loaded 11 chid ids of type : 3594 ++++++Number RLEs to save : 4958 TO DO : save crop sub photo not yet done ! save time : 0.355083703994751 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 2.9517533779144287 time for calcul the mask position with numpy : 1.0958523750305176 nb_pixel_total : 7982900 time to create 1 rle with new method : 1.2151193618774414 time for calcul the mask position with numpy : 0.025455236434936523 nb_pixel_total : 28872 time to create 1 rle with old method : 0.03705644607543945 time for calcul the mask position with numpy : 0.02435135841369629 nb_pixel_total : 35758 time to create 1 rle with old method : 0.03874468803405762 time for calcul the mask position with numpy : 0.023691415786743164 nb_pixel_total : 36355 time to create 1 rle with old method : 0.04091811180114746 time for calcul the mask position with numpy : 0.02445220947265625 nb_pixel_total : 34559 time to create 1 rle with old method : 0.039255619049072266 time for calcul the mask position with numpy : 0.027156352996826172 nb_pixel_total : 175956 time to create 1 rle with new method : 1.0174696445465088 create new chi : 3.6753177642822266 time to delete rle : 0.0007245540618896484 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 4942 TO DO : save crop sub photo not yet done ! save time : 0.3737664222717285 nb_obj : 11 nb_hashtags : 3 time to prepare the origin masks : 5.764930725097656 time for calcul the mask position with numpy : 0.7352099418640137 nb_pixel_total : 7895207 time to create 1 rle with new method : 0.8725419044494629 time for calcul the mask position with numpy : 0.022794246673583984 nb_pixel_total : 8993 time to create 1 rle with old method : 0.009552717208862305 time for calcul the mask position with numpy : 0.02242136001586914 nb_pixel_total : 19339 time to create 1 rle with old method : 0.021230459213256836 time for calcul the mask position with numpy : 0.02459859848022461 nb_pixel_total : 42560 time to create 1 rle with old method : 0.04845690727233887 time for calcul the mask position with numpy : 0.033365726470947266 nb_pixel_total : 76026 time to create 1 rle with old method : 0.08402848243713379 time for calcul the mask position with numpy : 0.023998022079467773 nb_pixel_total : 11687 time to create 1 rle with old method : 0.012671709060668945 time for calcul the mask position with numpy : 0.023730039596557617 nb_pixel_total : 105526 time to create 1 rle with old method : 0.11962175369262695 time for calcul the mask position with numpy : 0.026745319366455078 nb_pixel_total : 36182 time to create 1 rle with old method : 0.045455217361450195 time for calcul the mask position with numpy : 0.032628536224365234 nb_pixel_total : 35050 time to create 1 rle with old method : 0.03925633430480957 time for calcul the mask position with numpy : 0.02468395233154297 nb_pixel_total : 10285 time to create 1 rle with old method : 0.01174616813659668 time for calcul the mask position with numpy : 0.024487972259521484 nb_pixel_total : 22427 time to create 1 rle with old method : 0.025217771530151367 time for calcul the mask position with numpy : 0.026890277862548828 nb_pixel_total : 31118 time to create 1 rle with old method : 0.034609317779541016 create new chi : 2.387267589569092 time to delete rle : 0.0011339187622070312 batch 1 Loaded 23 chid ids of type : 3594 ++++++++++++++++Number RLEs to save : 7387 TO DO : save crop sub photo not yet done ! save time : 0.5129969120025635 nb_obj : 5 nb_hashtags : 4 time to prepare the origin masks : 3.666128396987915 time for calcul the mask position with numpy : 0.6196632385253906 nb_pixel_total : 8051463 time to create 1 rle with new method : 0.9873371124267578 time for calcul the mask position with numpy : 0.022868871688842773 nb_pixel_total : 11637 time to create 1 rle with old method : 0.012779712677001953 time for calcul the mask position with numpy : 0.022744417190551758 nb_pixel_total : 50786 time to create 1 rle with old method : 0.05517578125 time for calcul the mask position with numpy : 0.023622512817382812 nb_pixel_total : 20750 time to create 1 rle with old method : 0.022835969924926758 time for calcul the mask position with numpy : 0.02386641502380371 nb_pixel_total : 25487 time to create 1 rle with old method : 0.027956247329711914 time for calcul the mask position with numpy : 0.023505449295043945 nb_pixel_total : 134277 time to create 1 rle with old method : 0.14597606658935547 create new chi : 2.028162956237793 time to delete rle : 0.0006206035614013672 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 4518 TO DO : save crop sub photo not yet done ! save time : 0.3438124656677246 nb_obj : 8 nb_hashtags : 4 time to prepare the origin masks : 5.241391181945801 time for calcul the mask position with numpy : 0.8528134822845459 nb_pixel_total : 7813675 time to create 1 rle with new method : 0.9164540767669678 time for calcul the mask position with numpy : 0.04562234878540039 nb_pixel_total : 11418 time to create 1 rle with old method : 0.013221025466918945 time for calcul the mask position with numpy : 0.04042649269104004 nb_pixel_total : 25610 time to create 1 rle with old method : 0.028673171997070312 time for calcul the mask position with numpy : 0.03914785385131836 nb_pixel_total : 13015 time to create 1 rle with old method : 0.014854907989501953 time for calcul the mask position with numpy : 0.040993452072143555 nb_pixel_total : 48103 time to create 1 rle with old method : 0.05509328842163086 time for calcul the mask position with numpy : 0.03899431228637695 nb_pixel_total : 132765 time to create 1 rle with old method : 0.14983129501342773 time for calcul the mask position with numpy : 0.026061058044433594 nb_pixel_total : 20382 time to create 1 rle with old method : 0.022645950317382812 time for calcul the mask position with numpy : 0.025985002517700195 nb_pixel_total : 171315 time to create 1 rle with new method : 0.8321585655212402 time for calcul the mask position with numpy : 0.02630901336669922 nb_pixel_total : 58117 time to create 1 rle with old method : 0.06925463676452637 create new chi : 3.317185401916504 time to delete rle : 0.0012524127960205078 batch 1 Loaded 17 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 6894 TO DO : save crop sub photo not yet done ! save time : 0.47054195404052734 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 2.8042492866516113 time for calcul the mask position with numpy : 0.8263392448425293 nb_pixel_total : 7812582 time to create 1 rle with new method : 1.338672399520874 time for calcul the mask position with numpy : 0.030103206634521484 nb_pixel_total : 40354 time to create 1 rle with old method : 0.04323577880859375 time for calcul the mask position with numpy : 0.022461414337158203 nb_pixel_total : 15604 time to create 1 rle with old method : 0.01640629768371582 time for calcul the mask position with numpy : 0.023537635803222656 nb_pixel_total : 196270 time to create 1 rle with new method : 1.262254238128662 time for calcul the mask position with numpy : 0.023815155029296875 nb_pixel_total : 113242 time to create 1 rle with old method : 0.1417386531829834 time for calcul the mask position with numpy : 0.028710603713989258 nb_pixel_total : 116348 time to create 1 rle with old method : 0.12504982948303223 create new chi : 3.9447054862976074 time to delete rle : 0.001390218734741211 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 6146 TO DO : save crop sub photo not yet done ! save time : 0.430464506149292 map_output_result : {1403193575: (0.0, 'Should be the crop_list due to order', 0), 1403193573: (0.0, 'Should be the crop_list due to order', 0), 1403193571: (0.0, 'Should be the crop_list due to order', 0), 1403193570: (0.0, 'Should be the crop_list due to order', 0), 1403193569: (0.0, 'Should be the crop_list due to order', 0), 1403193568: (0.0, 'Should be the crop_list due to order', 0), 1403193567: (0.0, 'Should be the crop_list due to order', 0), 1403193566: (0.0, 'Should be the crop_list due to order', 0), 1403193562: (0.0, 'Should be the crop_list due to order', 0), 1403193560: (0.0, 'Should be the crop_list due to order', 0), 1403193559: (0.0, 'Should be the crop_list due to order', 0), 1403193558: (0.0, 'Should be the crop_list due to order', 0), 1403193556: (0.0, 'Should be the crop_list due to order', 0), 1403193555: (0.0, 'Should be the crop_list due to order', 0), 1403193472: (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 [1403193575, 1403193573, 1403193571, 1403193570, 1403193569, 1403193568, 1403193567, 1403193566, 1403193562, 1403193560, 1403193559, 1403193558, 1403193556, 1403193555, 1403193472] Looping around the photos to save general results len do output : 15 /1403193575.Didn't retrieve data . /1403193573.Didn't retrieve data . /1403193571.Didn't retrieve data . /1403193570.Didn't retrieve data . /1403193569.Didn't retrieve data . /1403193568.Didn't retrieve data . /1403193567.Didn't retrieve data . /1403193566.Didn't retrieve data . /1403193562.Didn't retrieve data . /1403193560.Didn't retrieve data . /1403193559.Didn't retrieve data . /1403193558.Didn't retrieve data . /1403193556.Didn't retrieve data . /1403193555.Didn't retrieve data . /1403193472.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, '4327901') ('3318', '30072108', '1403193575', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193573', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193571', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193570', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193569', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193568', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193567', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193566', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193562', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193560', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193559', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193558', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193556', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193555', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193472', None, None, None, None, None, '4327901') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 45 time used for this insertion : 0.016281843185424805 save_final save missing photos in datou_result : time spend for datou_step_exec : 109.53318691253662 time spend to save output : 0.02012157440185547 total time spend for step 3 : 109.55330848693848 step4:ventilate_hashtags_in_portfolio Fri Jan 9 21:14:38 2026 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 : 30072108 get user id for portfolio 30072108 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`=30072108 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','papier','autre','environnement','pet_fonce','carton','background','pehd','mal_croppe','flou','metal')) 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`=30072108 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','papier','autre','environnement','pet_fonce','carton','background','pehd','mal_croppe','flou','metal')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30072108 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','papier','autre','environnement','pet_fonce','carton','background','pehd','mal_croppe','flou','metal')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/30072229,30072230,30072231,30072232,30072233,30072234,30072235,30072236,30072237,30072238,30072239?tags=pet_clair,papier,autre,environnement,pet_fonce,carton,background,pehd,mal_croppe,flou,metal Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1403193575, 1403193573, 1403193571, 1403193570, 1403193569, 1403193568, 1403193567, 1403193566, 1403193562, 1403193560, 1403193559, 1403193558, 1403193556, 1403193555, 1403193472] Looping around the photos to save general results len do output : 1 /30072108. 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, '4327901') ('3318', '30072108', '1403193575', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193573', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193571', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193570', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193569', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193568', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193567', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193566', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193562', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193560', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193559', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193558', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193556', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193555', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193472', None, None, None, None, None, '4327901') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.017729997634887695 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.9961426258087158 time spend to save output : 0.01801156997680664 total time spend for step 4 : 2.0141541957855225 step5:final Fri Jan 9 21:14:40 2026 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 : {1403193575: ('0.04848364358281893',), 1403193573: ('0.04848364358281893',), 1403193571: ('0.04848364358281893',), 1403193570: ('0.04848364358281893',), 1403193569: ('0.04848364358281893',), 1403193568: ('0.04848364358281893',), 1403193567: ('0.04848364358281893',), 1403193566: ('0.04848364358281893',), 1403193562: ('0.04848364358281893',), 1403193560: ('0.04848364358281893',), 1403193559: ('0.04848364358281893',), 1403193558: ('0.04848364358281893',), 1403193556: ('0.04848364358281893',), 1403193555: ('0.04848364358281893',), 1403193472: ('0.04848364358281893',)} new output for save of step final : {1403193575: ('0.04848364358281893',), 1403193573: ('0.04848364358281893',), 1403193571: ('0.04848364358281893',), 1403193570: ('0.04848364358281893',), 1403193569: ('0.04848364358281893',), 1403193568: ('0.04848364358281893',), 1403193567: ('0.04848364358281893',), 1403193566: ('0.04848364358281893',), 1403193562: ('0.04848364358281893',), 1403193560: ('0.04848364358281893',), 1403193559: ('0.04848364358281893',), 1403193558: ('0.04848364358281893',), 1403193556: ('0.04848364358281893',), 1403193555: ('0.04848364358281893',), 1403193472: ('0.04848364358281893',)} [1403193575, 1403193573, 1403193571, 1403193570, 1403193569, 1403193568, 1403193567, 1403193566, 1403193562, 1403193560, 1403193559, 1403193558, 1403193556, 1403193555, 1403193472] Looping around the photos to save general results len do output : 15 /1403193575.Didn't retrieve data . /1403193573.Didn't retrieve data . /1403193571.Didn't retrieve data . /1403193570.Didn't retrieve data . /1403193569.Didn't retrieve data . /1403193568.Didn't retrieve data . /1403193567.Didn't retrieve data . /1403193566.Didn't retrieve data . /1403193562.Didn't retrieve data . /1403193560.Didn't retrieve data . /1403193559.Didn't retrieve data . /1403193558.Didn't retrieve data . /1403193556.Didn't retrieve data . /1403193555.Didn't retrieve data . /1403193472.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, '4327901') ('3318', '30072108', '1403193575', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193573', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193571', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193570', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193569', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193568', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193567', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193566', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193562', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193560', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193559', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193558', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193556', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193555', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193472', None, None, None, None, None, '4327901') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 45 time used for this insertion : 0.015914201736450195 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.15136480331420898 time spend to save output : 0.01658344268798828 total time spend for step 5 : 0.16794824600219727 step6:blur_detection Fri Jan 9 21:14:40 2026 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/1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311.jpg resize: (2160, 3840) 1403193575 -5.519458365191513 treat image : temp/1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4.jpg resize: (2160, 3840) 1403193573 -6.882773089656588 treat image : temp/1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926.jpg resize: (2160, 3840) 1403193571 -7.151040844820849 treat image : temp/1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895.jpg resize: (2160, 3840) 1403193570 -6.985487910370441 treat image : temp/1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0.jpg resize: (2160, 3840) 1403193569 -6.295035047429094 treat image : temp/1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4.jpg resize: (2160, 3840) 1403193568 -6.865533118728487 treat image : temp/1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5.jpg resize: (2160, 3840) 1403193567 -6.739599675723053 treat image : temp/1767989429_3252176_1403193566_2d0aa04c7a4e06d910f2416fdf51bb64.jpg resize: (2160, 3840) 1403193566 -6.936762237552429 treat image : temp/1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7.jpg resize: (2160, 3840) 1403193562 -6.936856030849502 treat image : temp/1767989429_3252176_1403193560_1a8f036e21f6a1c19bfc693116024321.jpg resize: (2160, 3840) 1403193560 -6.928943508105065 treat image : temp/1767989429_3252176_1403193559_168c2820cd32b1e6d0087da0ca14e4ed.jpg resize: (2160, 3840) 1403193559 -7.082935071498428 treat image : temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3.jpg resize: (2160, 3840) 1403193558 -6.971651008349719 treat image : temp/1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb.jpg resize: (2160, 3840) 1403193556 -6.360222022073126 treat image : temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251.jpg resize: (2160, 3840) 1403193555 -4.613950533185602 treat image : temp/1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3.jpg resize: (2160, 3840) 1403193472 -6.90281792538619 treat image : temp/1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311_rle_crop_4095539979_0.png resize: (273, 156) 1403196291 -5.095060230470918 treat image : temp/1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311_rle_crop_4095539980_0.png resize: (267, 309) 1403196292 -3.768055197213485 treat image : temp/1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311_rle_crop_4095539982_0.png resize: (518, 158) 1403196293 -4.097126042150769 treat image : temp/1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539986_0.png resize: (127, 178) 1403196294 -4.059434477451848 treat image : temp/1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539987_0.png resize: (443, 198) 1403196295 -4.077093278123204 treat image : temp/1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539988_0.png resize: (301, 350) 1403196296 -4.499190991559969 treat image : temp/1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539990_0.png resize: (327, 177) 1403196297 -4.459137112153757 treat image : temp/1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539992_0.png resize: (328, 188) 1403196298 -4.034210022157474 treat image : temp/1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539993_0.png resize: (254, 71) 1403196299 -3.9996854512381677 treat image : temp/1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539994_0.png resize: (372, 392) 1403196300 -4.557560143373577 treat image : temp/1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895_rle_crop_4095540002_0.png resize: (183, 154) 1403196301 -4.3367301710608865 treat image : temp/1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0_rle_crop_4095540004_0.png resize: (431, 428) 1403196302 -4.666964096628796 treat image : temp/1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0_rle_crop_4095540005_0.png resize: (149, 172) 1403196303 -3.629671348777214 treat image : temp/1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0_rle_crop_4095540006_0.png resize: (367, 174) 1403196304 -4.2155859991740785 treat image : temp/1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540011_0.png resize: (237, 112) 1403196305 -3.4765724493975148 treat image : temp/1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5_rle_crop_4095540016_0.png resize: (92, 103) 1403196306 -3.670260528862541 treat image : temp/1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5_rle_crop_4095540017_0.png resize: (171, 153) 1403196307 -3.4824752982213965 treat image : temp/1767989429_3252176_1403193566_2d0aa04c7a4e06d910f2416fdf51bb64_rle_crop_4095540021_0.png resize: (202, 262) 1403196308 -4.35017524816528 treat image : temp/1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540026_0.png resize: (355, 279) 1403196309 -4.5810384709089105 treat image : temp/1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540027_0.png resize: (432, 694) 1403196310 -4.188109124339071 treat image : temp/1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540028_0.png resize: (254, 227) 1403196311 -3.962351240712892 treat image : temp/1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540030_0.png resize: (132, 211) 1403196312 -4.202586362030384 treat image : temp/1767989429_3252176_1403193560_1a8f036e21f6a1c19bfc693116024321_rle_crop_4095540033_0.png resize: (288, 194) 1403196313 -4.886523414515071 treat image : temp/1767989429_3252176_1403193559_168c2820cd32b1e6d0087da0ca14e4ed_rle_crop_4095540037_0.png resize: (229, 260) 1403196314 -3.986927905592045 treat image : temp/1767989429_3252176_1403193559_168c2820cd32b1e6d0087da0ca14e4ed_rle_crop_4095540038_0.png resize: (238, 228) 1403196315 -3.348694363579084 treat image : temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540042_0.png resize: (198, 173) 1403196316 -4.830852849443776 treat image : temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540043_0.png resize: (99, 137) 1403196317 -4.909623374549414 treat image : temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540047_0.png resize: (104, 175) 1403196318 -4.300836120181184 treat image : temp/1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb_rle_crop_4095540053_0.png resize: (173, 160) 1403196319 -4.490313850061361 treat image : temp/1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb_rle_crop_4095540054_0.png resize: (255, 145) 1403196320 -4.612442354757421 treat image : temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540059_0.png resize: (158, 191) 1403196321 -3.6354103031850333 treat image : temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540062_0.png resize: (119, 156) 1403196322 -3.353981131444435 treat image : temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540064_0.png resize: (115, 135) 1403196323 -4.447357877446751 treat image : temp/1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540066_0.png resize: (404, 419) 1403196324 -4.887919038325993 treat image : temp/1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540068_0.png resize: (132, 166) 1403196325 -4.596539044653293 treat image : temp/1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4_rle_crop_4095539984_0.png resize: (307, 109) 1403196326 -3.7387438502180186 treat image : temp/1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539989_0.png resize: (474, 626) 1403196327 -4.347802328761294 treat image : temp/1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926_rle_crop_4095539991_0.png resize: (148, 93) 1403196328 -4.000055549719993 treat image : temp/1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540008_0.png resize: (192, 172) 1403196329 -2.603657016540727 treat image : temp/1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540009_0.png resize: (116, 161) 1403196330 -4.492328804710837 treat image : temp/1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4_rle_crop_4095540013_0.png resize: (147, 110) 1403196331 -2.3216769532224633 treat image : 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temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540063_0.png resize: (150, 214) 1403196518 -3.6433750408875922 treat image : temp/1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540029_0.png resize: (191, 392) 1403196528 -4.806347416567084 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 : 106 time used for this insertion : 0.022841215133666992 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 106 time used for this insertion : 0.03672909736633301 save missing photos in datou_result : time spend for datou_step_exec : 57.99543213844299 time spend to save output : 0.0658571720123291 total time spend for step 6 : 58.06128931045532 step7:brightness Fri Jan 9 21:15:38 2026 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/1767989429_3252176_1403193575_86b93046ae17c37a7bbb026cccdd2311.jpg treat image : temp/1767989429_3252176_1403193573_82cdc057bc5c2c53a5c84c22ae3c8da4.jpg treat image : temp/1767989429_3252176_1403193571_dfc367600eb35c159fe58498889d9926.jpg treat image : temp/1767989429_3252176_1403193570_2afd52334e76da865962d963933e3895.jpg treat image : temp/1767989429_3252176_1403193569_f9532f300532b1e83a56334c0aa511e0.jpg treat image : temp/1767989429_3252176_1403193568_d31931622008049476d693e1ca3025c4.jpg treat image : temp/1767989429_3252176_1403193567_dc2a687c629cd7490a0ebf8ae3be0ea5.jpg treat image : temp/1767989429_3252176_1403193566_2d0aa04c7a4e06d910f2416fdf51bb64.jpg treat image : temp/1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7.jpg treat image : temp/1767989429_3252176_1403193560_1a8f036e21f6a1c19bfc693116024321.jpg treat image : 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temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540050_0.png treat image : temp/1767989429_3252176_1403193556_c7047c36544aadbb2ebc1234371664bb_rle_crop_4095540055_0.png treat image : temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540057_0.png treat image : temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540058_0.png treat image : temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540060_0.png treat image : temp/1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540065_0.png treat image : temp/1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540067_0.png treat image : temp/1767989429_3252176_1403193472_a3dd828aeffc2a505c1ddc3e82d196a3_rle_crop_4095540069_0.png treat image : temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540041_0.png treat image : temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540044_0.png treat image : temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540045_0.png treat image : temp/1767989429_3252176_1403193558_53fde33821737cf3fe132b54991910f3_rle_crop_4095540051_0.png treat image : temp/1767989429_3252176_1403193555_c4481a48e943c90ba60bea424820a251_rle_crop_4095540063_0.png treat image : temp/1767989429_3252176_1403193562_0079a83aa64b179e71267fa20b64b7a7_rle_crop_4095540029_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 : 106 time used for this insertion : 0.023059844970703125 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 106 time used for this insertion : 0.03871583938598633 save missing photos in datou_result : time spend for datou_step_exec : 13.553161144256592 time spend to save output : 0.06729936599731445 total time spend for step 7 : 13.620460510253906 step8:velours_tree Fri Jan 9 21:15:52 2026 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 : 0.15758442878723145 time spend to save output : 4.506111145019531e-05 total time spend for step 8 : 0.15762948989868164 step9:send_mail_cod Fri Jan 9 21:15:52 2026 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_P30072108_09-01-2026_21_15_52.pdf 30072229 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 .imagette300722291767989752 30072230 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 .imagette300722301767989754 30072231 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette300722311767989756 30072233 change filename to text .imagette300722331767989756 30072234 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 .imagette300722341767989757 30072235 imagette300722351767989758 30072236 imagette300722361767989758 30072237 imagette300722371767989758 30072238 imagette300722381767989758 30072239 change filename to text .change filename to text .change filename to text .change filename to text .imagette300722391767989758 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=30072108 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/30072229,30072230,30072231,30072232,30072233,30072234,30072235,30072236,30072237,30072238,30072239?tags=pet_clair,papier,autre,environnement,pet_fonce,carton,background,pehd,mal_croppe,flou,metal your option no_mail is active, we will not send the real mail to your client args[1403193575] : ((1403193575, -5.519458365191513, 492609224), (1403193575, -1.3192772684635914, 501862349), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193573] : ((1403193573, -6.882773089656588, 492609224), (1403193573, -0.24376058907327766, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193571] : ((1403193571, -7.151040844820849, 492609224), (1403193571, -0.2473371932992324, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193570] : ((1403193570, -6.985487910370441, 492609224), (1403193570, -0.15621798142663945, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193569] : ((1403193569, -6.295035047429094, 492609224), (1403193569, -0.4295262827151077, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193568] : ((1403193568, -6.865533118728487, 492609224), (1403193568, -0.04452686100259072, 2107752395), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193567] : ((1403193567, -6.739599675723053, 492609224), (1403193567, -0.3752489652529211, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193566] : ((1403193566, -6.936762237552429, 492609224), (1403193566, -0.2969758941630459, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193562] : ((1403193562, -6.936856030849502, 492609224), (1403193562, -0.2604554679342417, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193560] : ((1403193560, -6.928943508105065, 492609224), (1403193560, -0.36949582673428905, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193559] : ((1403193559, -7.082935071498428, 492609224), (1403193559, -0.16469478103046853, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193558] : ((1403193558, -6.971651008349719, 492609224), (1403193558, -0.2902910685389421, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193556] : ((1403193556, -6.360222022073126, 492609224), (1403193556, -0.5422732610176944, 501862349), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193555] : ((1403193555, -4.613950533185602, 492609224), (1403193555, -1.4267883645808206, 501862349), '0.04848364358281893') We are sending mail with results at report@fotonower.com args[1403193472] : ((1403193472, -6.90281792538619, 492609224), (1403193472, -0.18823019912889866, 496442774), '0.04848364358281893') We are sending mail with results at report@fotonower.com refus_total : 0.04848364358281893 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=30072108 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_P30072108_09-01-2026_21_15_52.pdf results_Auto_P30072108_09-01-2026_21_15_52.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30072108_09-01-2026_21_15_52.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','30072108','results_Auto_P30072108_09-01-2026_21_15_52.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30072108_09-01-2026_21_15_52.pdf','pdf','','1.02','0.04848364358281893') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1403193575, 1403193573, 1403193571, 1403193570, 1403193569, 1403193568, 1403193567, 1403193566, 1403193562, 1403193560, 1403193559, 1403193558, 1403193556, 1403193555, 1403193472] 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, '4327901') ('3318', '30072108', '1403193575', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193573', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193571', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193570', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193569', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193568', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193567', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193566', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193562', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193560', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193559', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193558', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193556', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193555', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193472', None, None, None, None, None, '4327901') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.018125057220458984 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.32228970527649 time spend to save output : 0.018380165100097656 total time spend for step 9 : 9.340669870376587 step10:split_time_score Fri Jan 9 21:16:01 2026 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'}] (('20', 15),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 09012026 30072108 Nombre de photos uploadées : 15 / 23040 (0%) 09012026 30072108 Nombre de photos taguées (types de déchets): 0 / 15 (0%) 09012026 30072108 Nombre de photos taguées (volume) : 0 / 15 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 4.5299530029296875e-06 ??????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0006728172302246094 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.24623966217041016 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 : 0.04309769020070876 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30064897_09-01-2026_07_12_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30064897 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30064897 AND mptpi.`type`=3726 To do Qualite : 0.04467766520703957 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30066437_09-01-2026_11_12_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30066437 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30066437 AND mptpi.`type`=3726 To do Qualite : 0.03704448516803841 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30066438_09-01-2026_11_14_00.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30066438 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`=30066438 AND mptpi.`type`=3594 To do Qualite : 0.02840877116406274 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30066439_09-01-2026_11_08_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30066439 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30066439 AND mptpi.`type`=3726 To do Qualite : 0.033368831107311925 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30070108_09-01-2026_17_13_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30070108 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30070108 AND mptpi.`type`=3726 To do Qualite : 0.0649756289958113 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30067991_09-01-2026_13_43_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30067991 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`=30067991 AND mptpi.`type`=3594 To do Qualite : 0.033056555532898976 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30068353_09-01-2026_14_09_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30068353 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30068353 AND mptpi.`type`=3726 To do Qualite : 0.046229963932897654 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30068934_09-01-2026_15_10_00.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30068934 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30068934 AND mptpi.`type`=3726 To do Qualite : 0.028911722082552882 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30070919_09-01-2026_18_39_27.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30070919 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30070919 AND mptpi.`type`=3726 To do Qualite : 0.012778442881310104 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30070921_09-01-2026_18_40_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30070921 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=30070921 AND mptpi.`type`=3726 To do Qualite : 0.04848364358281893 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P30072108_09-01-2026_21_15_52.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 30072108 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`=30072108 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'09012026': {'nb_upload': 15, '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 [1403193575, 1403193573, 1403193571, 1403193570, 1403193569, 1403193568, 1403193567, 1403193566, 1403193562, 1403193560, 1403193559, 1403193558, 1403193556, 1403193555, 1403193472] Looping around the photos to save general results len do output : 1 /30072108Didn'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, '4327901') ('3318', '30072108', '1403193575', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193573', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193571', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193570', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193569', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193568', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193567', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193566', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193562', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193560', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193559', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193558', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193556', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193555', None, None, None, None, None, '4327901') ('3318', None, None, None, None, None, None, None, '4327901') ('3318', '30072108', '1403193472', None, None, None, None, None, '4327901') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.01931309700012207 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.2633512020111084 time spend to save output : 0.019541025161743164 total time spend for step 10 : 1.2828922271728516 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 15 set_done_treatment 141.99user 161.49system 5:37.72elapsed 89%CPU (0avgtext+0avgdata 5118080maxresident)k 642568inputs+151624outputs (5488major+13609045minor)pagefaults 0swaps